modelId
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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-08-28 06:27:35
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 523
values | tags
listlengths 1
4.05k
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stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
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deepghs/anime_portrait
|
deepghs
| 2023-10-10T15:11:56Z | 0 | 1 | null |
[
"onnx",
"art",
"image-classification",
"dataset:deepghs/anime_portrait",
"license:openrail",
"region:us"
] |
image-classification
| 2023-10-09T08:08:37Z |
---
license: openrail
datasets:
- deepghs/anime_portrait
metrics:
- accuracy
- f1
pipeline_tag: image-classification
tags:
- art
---
| Name | FLOPS | Params | Accuracy | AUC | Confusion | Labels |
|:-------------------------:|:-------:|:--------:|:----------:|:------:|:-----------------------------------------------------------------------------------------------------------------:|:----------------------------:|
| caformer_s36_v0 | 22.10G | 37.22M | 98.10% | 0.9977 | [confusion](https://huggingface.co/deepghs/anime_portrait/blob/main/caformer_s36_v0/plot_confusion.png) | `person`, `halfbody`, `head` |
| mobilenetv3_small_v0_dist | 0.16G | 1.51M | 97.56% | 0.9969 | [confusion](https://huggingface.co/deepghs/anime_portrait/blob/main/mobilenetv3_small_v0_dist/plot_confusion.png) | `person`, `halfbody`, `head` |
| mobilenetv3_v0_dist | 0.63G | 4.18M | 97.98% | 0.9983 | [confusion](https://huggingface.co/deepghs/anime_portrait/blob/main/mobilenetv3_v0_dist/plot_confusion.png) | `person`, `halfbody`, `head` |
|
waldie/Mistral-Pygmalion-7b-8bpw-h8-exl2
|
waldie
| 2023-10-10T15:11:41Z | 11 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"Mistral",
"Pygmalion",
"llama-2",
"llama-2-7b",
"en",
"license:cc-by-nc-nd-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-10-10T14:50:02Z |
---
license: cc-by-nc-nd-4.0
language:
- en
library_name: transformers
pipeline_tag: text-generation
tags:
- Mistral
- Pygmalion
- llama-2
- llama-2-7b
---
quant of [Delcos'](https://huggingface.co/Delcos) [MistralPy-7b](https://huggingface.co/Delcos/Mistral-Pygmalion-7b)
```
python3 convert.py \
-i /input/Delcos_Mistral-Pygmalion-7b/ \
-c /input/wikitext/0000.parquet \
-o /output/temp/ \
-cf /output/8bpw/ \
-b 8.0 \
-hb 8
```
|
aiknight87/llama-2-7b-hf-tuned-200
|
aiknight87
| 2023-10-10T15:11:16Z | 1 | 0 |
peft
|
[
"peft",
"llama",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-2-7b-hf",
"base_model:adapter:meta-llama/Llama-2-7b-hf",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2023-10-10T14:15:47Z |
---
library_name: peft
base_model: meta-llama/Llama-2-7b-hf
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.6.0.dev0
|
alienverarslan/llama-2-7B-32K-instruct-7209-web-articles-fine-tuned
|
alienverarslan
| 2023-10-10T15:07:32Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"custom_code",
"en",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-09-22T17:06:49Z |
---
language:
- en
library_name: transformers
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
waldie/Mistral-Pygmalion-7b-4bpw-h6-exl2
|
waldie
| 2023-10-10T14:47:14Z | 10 | 1 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"Mistral",
"Pygmalion",
"llama-2",
"llama-2-7b",
"en",
"license:cc-by-nc-nd-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-10-10T14:35:50Z |
---
license: cc-by-nc-nd-4.0
language:
- en
library_name: transformers
pipeline_tag: text-generation
tags:
- Mistral
- Pygmalion
- llama-2
- llama-2-7b
---
quant of [Delcos'](https://huggingface.co/Delcos) [MistralPy-7b](https://huggingface.co/Delcos/Mistral-Pygmalion-7b)
```
python3 convert.py \
-i /input/Delcos_Mistral-Pygmalion-7b/ \
-c /input/wikitext/0000.parquet \
-o /output/temp/ \
-cf /output/4bpw/ \
-b 4.0 \
-hb 6
```
|
HuangLab/CELL-E_2_HPA_Finetuned_2560
|
HuangLab
| 2023-10-10T14:44:55Z | 0 | 2 |
pytorch
|
[
"pytorch",
"biology",
"microscopy",
"text-to-image",
"transformers",
"license:mit",
"region:us"
] |
text-to-image
| 2023-05-13T00:37:16Z |
---
license: mit
library_name: pytorch
tags:
- biology
- microscopy
- text-to-image
- transformers
metrics:
- accuracy
---
[](huanglab.ucsf.edu)
# CELL-E 2
## Model description
[](https://bohuanglab.github.io/CELL-E_2/)
CELL-E 2 is the second iteration of the original [CELL-E](https://www.biorxiv.org/content/10.1101/2022.05.27.493774v1) model which utilizes an amino acid sequence and nucleus image to make predictions of subcellular protein localization with respect to the nucleus.
CELL-E 2 is novel bidirectional transformer that can generate images depicting protein subcellular localization from the amino acid sequences (and *vice versa*).
CELL-E 2 not only captures the spatial complexity of protein localization and produce probability estimates of localization atop a nucleus image, but also being able to generate sequences from images, enabling *de novo* protein design.
We trained on the [Human Protein Atlas](https://www.proteinatlas.org) (HPA) and the [OpenCell](https://opencell.czbiohub.org) datasets.
CELL-E 2 utilizes pretrained amino acid embeddings from [ESM-2](https://github.com/facebookresearch/esm).
Localization is predicted as a binary image atop the provided nucleus. The logit values are weighted against these binary images to produce a heatmap of expected localization.
## Spaces
We have two spaces available where you can run predictions on your own data!
- [Image Prediction](https://huggingface.co/spaces/HuangLab/CELL-E_2-Image_Prediction)
- [Sequence Prediction](https://huggingface.co/spaces/HuangLab/CELL-E_2-Sequence_Prediction)
## Model variations
We have made several versions of CELL-E 2 available. The naming scheme follows the structure ```training set_hidden size``` where the hidden size is set to the embedding dimension of the pretrained ESM-2 model.
We annotate the most useful models under Notes, however other models can be used if memory constraints are present.
Since these models share similarities with BERT, the embeddings from any of these models may be benefical for downstream tasks.
**HPA Models**:
HPA models are trained on the HPA dataset. They are best for general purpose predictions as they include a variety of cell types.
| Model | Size | Notes
|------------------------|--------------------------------|-------|
| [`HPA_480`](https://huggingface.co/HuangLab/CELL-E_2_HPA_480) | 4.73 GB | **Best for Image Prediction** |
| [`HPA_640`](https://huggingface.co/HuangLab/CELL-E_2_HPA_640) | 6.31 GB | |
| [`HPA_1280`](https://huggingface.co/HuangLab/CELL-E_2_HPA_1280) | 10.8 GB | |
| [`HPA_2560`](https://huggingface.co/HuangLab/CELL-E_2_HPA_2560) | 17.5 GB | **Best for Sequence Prediction** |
**OpenCell Models**:
OpenCell models are trained on the OpenCell dataset. These only contain HEK cells and should ideally only be used for predictions on HEK cells. They perform well on image prediction but the generate heatmaps contain little information.
| Model | Size | Notes
|------------------------|--------------------------------|-------|
| [`OpenCell_480`](https://huggingface.co/HuangLab/CELL-E_2_OpenCell_480) | 4.73 GB | |
| [`OpenCell_640`](https://huggingface.co/HuangLab/CELL-E_2_OpenCell_640) | 6.31 GB | |
| [`OpenCell_1280`](https://huggingface.co/HuangLab/CELL-E_2_OpenCel_1280) | 10.8 GB | |
| [`OpenCell_2560`](https://huggingface.co/HuangLab/CELL-E_2_OpenCell_2560) | 17.5 GB | **Best for Sequence Prediction** |
**Finetuned HPA Models**:
These models were used the HPA models as checkpoints, but then were finetuned on the OpenCell dataset. We found that they improve image generation capabilities, but did not necessary see an improvement in sequence prediction.
| Model | Size | Notes
|------------------------|--------------------------------|-------|
| [`HPA_480`](https://huggingface.co/HuangLab/CELL-E_2_HPA_Finetuned_480) | 4.73 GB | **Best for Image Prediction** |
| [`HPA_640`](https://huggingface.co/HuangLab/CELL-E_2_HPA_Finetuned_640) | 6.31 GB | |
| [`HPA_1280`](https://huggingface.co/HuangLab/CELL-E_2_HPA_Finetuned_1280) | 10.8 GB | |
| [`HPA_2560`](https://huggingface.co/HuangLab/CELL-E_2_HPA_Finetuned_2560) | 17.5 GB | |
To reduce download size, we removed the ESM-2 model from the checkpoint. This should be downloaded the first time the code is run, but is otherwise something to be aware of if loading into other projects.
### How to use
The full codebase is available on [GitHub](https://github.com/BoHuangLab/CELL-E_2).
Download the model and make sure ```nuclues_vqgan.yaml```, ```threshold_vqgan.yaml```, ```config.yaml```, and ```model.ckpt``` are present.
```
Here is how to use this model to do sequence prediction:
```python
configs = OmegaConf.load(configs/config.yaml);
model = instantiate_from_config(configs.model).to(device);
model.sample(text=sequence, condition=nucleus)
```
### BibTeX entry and citation info
```bibtex
@inproceedings{
anonymous2023translating,
title={CELL-E 2: Translating Proteins to Pictures and Back with a Bidirectional Text-to-Image Transformer},
author={Emaad Khwaja, Yun S. Song, Aaron Agarunov, and Bo Huang},
booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
year={2023},
url={https://openreview.net/forum?id=YSMLVffl5u}
}
```
### Contact
We are an interdisciplinary lab based at [UCSF](https://www.ucsf.edu). We are particularly seeking talents in optical engineering, machine learning, and cellular microscopy. [Please reach out to Bo if you're interested in collaborating!](http://huanglab.ucsf.edu/Contact.html)
|
HuangLab/CELL-E_2_HPA_2560
|
HuangLab
| 2023-10-10T14:44:50Z | 6 | 0 |
pytorch
|
[
"pytorch",
"biology",
"microscopy",
"text-to-image",
"transformers",
"license:mit",
"region:us"
] |
text-to-image
| 2023-05-13T00:13:44Z |
---
license: mit
library_name: pytorch
tags:
- biology
- microscopy
- text-to-image
- transformers
metrics:
- accuracy
---
[](huanglab.ucsf.edu)
# CELL-E 2
## Model description
[](https://bohuanglab.github.io/CELL-E_2/)
CELL-E 2 is the second iteration of the original [CELL-E](https://www.biorxiv.org/content/10.1101/2022.05.27.493774v1) model which utilizes an amino acid sequence and nucleus image to make predictions of subcellular protein localization with respect to the nucleus.
CELL-E 2 is novel bidirectional transformer that can generate images depicting protein subcellular localization from the amino acid sequences (and *vice versa*).
CELL-E 2 not only captures the spatial complexity of protein localization and produce probability estimates of localization atop a nucleus image, but also being able to generate sequences from images, enabling *de novo* protein design.
We trained on the [Human Protein Atlas](https://www.proteinatlas.org) (HPA) and the [OpenCell](https://opencell.czbiohub.org) datasets.
CELL-E 2 utilizes pretrained amino acid embeddings from [ESM-2](https://github.com/facebookresearch/esm).
Localization is predicted as a binary image atop the provided nucleus. The logit values are weighted against these binary images to produce a heatmap of expected localization.
## Spaces
We have two spaces available where you can run predictions on your own data!
- [Image Prediction](https://huggingface.co/spaces/HuangLab/CELL-E_2-Image_Prediction)
- [Sequence Prediction](https://huggingface.co/spaces/HuangLab/CELL-E_2-Sequence_Prediction)
## Model variations
We have made several versions of CELL-E 2 available. The naming scheme follows the structure ```training set_hidden size``` where the hidden size is set to the embedding dimension of the pretrained ESM-2 model.
We annotate the most useful models under Notes, however other models can be used if memory constraints are present.
Since these models share similarities with BERT, the embeddings from any of these models may be benefical for downstream tasks.
**HPA Models**:
HPA models are trained on the HPA dataset. They are best for general purpose predictions as they include a variety of cell types.
| Model | Size | Notes
|------------------------|--------------------------------|-------|
| [`HPA_480`](https://huggingface.co/HuangLab/CELL-E_2_HPA_480) | 4.73 GB | **Best for Image Prediction** |
| [`HPA_640`](https://huggingface.co/HuangLab/CELL-E_2_HPA_640) | 6.31 GB | |
| [`HPA_1280`](https://huggingface.co/HuangLab/CELL-E_2_HPA_1280) | 10.8 GB | |
| [`HPA_2560`](https://huggingface.co/HuangLab/CELL-E_2_HPA_2560) | 17.5 GB | **Best for Sequence Prediction** |
**OpenCell Models**:
OpenCell models are trained on the OpenCell dataset. These only contain HEK cells and should ideally only be used for predictions on HEK cells. They perform well on image prediction but the generate heatmaps contain little information.
| Model | Size | Notes
|------------------------|--------------------------------|-------|
| [`OpenCell_480`](https://huggingface.co/HuangLab/CELL-E_2_OpenCell_480) | 4.73 GB | |
| [`OpenCell_640`](https://huggingface.co/HuangLab/CELL-E_2_OpenCell_640) | 6.31 GB | |
| [`OpenCell_1280`](https://huggingface.co/HuangLab/CELL-E_2_OpenCel_1280) | 10.8 GB | |
| [`OpenCell_2560`](https://huggingface.co/HuangLab/CELL-E_2_OpenCell_2560) | 17.5 GB | **Best for Sequence Prediction** |
**Finetuned HPA Models**:
These models were used the HPA models as checkpoints, but then were finetuned on the OpenCell dataset. We found that they improve image generation capabilities, but did not necessary see an improvement in sequence prediction.
| Model | Size | Notes
|------------------------|--------------------------------|-------|
| [`HPA_480`](https://huggingface.co/HuangLab/CELL-E_2_HPA_Finetuned_480) | 4.73 GB | **Best for Image Prediction** |
| [`HPA_640`](https://huggingface.co/HuangLab/CELL-E_2_HPA_Finetuned_640) | 6.31 GB | |
| [`HPA_1280`](https://huggingface.co/HuangLab/CELL-E_2_HPA_Finetuned_1280) | 10.8 GB | |
| [`HPA_2560`](https://huggingface.co/HuangLab/CELL-E_2_HPA_Finetuned_2560) | 17.5 GB | |
To reduce download size, we removed the ESM-2 model from the checkpoint. This should be downloaded the first time the code is run, but is otherwise something to be aware of if loading into other projects.
### How to use
The full codebase is available on [GitHub](https://github.com/BoHuangLab/CELL-E_2).
Download the model and make sure ```nuclues_vqgan.yaml```, ```threshold_vqgan.yaml```, ```config.yaml```, and ```model.ckpt``` are present.
```
Here is how to use this model to do sequence prediction:
```python
configs = OmegaConf.load(configs/config.yaml);
model = instantiate_from_config(configs.model).to(device);
model.sample(text=sequence, condition=nucleus)
```
### BibTeX entry and citation info
```bibtex
@inproceedings{
anonymous2023translating,
title={CELL-E 2: Translating Proteins to Pictures and Back with a Bidirectional Text-to-Image Transformer},
author={Emaad Khwaja, Yun S. Song, Aaron Agarunov, and Bo Huang},
booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
year={2023},
url={https://openreview.net/forum?id=YSMLVffl5u}
}
```
### Contact
We are an interdisciplinary lab based at [UCSF](https://www.ucsf.edu). We are particularly seeking talents in optical engineering, machine learning, and cellular microscopy. [Please reach out to Bo if you're interested in collaborating!](http://huanglab.ucsf.edu/Contact.html)
|
HuangLab/CELL-E_2_HPA_Finetuned_640
|
HuangLab
| 2023-10-10T14:44:41Z | 3 | 0 |
pytorch
|
[
"pytorch",
"biology",
"microscopy",
"text-to-image",
"transformers",
"license:mit",
"region:us"
] |
text-to-image
| 2023-05-13T00:37:00Z |
---
license: mit
library_name: pytorch
tags:
- biology
- microscopy
- text-to-image
- transformers
metrics:
- accuracy
---
[](huanglab.ucsf.edu)
# CELL-E 2
## Model description
[](https://bohuanglab.github.io/CELL-E_2/)
CELL-E 2 is the second iteration of the original [CELL-E](https://www.biorxiv.org/content/10.1101/2022.05.27.493774v1) model which utilizes an amino acid sequence and nucleus image to make predictions of subcellular protein localization with respect to the nucleus.
CELL-E 2 is novel bidirectional transformer that can generate images depicting protein subcellular localization from the amino acid sequences (and *vice versa*).
CELL-E 2 not only captures the spatial complexity of protein localization and produce probability estimates of localization atop a nucleus image, but also being able to generate sequences from images, enabling *de novo* protein design.
We trained on the [Human Protein Atlas](https://www.proteinatlas.org) (HPA) and the [OpenCell](https://opencell.czbiohub.org) datasets.
CELL-E 2 utilizes pretrained amino acid embeddings from [ESM-2](https://github.com/facebookresearch/esm).
Localization is predicted as a binary image atop the provided nucleus. The logit values are weighted against these binary images to produce a heatmap of expected localization.
## Spaces
We have two spaces available where you can run predictions on your own data!
- [Image Prediction](https://huggingface.co/spaces/HuangLab/CELL-E_2-Image_Prediction)
- [Sequence Prediction](https://huggingface.co/spaces/HuangLab/CELL-E_2-Sequence_Prediction)
## Model variations
We have made several versions of CELL-E 2 available. The naming scheme follows the structure ```training set_hidden size``` where the hidden size is set to the embedding dimension of the pretrained ESM-2 model.
We annotate the most useful models under Notes, however other models can be used if memory constraints are present.
Since these models share similarities with BERT, the embeddings from any of these models may be benefical for downstream tasks.
**HPA Models**:
HPA models are trained on the HPA dataset. They are best for general purpose predictions as they include a variety of cell types.
| Model | Size | Notes
|------------------------|--------------------------------|-------|
| [`HPA_480`](https://huggingface.co/HuangLab/CELL-E_2_HPA_480) | 4.73 GB | **Best for Image Prediction** |
| [`HPA_640`](https://huggingface.co/HuangLab/CELL-E_2_HPA_640) | 6.31 GB | |
| [`HPA_1280`](https://huggingface.co/HuangLab/CELL-E_2_HPA_1280) | 10.8 GB | |
| [`HPA_2560`](https://huggingface.co/HuangLab/CELL-E_2_HPA_2560) | 17.5 GB | **Best for Sequence Prediction** |
**OpenCell Models**:
OpenCell models are trained on the OpenCell dataset. These only contain HEK cells and should ideally only be used for predictions on HEK cells. They perform well on image prediction but the generate heatmaps contain little information.
| Model | Size | Notes
|------------------------|--------------------------------|-------|
| [`OpenCell_480`](https://huggingface.co/HuangLab/CELL-E_2_OpenCell_480) | 4.73 GB | |
| [`OpenCell_640`](https://huggingface.co/HuangLab/CELL-E_2_OpenCell_640) | 6.31 GB | |
| [`OpenCell_1280`](https://huggingface.co/HuangLab/CELL-E_2_OpenCel_1280) | 10.8 GB | |
| [`OpenCell_2560`](https://huggingface.co/HuangLab/CELL-E_2_OpenCell_2560) | 17.5 GB | **Best for Sequence Prediction** |
**Finetuned HPA Models**:
These models were used the HPA models as checkpoints, but then were finetuned on the OpenCell dataset. We found that they improve image generation capabilities, but did not necessary see an improvement in sequence prediction.
| Model | Size | Notes
|------------------------|--------------------------------|-------|
| [`HPA_480`](https://huggingface.co/HuangLab/CELL-E_2_HPA_Finetuned_480) | 4.73 GB | **Best for Image Prediction** |
| [`HPA_640`](https://huggingface.co/HuangLab/CELL-E_2_HPA_Finetuned_640) | 6.31 GB | |
| [`HPA_1280`](https://huggingface.co/HuangLab/CELL-E_2_HPA_Finetuned_1280) | 10.8 GB | |
| [`HPA_2560`](https://huggingface.co/HuangLab/CELL-E_2_HPA_Finetuned_2560) | 17.5 GB | |
To reduce download size, we removed the ESM-2 model from the checkpoint. This should be downloaded the first time the code is run, but is otherwise something to be aware of if loading into other projects.
### How to use
The full codebase is available on [GitHub](https://github.com/BoHuangLab/CELL-E_2).
Download the model and make sure ```nuclues_vqgan.yaml```, ```threshold_vqgan.yaml```, ```config.yaml```, and ```model.ckpt``` are present.
```
Here is how to use this model to do sequence prediction:
```python
configs = OmegaConf.load(configs/config.yaml);
model = instantiate_from_config(configs.model).to(device);
model.sample(text=sequence, condition=nucleus)
```
### BibTeX entry and citation info
```bibtex
@inproceedings{
anonymous2023translating,
title={CELL-E 2: Translating Proteins to Pictures and Back with a Bidirectional Text-to-Image Transformer},
author={Emaad Khwaja, Yun S. Song, Aaron Agarunov, and Bo Huang},
booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
year={2023},
url={https://openreview.net/forum?id=YSMLVffl5u}
}
```
### Contact
We are an interdisciplinary lab based at [UCSF](https://www.ucsf.edu). We are particularly seeking talents in optical engineering, machine learning, and cellular microscopy. [Please reach out to Bo if you're interested in collaborating!](http://huanglab.ucsf.edu/Contact.html)
|
HuangLab/CELL-E_2_HPA_640
|
HuangLab
| 2023-10-10T14:44:35Z | 1 | 0 |
pytorch
|
[
"pytorch",
"biology",
"microscopy",
"text-to-image",
"transformers",
"license:mit",
"region:us"
] |
text-to-image
| 2023-05-13T00:27:44Z |
---
license: mit
library_name: pytorch
tags:
- biology
- microscopy
- text-to-image
- transformers
metrics:
- accuracy
---
[](huanglab.ucsf.edu)
# CELL-E 2
## Model description
[](https://bohuanglab.github.io/CELL-E_2/)
CELL-E 2 is the second iteration of the original [CELL-E](https://www.biorxiv.org/content/10.1101/2022.05.27.493774v1) model which utilizes an amino acid sequence and nucleus image to make predictions of subcellular protein localization with respect to the nucleus.
CELL-E 2 is novel bidirectional transformer that can generate images depicting protein subcellular localization from the amino acid sequences (and *vice versa*).
CELL-E 2 not only captures the spatial complexity of protein localization and produce probability estimates of localization atop a nucleus image, but also being able to generate sequences from images, enabling *de novo* protein design.
We trained on the [Human Protein Atlas](https://www.proteinatlas.org) (HPA) and the [OpenCell](https://opencell.czbiohub.org) datasets.
CELL-E 2 utilizes pretrained amino acid embeddings from [ESM-2](https://github.com/facebookresearch/esm).
Localization is predicted as a binary image atop the provided nucleus. The logit values are weighted against these binary images to produce a heatmap of expected localization.
## Spaces
We have two spaces available where you can run predictions on your own data!
- [Image Prediction](https://huggingface.co/spaces/HuangLab/CELL-E_2-Image_Prediction)
- [Sequence Prediction](https://huggingface.co/spaces/HuangLab/CELL-E_2-Sequence_Prediction)
## Model variations
We have made several versions of CELL-E 2 available. The naming scheme follows the structure ```training set_hidden size``` where the hidden size is set to the embedding dimension of the pretrained ESM-2 model.
We annotate the most useful models under Notes, however other models can be used if memory constraints are present.
Since these models share similarities with BERT, the embeddings from any of these models may be benefical for downstream tasks.
**HPA Models**:
HPA models are trained on the HPA dataset. They are best for general purpose predictions as they include a variety of cell types.
| Model | Size | Notes
|------------------------|--------------------------------|-------|
| [`HPA_480`](https://huggingface.co/HuangLab/CELL-E_2_HPA_480) | 4.73 GB | **Best for Image Prediction** |
| [`HPA_640`](https://huggingface.co/HuangLab/CELL-E_2_HPA_640) | 6.31 GB | |
| [`HPA_1280`](https://huggingface.co/HuangLab/CELL-E_2_HPA_1280) | 10.8 GB | |
| [`HPA_2560`](https://huggingface.co/HuangLab/CELL-E_2_HPA_2560) | 17.5 GB | **Best for Sequence Prediction** |
**OpenCell Models**:
OpenCell models are trained on the OpenCell dataset. These only contain HEK cells and should ideally only be used for predictions on HEK cells. They perform well on image prediction but the generate heatmaps contain little information.
| Model | Size | Notes
|------------------------|--------------------------------|-------|
| [`OpenCell_480`](https://huggingface.co/HuangLab/CELL-E_2_OpenCell_480) | 4.73 GB | |
| [`OpenCell_640`](https://huggingface.co/HuangLab/CELL-E_2_OpenCell_640) | 6.31 GB | |
| [`OpenCell_1280`](https://huggingface.co/HuangLab/CELL-E_2_OpenCel_1280) | 10.8 GB | |
| [`OpenCell_2560`](https://huggingface.co/HuangLab/CELL-E_2_OpenCell_2560) | 17.5 GB | **Best for Sequence Prediction** |
**Finetuned HPA Models**:
These models were used the HPA models as checkpoints, but then were finetuned on the OpenCell dataset. We found that they improve image generation capabilities, but did not necessary see an improvement in sequence prediction.
| Model | Size | Notes
|------------------------|--------------------------------|-------|
| [`HPA_480`](https://huggingface.co/HuangLab/CELL-E_2_HPA_Finetuned_480) | 4.73 GB | **Best for Image Prediction** |
| [`HPA_640`](https://huggingface.co/HuangLab/CELL-E_2_HPA_Finetuned_640) | 6.31 GB | |
| [`HPA_1280`](https://huggingface.co/HuangLab/CELL-E_2_HPA_Finetuned_1280) | 10.8 GB | |
| [`HPA_2560`](https://huggingface.co/HuangLab/CELL-E_2_HPA_Finetuned_2560) | 17.5 GB | |
To reduce download size, we removed the ESM-2 model from the checkpoint. This should be downloaded the first time the code is run, but is otherwise something to be aware of if loading into other projects.
### How to use
The full codebase is available on [GitHub](https://github.com/BoHuangLab/CELL-E_2).
Download the model and make sure ```nuclues_vqgan.yaml```, ```threshold_vqgan.yaml```, ```config.yaml```, and ```model.ckpt``` are present.
```
Here is how to use this model to do sequence prediction:
```python
configs = OmegaConf.load(configs/config.yaml);
model = instantiate_from_config(configs.model).to(device);
model.sample(text=sequence, condition=nucleus)
```
### BibTeX entry and citation info
```bibtex
@inproceedings{
anonymous2023translating,
title={CELL-E 2: Translating Proteins to Pictures and Back with a Bidirectional Text-to-Image Transformer},
author={Emaad Khwaja, Yun S. Song, Aaron Agarunov, and Bo Huang},
booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
year={2023},
url={https://openreview.net/forum?id=YSMLVffl5u}
}
```
### Contact
We are an interdisciplinary lab based at [UCSF](https://www.ucsf.edu). We are particularly seeking talents in optical engineering, machine learning, and cellular microscopy. [Please reach out to Bo if you're interested in collaborating!](http://huanglab.ucsf.edu/Contact.html)
|
HuangLab/CELL-E_2_OpenCell_480
|
HuangLab
| 2023-10-10T14:44:31Z | 1 | 0 |
pytorch
|
[
"pytorch",
"biology",
"microscopy",
"text-to-image",
"transformers",
"license:mit",
"region:us"
] |
text-to-image
| 2023-05-13T00:36:22Z |
---
license: mit
library_name: pytorch
tags:
- biology
- microscopy
- text-to-image
- transformers
metrics:
- accuracy
---
[](huanglab.ucsf.edu)
# CELL-E 2
## Model description
[](https://bohuanglab.github.io/CELL-E_2/)
CELL-E 2 is the second iteration of the original [CELL-E](https://www.biorxiv.org/content/10.1101/2022.05.27.493774v1) model which utilizes an amino acid sequence and nucleus image to make predictions of subcellular protein localization with respect to the nucleus.
CELL-E 2 is novel bidirectional transformer that can generate images depicting protein subcellular localization from the amino acid sequences (and *vice versa*).
CELL-E 2 not only captures the spatial complexity of protein localization and produce probability estimates of localization atop a nucleus image, but also being able to generate sequences from images, enabling *de novo* protein design.
We trained on the [Human Protein Atlas](https://www.proteinatlas.org) (HPA) and the [OpenCell](https://opencell.czbiohub.org) datasets.
CELL-E 2 utilizes pretrained amino acid embeddings from [ESM-2](https://github.com/facebookresearch/esm).
Localization is predicted as a binary image atop the provided nucleus. The logit values are weighted against these binary images to produce a heatmap of expected localization.
## Spaces
We have two spaces available where you can run predictions on your own data!
- [Image Prediction](https://huggingface.co/spaces/HuangLab/CELL-E_2-Image_Prediction)
- [Sequence Prediction](https://huggingface.co/spaces/HuangLab/CELL-E_2-Sequence_Prediction)
## Model variations
We have made several versions of CELL-E 2 available. The naming scheme follows the structure ```training set_hidden size``` where the hidden size is set to the embedding dimension of the pretrained ESM-2 model.
We annotate the most useful models under Notes, however other models can be used if memory constraints are present.
Since these models share similarities with BERT, the embeddings from any of these models may be benefical for downstream tasks.
**HPA Models**:
HPA models are trained on the HPA dataset. They are best for general purpose predictions as they include a variety of cell types.
| Model | Size | Notes
|------------------------|--------------------------------|-------|
| [`HPA_480`](https://huggingface.co/HuangLab/CELL-E_2_HPA_480) | 4.73 GB | **Best for Image Prediction** |
| [`HPA_640`](https://huggingface.co/HuangLab/CELL-E_2_HPA_640) | 6.31 GB | |
| [`HPA_1280`](https://huggingface.co/HuangLab/CELL-E_2_HPA_1280) | 10.8 GB | |
| [`HPA_2560`](https://huggingface.co/HuangLab/CELL-E_2_HPA_2560) | 17.5 GB | **Best for Sequence Prediction** |
**OpenCell Models**:
OpenCell models are trained on the OpenCell dataset. These only contain HEK cells and should ideally only be used for predictions on HEK cells. They perform well on image prediction but the generate heatmaps contain little information.
| Model | Size | Notes
|------------------------|--------------------------------|-------|
| [`OpenCell_480`](https://huggingface.co/HuangLab/CELL-E_2_OpenCell_480) | 4.73 GB | |
| [`OpenCell_640`](https://huggingface.co/HuangLab/CELL-E_2_OpenCell_640) | 6.31 GB | |
| [`OpenCell_1280`](https://huggingface.co/HuangLab/CELL-E_2_OpenCel_1280) | 10.8 GB | |
| [`OpenCell_2560`](https://huggingface.co/HuangLab/CELL-E_2_OpenCell_2560) | 17.5 GB | **Best for Sequence Prediction** |
**Finetuned HPA Models**:
These models were used the HPA models as checkpoints, but then were finetuned on the OpenCell dataset. We found that they improve image generation capabilities, but did not necessary see an improvement in sequence prediction.
| Model | Size | Notes
|------------------------|--------------------------------|-------|
| [`HPA_480`](https://huggingface.co/HuangLab/CELL-E_2_HPA_Finetuned_480) | 4.73 GB | **Best for Image Prediction** |
| [`HPA_640`](https://huggingface.co/HuangLab/CELL-E_2_HPA_Finetuned_640) | 6.31 GB | |
| [`HPA_1280`](https://huggingface.co/HuangLab/CELL-E_2_HPA_Finetuned_1280) | 10.8 GB | |
| [`HPA_2560`](https://huggingface.co/HuangLab/CELL-E_2_HPA_Finetuned_2560) | 17.5 GB | |
To reduce download size, we removed the ESM-2 model from the checkpoint. This should be downloaded the first time the code is run, but is otherwise something to be aware of if loading into other projects.
### How to use
The full codebase is available on [GitHub](https://github.com/BoHuangLab/CELL-E_2).
Download the model and make sure ```nuclues_vqgan.yaml```, ```threshold_vqgan.yaml```, ```config.yaml```, and ```model.ckpt``` are present.
```
Here is how to use this model to do sequence prediction:
```python
configs = OmegaConf.load(configs/config.yaml);
model = instantiate_from_config(configs.model).to(device);
model.sample(text=sequence, condition=nucleus)
```
### BibTeX entry and citation info
```bibtex
@inproceedings{
anonymous2023translating,
title={CELL-E 2: Translating Proteins to Pictures and Back with a Bidirectional Text-to-Image Transformer},
author={Emaad Khwaja, Yun S. Song, Aaron Agarunov, and Bo Huang},
booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
year={2023},
url={https://openreview.net/forum?id=YSMLVffl5u}
}
```
### Contact
We are an interdisciplinary lab based at [UCSF](https://www.ucsf.edu). We are particularly seeking talents in optical engineering, machine learning, and cellular microscopy. [Please reach out to Bo if you're interested in collaborating!](http://huanglab.ucsf.edu/Contact.html)
|
dude121/ppo-LunarLander-v2
|
dude121
| 2023-10-10T14:44:29Z | 0 | 1 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-10-10T01:14:57Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 267.82 +/- 15.69
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
!apt install swig cmake
!pip install -r https://raw.githubusercontent.com/huggingface/deep-rl-class/main/notebooks/unit1/requirements-unit1.txt
!sudo apt-get update
!sudo apt-get install -y python3-opengl
!apt install ffmpeg
!apt install xvfb
!pip3 install pyvirtualdisplay
# Virtual display
from pyvirtualdisplay import Display
virtual_display = Display(visible=0, size=(1400, 900))
virtual_display.start()
import gymnasium
from huggingface_sb3 import load_from_hub, package_to_hub
from huggingface_hub import notebook_login # To log to our Hugging Face account to be able to upload models to the Hub.
from stable_baselines3 import PPO
from stable_baselines3.common.env_util import make_vec_env
from stable_baselines3.common.evaluation import evaluate_policy
from stable_baselines3.common.monitor import Monitor
import gymnasium as gym
# First, we create our environment called LunarLander-v2
env = gym.make("LunarLander-v2")
# Then we reset this environment
observation, info = env.reset()
for _ in range(20):
# Take a random action
action = env.action_space.sample()
print("Action taken:", action)
# Do this action in the environment and get
# next_state, reward, terminated, truncated and info
observation, reward, terminated, truncated, info = env.step(action)
# If the game is terminated (in our case we land, crashed) or truncated (timeout)
if terminated or truncated:
# Reset the environment
print("Environment is reset")
observation, info = env.reset()
env.close()
# We create our environment with gym.make("<name_of_the_environment>")
env = gym.make("LunarLander-v2")
env.reset()
print("_____OBSERVATION SPACE_____ \n")
print("Observation Space Shape", env.observation_space.shape)
print("Sample observation", env.observation_space.sample()) # Get a random observation
# Create the environment
env = make_vec_env('LunarLander-v2', n_envs=16)
# We added some parameters to accelerate the training
model = PPO(
policy = 'MlpPolicy',
env = env,
n_steps = 1024,
batch_size = 64,
n_epochs = 4,
gamma = 0.999,
gae_lambda = 0.98,
ent_coef = 0.01,
verbose=1)
# Train it for 1,000,000 timesteps
model.learn(total_timesteps=1000000)
# Save the model
model_name = "ppo-LunarLander-v2"
model.save(model_name)
# Get mean reward
eval_env = Monitor(gym.make("LunarLander-v2"))
mean_reward, std_reward = evaluate_policy(model, eval_env, n_eval_episodes=10, deterministic=True)
print(f"mean_reward={mean_reward:.2f} +/- {std_reward}")
notebook_login()
!git config --global credential.helper store
import gymnasium as gym
from stable_baselines3 import PPO
from stable_baselines3.common.vec_env import DummyVecEnv
from stable_baselines3.common.env_util import make_vec_env
from huggingface_sb3 import package_to_hub
# PLACE the variables you've just defined two cells above
# Define the name of the environment
env_id = "LunarLander-v2"
# TODO: Define the model architecture we used
model_architecture = "PPO"
## Define a repo_id
## repo_id is the id of the model repository from the Hugging Face Hub (repo_id = {organization}/{repo_name} for instance ThomasSimonini/ppo-LunarLander-v2
## CHANGE WITH YOUR REPO ID
repo_id = "dude121/ppo-LunarLander-v2" # Change with your repo id, you can't push with mine 😄
## Define the commit message
commit_message = "Upload PPO LunarLander-v2 trained agent"
# Create the evaluation env and set the render_mode="rgb_array"
eval_env = DummyVecEnv([lambda: gym.make(env_id, render_mode="rgb_array")])
# PLACE the package_to_hub function you've just filled here
package_to_hub(model=model, # Our trained model
model_name=model_name, # The name of our trained model
model_architecture=model_architecture, # The model architecture we used: in our case PPO
env_id=env_id, # Name of the environment
eval_env=eval_env, # Evaluation Environment
repo_id=repo_id, # id of the model repository from the Hugging Face Hub (repo_id = {organization}/{repo_name} for instance ThomasSimonini/ppo-LunarLander-v2
commit_message=commit_message)
...
...
```
|
HuangLab/CELL-E_2_OpenCell_640
|
HuangLab
| 2023-10-10T14:44:25Z | 3 | 0 |
pytorch
|
[
"pytorch",
"biology",
"microscopy",
"text-to-image",
"transformers",
"license:mit",
"region:us"
] |
text-to-image
| 2023-05-13T00:28:10Z |
---
license: mit
library_name: pytorch
tags:
- biology
- microscopy
- text-to-image
- transformers
metrics:
- accuracy
---
[](huanglab.ucsf.edu)
# CELL-E 2
## Model description
[](https://bohuanglab.github.io/CELL-E_2/)
CELL-E 2 is the second iteration of the original [CELL-E](https://www.biorxiv.org/content/10.1101/2022.05.27.493774v1) model which utilizes an amino acid sequence and nucleus image to make predictions of subcellular protein localization with respect to the nucleus.
CELL-E 2 is novel bidirectional transformer that can generate images depicting protein subcellular localization from the amino acid sequences (and *vice versa*).
CELL-E 2 not only captures the spatial complexity of protein localization and produce probability estimates of localization atop a nucleus image, but also being able to generate sequences from images, enabling *de novo* protein design.
We trained on the [Human Protein Atlas](https://www.proteinatlas.org) (HPA) and the [OpenCell](https://opencell.czbiohub.org) datasets.
CELL-E 2 utilizes pretrained amino acid embeddings from [ESM-2](https://github.com/facebookresearch/esm).
Localization is predicted as a binary image atop the provided nucleus. The logit values are weighted against these binary images to produce a heatmap of expected localization.
## Spaces
We have two spaces available where you can run predictions on your own data!
- [Image Prediction](https://huggingface.co/spaces/HuangLab/CELL-E_2-Image_Prediction)
- [Sequence Prediction](https://huggingface.co/spaces/HuangLab/CELL-E_2-Sequence_Prediction)
## Model variations
We have made several versions of CELL-E 2 available. The naming scheme follows the structure ```training set_hidden size``` where the hidden size is set to the embedding dimension of the pretrained ESM-2 model.
We annotate the most useful models under Notes, however other models can be used if memory constraints are present.
Since these models share similarities with BERT, the embeddings from any of these models may be benefical for downstream tasks.
**HPA Models**:
HPA models are trained on the HPA dataset. They are best for general purpose predictions as they include a variety of cell types.
| Model | Size | Notes
|------------------------|--------------------------------|-------|
| [`HPA_480`](https://huggingface.co/HuangLab/CELL-E_2_HPA_480) | 4.73 GB | **Best for Image Prediction** |
| [`HPA_640`](https://huggingface.co/HuangLab/CELL-E_2_HPA_640) | 6.31 GB | |
| [`HPA_1280`](https://huggingface.co/HuangLab/CELL-E_2_HPA_1280) | 10.8 GB | |
| [`HPA_2560`](https://huggingface.co/HuangLab/CELL-E_2_HPA_2560) | 17.5 GB | **Best for Sequence Prediction** |
**OpenCell Models**:
OpenCell models are trained on the OpenCell dataset. These only contain HEK cells and should ideally only be used for predictions on HEK cells. They perform well on image prediction but the generate heatmaps contain little information.
| Model | Size | Notes
|------------------------|--------------------------------|-------|
| [`OpenCell_480`](https://huggingface.co/HuangLab/CELL-E_2_OpenCell_480) | 4.73 GB | |
| [`OpenCell_640`](https://huggingface.co/HuangLab/CELL-E_2_OpenCell_640) | 6.31 GB | |
| [`OpenCell_1280`](https://huggingface.co/HuangLab/CELL-E_2_OpenCel_1280) | 10.8 GB | |
| [`OpenCell_2560`](https://huggingface.co/HuangLab/CELL-E_2_OpenCell_2560) | 17.5 GB | **Best for Sequence Prediction** |
**Finetuned HPA Models**:
These models were used the HPA models as checkpoints, but then were finetuned on the OpenCell dataset. We found that they improve image generation capabilities, but did not necessary see an improvement in sequence prediction.
| Model | Size | Notes
|------------------------|--------------------------------|-------|
| [`HPA_480`](https://huggingface.co/HuangLab/CELL-E_2_HPA_Finetuned_480) | 4.73 GB | **Best for Image Prediction** |
| [`HPA_640`](https://huggingface.co/HuangLab/CELL-E_2_HPA_Finetuned_640) | 6.31 GB | |
| [`HPA_1280`](https://huggingface.co/HuangLab/CELL-E_2_HPA_Finetuned_1280) | 10.8 GB | |
| [`HPA_2560`](https://huggingface.co/HuangLab/CELL-E_2_HPA_Finetuned_2560) | 17.5 GB | |
To reduce download size, we removed the ESM-2 model from the checkpoint. This should be downloaded the first time the code is run, but is otherwise something to be aware of if loading into other projects.
### How to use
The full codebase is available on [GitHub](https://github.com/BoHuangLab/CELL-E_2).
Download the model and make sure ```nuclues_vqgan.yaml```, ```threshold_vqgan.yaml```, ```config.yaml```, and ```model.ckpt``` are present.
```
Here is how to use this model to do sequence prediction:
```python
configs = OmegaConf.load(configs/config.yaml);
model = instantiate_from_config(configs.model).to(device);
model.sample(text=sequence, condition=nucleus)
```
### BibTeX entry and citation info
```bibtex
@inproceedings{
anonymous2023translating,
title={CELL-E 2: Translating Proteins to Pictures and Back with a Bidirectional Text-to-Image Transformer},
author={Emaad Khwaja, Yun S. Song, Aaron Agarunov, and Bo Huang},
booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
year={2023},
url={https://openreview.net/forum?id=YSMLVffl5u}
}
```
### Contact
We are an interdisciplinary lab based at [UCSF](https://www.ucsf.edu). We are particularly seeking talents in optical engineering, machine learning, and cellular microscopy. [Please reach out to Bo if you're interested in collaborating!](http://huanglab.ucsf.edu/Contact.html)
|
HuangLab/CELL-E_2_HPA_Finetuned_1280
|
HuangLab
| 2023-10-10T14:44:20Z | 3 | 0 |
pytorch
|
[
"pytorch",
"biology",
"microscopy",
"text-to-image",
"transformers",
"license:mit",
"region:us"
] |
text-to-image
| 2023-05-13T00:37:38Z |
---
license: mit
library_name: pytorch
tags:
- biology
- microscopy
- text-to-image
- transformers
metrics:
- accuracy
---
[](huanglab.ucsf.edu)
# CELL-E 2
## Model description
[](https://bohuanglab.github.io/CELL-E_2/)
CELL-E 2 is the second iteration of the original [CELL-E](https://www.biorxiv.org/content/10.1101/2022.05.27.493774v1) model which utilizes an amino acid sequence and nucleus image to make predictions of subcellular protein localization with respect to the nucleus.
CELL-E 2 is novel bidirectional transformer that can generate images depicting protein subcellular localization from the amino acid sequences (and *vice versa*).
CELL-E 2 not only captures the spatial complexity of protein localization and produce probability estimates of localization atop a nucleus image, but also being able to generate sequences from images, enabling *de novo* protein design.
We trained on the [Human Protein Atlas](https://www.proteinatlas.org) (HPA) and the [OpenCell](https://opencell.czbiohub.org) datasets.
CELL-E 2 utilizes pretrained amino acid embeddings from [ESM-2](https://github.com/facebookresearch/esm).
Localization is predicted as a binary image atop the provided nucleus. The logit values are weighted against these binary images to produce a heatmap of expected localization.
## Spaces
We have two spaces available where you can run predictions on your own data!
- [Image Prediction](https://huggingface.co/spaces/HuangLab/CELL-E_2-Image_Prediction)
- [Sequence Prediction](https://huggingface.co/spaces/HuangLab/CELL-E_2-Sequence_Prediction)
## Model variations
We have made several versions of CELL-E 2 available. The naming scheme follows the structure ```training set_hidden size``` where the hidden size is set to the embedding dimension of the pretrained ESM-2 model.
We annotate the most useful models under Notes, however other models can be used if memory constraints are present.
Since these models share similarities with BERT, the embeddings from any of these models may be benefical for downstream tasks.
**HPA Models**:
HPA models are trained on the HPA dataset. They are best for general purpose predictions as they include a variety of cell types.
| Model | Size | Notes
|------------------------|--------------------------------|-------|
| [`HPA_480`](https://huggingface.co/HuangLab/CELL-E_2_HPA_480) | 4.73 GB | **Best for Image Prediction** |
| [`HPA_640`](https://huggingface.co/HuangLab/CELL-E_2_HPA_640) | 6.31 GB | |
| [`HPA_1280`](https://huggingface.co/HuangLab/CELL-E_2_HPA_1280) | 10.8 GB | |
| [`HPA_2560`](https://huggingface.co/HuangLab/CELL-E_2_HPA_2560) | 17.5 GB | **Best for Sequence Prediction** |
**OpenCell Models**:
OpenCell models are trained on the OpenCell dataset. These only contain HEK cells and should ideally only be used for predictions on HEK cells. They perform well on image prediction but the generate heatmaps contain little information.
| Model | Size | Notes
|------------------------|--------------------------------|-------|
| [`OpenCell_480`](https://huggingface.co/HuangLab/CELL-E_2_OpenCell_480) | 4.73 GB | |
| [`OpenCell_640`](https://huggingface.co/HuangLab/CELL-E_2_OpenCell_640) | 6.31 GB | |
| [`OpenCell_1280`](https://huggingface.co/HuangLab/CELL-E_2_OpenCel_1280) | 10.8 GB | |
| [`OpenCell_2560`](https://huggingface.co/HuangLab/CELL-E_2_OpenCell_2560) | 17.5 GB | **Best for Sequence Prediction** |
**Finetuned HPA Models**:
These models were used the HPA models as checkpoints, but then were finetuned on the OpenCell dataset. We found that they improve image generation capabilities, but did not necessary see an improvement in sequence prediction.
| Model | Size | Notes
|------------------------|--------------------------------|-------|
| [`HPA_480`](https://huggingface.co/HuangLab/CELL-E_2_HPA_Finetuned_480) | 4.73 GB | **Best for Image Prediction** |
| [`HPA_640`](https://huggingface.co/HuangLab/CELL-E_2_HPA_Finetuned_640) | 6.31 GB | |
| [`HPA_1280`](https://huggingface.co/HuangLab/CELL-E_2_HPA_Finetuned_1280) | 10.8 GB | |
| [`HPA_2560`](https://huggingface.co/HuangLab/CELL-E_2_HPA_Finetuned_2560) | 17.5 GB | |
To reduce download size, we removed the ESM-2 model from the checkpoint. This should be downloaded the first time the code is run, but is otherwise something to be aware of if loading into other projects.
### How to use
The full codebase is available on [GitHub](https://github.com/BoHuangLab/CELL-E_2).
Download the model and make sure ```nuclues_vqgan.yaml```, ```threshold_vqgan.yaml```, ```config.yaml```, and ```model.ckpt``` are present.
```
Here is how to use this model to do sequence prediction:
```python
configs = OmegaConf.load(configs/config.yaml);
model = instantiate_from_config(configs.model).to(device);
model.sample(text=sequence, condition=nucleus)
```
### BibTeX entry and citation info
```bibtex
@inproceedings{
anonymous2023translating,
title={CELL-E 2: Translating Proteins to Pictures and Back with a Bidirectional Text-to-Image Transformer},
author={Emaad Khwaja, Yun S. Song, Aaron Agarunov, and Bo Huang},
booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
year={2023},
url={https://openreview.net/forum?id=YSMLVffl5u}
}
```
### Contact
We are an interdisciplinary lab based at [UCSF](https://www.ucsf.edu). We are particularly seeking talents in optical engineering, machine learning, and cellular microscopy. [Please reach out to Bo if you're interested in collaborating!](http://huanglab.ucsf.edu/Contact.html)
|
HuangLab/CELL-E_2_OpenCell_1280
|
HuangLab
| 2023-10-10T14:44:15Z | 2 | 0 |
pytorch
|
[
"pytorch",
"biology",
"microscopy",
"text-to-image",
"transformers",
"license:mit",
"region:us"
] |
text-to-image
| 2023-05-13T00:28:58Z |
---
license: mit
library_name: pytorch
tags:
- biology
- microscopy
- text-to-image
- transformers
metrics:
- accuracy
---
[](huanglab.ucsf.edu)
# CELL-E 2
## Model description
[](https://bohuanglab.github.io/CELL-E_2/)
CELL-E 2 is the second iteration of the original [CELL-E](https://www.biorxiv.org/content/10.1101/2022.05.27.493774v1) model which utilizes an amino acid sequence and nucleus image to make predictions of subcellular protein localization with respect to the nucleus.
CELL-E 2 is novel bidirectional transformer that can generate images depicting protein subcellular localization from the amino acid sequences (and *vice versa*).
CELL-E 2 not only captures the spatial complexity of protein localization and produce probability estimates of localization atop a nucleus image, but also being able to generate sequences from images, enabling *de novo* protein design.
We trained on the [Human Protein Atlas](https://www.proteinatlas.org) (HPA) and the [OpenCell](https://opencell.czbiohub.org) datasets.
CELL-E 2 utilizes pretrained amino acid embeddings from [ESM-2](https://github.com/facebookresearch/esm).
Localization is predicted as a binary image atop the provided nucleus. The logit values are weighted against these binary images to produce a heatmap of expected localization.
## Spaces
We have two spaces available where you can run predictions on your own data!
- [Image Prediction](https://huggingface.co/spaces/HuangLab/CELL-E_2-Image_Prediction)
- [Sequence Prediction](https://huggingface.co/spaces/HuangLab/CELL-E_2-Sequence_Prediction)
## Model variations
We have made several versions of CELL-E 2 available. The naming scheme follows the structure ```training set_hidden size``` where the hidden size is set to the embedding dimension of the pretrained ESM-2 model.
We annotate the most useful models under Notes, however other models can be used if memory constraints are present.
Since these models share similarities with BERT, the embeddings from any of these models may be benefical for downstream tasks.
**HPA Models**:
HPA models are trained on the HPA dataset. They are best for general purpose predictions as they include a variety of cell types.
| Model | Size | Notes
|------------------------|--------------------------------|-------|
| [`HPA_480`](https://huggingface.co/HuangLab/CELL-E_2_HPA_480) | 4.73 GB | **Best for Image Prediction** |
| [`HPA_640`](https://huggingface.co/HuangLab/CELL-E_2_HPA_640) | 6.31 GB | |
| [`HPA_1280`](https://huggingface.co/HuangLab/CELL-E_2_HPA_1280) | 10.8 GB | |
| [`HPA_2560`](https://huggingface.co/HuangLab/CELL-E_2_HPA_2560) | 17.5 GB | **Best for Sequence Prediction** |
**OpenCell Models**:
OpenCell models are trained on the OpenCell dataset. These only contain HEK cells and should ideally only be used for predictions on HEK cells. They perform well on image prediction but the generate heatmaps contain little information.
| Model | Size | Notes
|------------------------|--------------------------------|-------|
| [`OpenCell_480`](https://huggingface.co/HuangLab/CELL-E_2_OpenCell_480) | 4.73 GB | |
| [`OpenCell_640`](https://huggingface.co/HuangLab/CELL-E_2_OpenCell_640) | 6.31 GB | |
| [`OpenCell_1280`](https://huggingface.co/HuangLab/CELL-E_2_OpenCel_1280) | 10.8 GB | |
| [`OpenCell_2560`](https://huggingface.co/HuangLab/CELL-E_2_OpenCell_2560) | 17.5 GB | **Best for Sequence Prediction** |
**Finetuned HPA Models**:
These models were used the HPA models as checkpoints, but then were finetuned on the OpenCell dataset. We found that they improve image generation capabilities, but did not necessary see an improvement in sequence prediction.
| Model | Size | Notes
|------------------------|--------------------------------|-------|
| [`HPA_480`](https://huggingface.co/HuangLab/CELL-E_2_HPA_Finetuned_480) | 4.73 GB | **Best for Image Prediction** |
| [`HPA_640`](https://huggingface.co/HuangLab/CELL-E_2_HPA_Finetuned_640) | 6.31 GB | |
| [`HPA_1280`](https://huggingface.co/HuangLab/CELL-E_2_HPA_Finetuned_1280) | 10.8 GB | |
| [`HPA_2560`](https://huggingface.co/HuangLab/CELL-E_2_HPA_Finetuned_2560) | 17.5 GB | |
To reduce download size, we removed the ESM-2 model from the checkpoint. This should be downloaded the first time the code is run, but is otherwise something to be aware of if loading into other projects.
### How to use
The full codebase is available on [GitHub](https://github.com/BoHuangLab/CELL-E_2).
Download the model and make sure ```nuclues_vqgan.yaml```, ```threshold_vqgan.yaml```, ```config.yaml```, and ```model.ckpt``` are present.
```
Here is how to use this model to do sequence prediction:
```python
configs = OmegaConf.load(configs/config.yaml);
model = instantiate_from_config(configs.model).to(device);
model.sample(text=sequence, condition=nucleus)
```
### BibTeX entry and citation info
```bibtex
@inproceedings{
anonymous2023translating,
title={CELL-E 2: Translating Proteins to Pictures and Back with a Bidirectional Text-to-Image Transformer},
author={Emaad Khwaja, Yun S. Song, Aaron Agarunov, and Bo Huang},
booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
year={2023},
url={https://openreview.net/forum?id=YSMLVffl5u}
}
```
### Contact
We are an interdisciplinary lab based at [UCSF](https://www.ucsf.edu). We are particularly seeking talents in optical engineering, machine learning, and cellular microscopy. [Please reach out to Bo if you're interested in collaborating!](http://huanglab.ucsf.edu/Contact.html)
|
HuangLab/CELL-E_2_HPA_480
|
HuangLab
| 2023-10-10T14:44:09Z | 1 | 0 |
pytorch
|
[
"pytorch",
"biology",
"microscopy",
"text-to-image",
"transformers",
"license:mit",
"region:us"
] |
text-to-image
| 2023-05-12T23:56:45Z |
---
license: mit
library_name: pytorch
tags:
- biology
- microscopy
- text-to-image
- transformers
metrics:
- accuracy
---
[](huanglab.ucsf.edu)
# CELL-E 2
## Model description
[](https://bohuanglab.github.io/CELL-E_2/)
CELL-E 2 is the second iteration of the original [CELL-E](https://www.biorxiv.org/content/10.1101/2022.05.27.493774v1) model which utilizes an amino acid sequence and nucleus image to make predictions of subcellular protein localization with respect to the nucleus.
CELL-E 2 is novel bidirectional transformer that can generate images depicting protein subcellular localization from the amino acid sequences (and *vice versa*).
CELL-E 2 not only captures the spatial complexity of protein localization and produce probability estimates of localization atop a nucleus image, but also being able to generate sequences from images, enabling *de novo* protein design.
We trained on the [Human Protein Atlas](https://www.proteinatlas.org) (HPA) and the [OpenCell](https://opencell.czbiohub.org) datasets.
CELL-E 2 utilizes pretrained amino acid embeddings from [ESM-2](https://github.com/facebookresearch/esm).
Localization is predicted as a binary image atop the provided nucleus. The logit values are weighted against these binary images to produce a heatmap of expected localization.
## Spaces
We have two spaces available where you can run predictions on your own data!
- [Image Prediction](https://huggingface.co/spaces/HuangLab/CELL-E_2-Image_Prediction)
- [Sequence Prediction](https://huggingface.co/spaces/HuangLab/CELL-E_2-Sequence_Prediction)
## Model variations
We have made several versions of CELL-E 2 available. The naming scheme follows the structure ```training set_hidden size``` where the hidden size is set to the embedding dimension of the pretrained ESM-2 model.
We annotate the most useful models under Notes, however other models can be used if memory constraints are present.
Since these models share similarities with BERT, the embeddings from any of these models may be benefical for downstream tasks.
**HPA Models**:
HPA models are trained on the HPA dataset. They are best for general purpose predictions as they include a variety of cell types.
| Model | Size | Notes
|------------------------|--------------------------------|-------|
| [`HPA_480`](https://huggingface.co/HuangLab/CELL-E_2_HPA_480) | 4.73 GB | **Best for Image Prediction** |
| [`HPA_640`](https://huggingface.co/HuangLab/CELL-E_2_HPA_640) | 6.31 GB | |
| [`HPA_1280`](https://huggingface.co/HuangLab/CELL-E_2_HPA_1280) | 10.8 GB | |
| [`HPA_2560`](https://huggingface.co/HuangLab/CELL-E_2_HPA_2560) | 17.5 GB | **Best for Sequence Prediction** |
**OpenCell Models**:
OpenCell models are trained on the OpenCell dataset. These only contain HEK cells and should ideally only be used for predictions on HEK cells. They perform well on image prediction but the generate heatmaps contain little information.
| Model | Size | Notes
|------------------------|--------------------------------|-------|
| [`OpenCell_480`](https://huggingface.co/HuangLab/CELL-E_2_OpenCell_480) | 4.73 GB | |
| [`OpenCell_640`](https://huggingface.co/HuangLab/CELL-E_2_OpenCell_640) | 6.31 GB | |
| [`OpenCell_1280`](https://huggingface.co/HuangLab/CELL-E_2_OpenCel_1280) | 10.8 GB | |
| [`OpenCell_2560`](https://huggingface.co/HuangLab/CELL-E_2_OpenCell_2560) | 17.5 GB | **Best for Sequence Prediction** |
**Finetuned HPA Models**:
These models were used the HPA models as checkpoints, but then were finetuned on the OpenCell dataset. We found that they improve image generation capabilities, but did not necessary see an improvement in sequence prediction.
| Model | Size | Notes
|------------------------|--------------------------------|-------|
| [`HPA_480`](https://huggingface.co/HuangLab/CELL-E_2_HPA_Finetuned_480) | 4.73 GB | **Best for Image Prediction** |
| [`HPA_640`](https://huggingface.co/HuangLab/CELL-E_2_HPA_Finetuned_640) | 6.31 GB | |
| [`HPA_1280`](https://huggingface.co/HuangLab/CELL-E_2_HPA_Finetuned_1280) | 10.8 GB | |
| [`HPA_2560`](https://huggingface.co/HuangLab/CELL-E_2_HPA_Finetuned_2560) | 17.5 GB | |
To reduce download size, we removed the ESM-2 model from the checkpoint. This should be downloaded the first time the code is run, but is otherwise something to be aware of if loading into other projects.
### How to use
The full codebase is available on [GitHub](https://github.com/BoHuangLab/CELL-E_2).
Download the model and make sure ```nuclues_vqgan.yaml```, ```threshold_vqgan.yaml```, ```config.yaml```, and ```model.ckpt``` are present.
```
Here is how to use this model to do sequence prediction:
```python
configs = OmegaConf.load(configs/config.yaml);
model = instantiate_from_config(configs.model).to(device);
model.sample(text=sequence, condition=nucleus)
```
### BibTeX entry and citation info
```bibtex
@inproceedings{
anonymous2023translating,
title={CELL-E 2: Translating Proteins to Pictures and Back with a Bidirectional Text-to-Image Transformer},
author={Emaad Khwaja, Yun S. Song, Aaron Agarunov, and Bo Huang},
booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
year={2023},
url={https://openreview.net/forum?id=YSMLVffl5u}
}
```
### Contact
We are an interdisciplinary lab based at [UCSF](https://www.ucsf.edu). We are particularly seeking talents in optical engineering, machine learning, and cellular microscopy. [Please reach out to Bo if you're interested in collaborating!](http://huanglab.ucsf.edu/Contact.html)
|
HuangLab/CELL-E_2_OpenCell_2560
|
HuangLab
| 2023-10-10T14:44:03Z | 5 | 3 |
pytorch
|
[
"pytorch",
"biology",
"microscopy",
"text-to-image",
"transformers",
"license:mit",
"region:us"
] |
text-to-image
| 2023-05-12T23:43:18Z |
---
license: mit
library_name: pytorch
tags:
- biology
- microscopy
- text-to-image
- transformers
metrics:
- accuracy
---
[](huanglab.ucsf.edu)
# CELL-E 2
## Model description
[](https://bohuanglab.github.io/CELL-E_2/)
CELL-E 2 is the second iteration of the original [CELL-E](https://www.biorxiv.org/content/10.1101/2022.05.27.493774v1) model which utilizes an amino acid sequence and nucleus image to make predictions of subcellular protein localization with respect to the nucleus.
CELL-E 2 is novel bidirectional transformer that can generate images depicting protein subcellular localization from the amino acid sequences (and *vice versa*).
CELL-E 2 not only captures the spatial complexity of protein localization and produce probability estimates of localization atop a nucleus image, but also being able to generate sequences from images, enabling *de novo* protein design.
We trained on the [Human Protein Atlas](https://www.proteinatlas.org) (HPA) and the [OpenCell](https://opencell.czbiohub.org) datasets.
CELL-E 2 utilizes pretrained amino acid embeddings from [ESM-2](https://github.com/facebookresearch/esm).
Localization is predicted as a binary image atop the provided nucleus. The logit values are weighted against these binary images to produce a heatmap of expected localization.
## Spaces
We have two spaces available where you can run predictions on your own data!
- [Image Prediction](https://huggingface.co/spaces/HuangLab/CELL-E_2-Image_Prediction)
- [Sequence Prediction](https://huggingface.co/spaces/HuangLab/CELL-E_2-Sequence_Prediction)
## Model variations
We have made several versions of CELL-E 2 available. The naming scheme follows the structure ```training set_hidden size``` where the hidden size is set to the embedding dimension of the pretrained ESM-2 model.
We annotate the most useful models under Notes, however other models can be used if memory constraints are present.
Since these models share similarities with BERT, the embeddings from any of these models may be benefical for downstream tasks.
**HPA Models**:
HPA models are trained on the HPA dataset. They are best for general purpose predictions as they include a variety of cell types.
| Model | Size | Notes
|------------------------|--------------------------------|-------|
| [`HPA_480`](https://huggingface.co/HuangLab/CELL-E_2_HPA_480) | 4.73 GB | **Best for Image Prediction** |
| [`HPA_640`](https://huggingface.co/HuangLab/CELL-E_2_HPA_640) | 6.31 GB | |
| [`HPA_1280`](https://huggingface.co/HuangLab/CELL-E_2_HPA_1280) | 10.8 GB | |
| [`HPA_2560`](https://huggingface.co/HuangLab/CELL-E_2_HPA_2560) | 17.5 GB | **Best for Sequence Prediction** |
**OpenCell Models**:
OpenCell models are trained on the OpenCell dataset. These only contain HEK cells and should ideally only be used for predictions on HEK cells. They perform well on image prediction but the generate heatmaps contain little information.
| Model | Size | Notes
|------------------------|--------------------------------|-------|
| [`OpenCell_480`](https://huggingface.co/HuangLab/CELL-E_2_OpenCell_480) | 4.73 GB | |
| [`OpenCell_640`](https://huggingface.co/HuangLab/CELL-E_2_OpenCell_640) | 6.31 GB | |
| [`OpenCell_1280`](https://huggingface.co/HuangLab/CELL-E_2_OpenCel_1280) | 10.8 GB | |
| [`OpenCell_2560`](https://huggingface.co/HuangLab/CELL-E_2_OpenCell_2560) | 17.5 GB | **Best for Sequence Prediction** |
**Finetuned HPA Models**:
These models were used the HPA models as checkpoints, but then were finetuned on the OpenCell dataset. We found that they improve image generation capabilities, but did not necessary see an improvement in sequence prediction.
| Model | Size | Notes
|------------------------|--------------------------------|-------|
| [`HPA_480`](https://huggingface.co/HuangLab/CELL-E_2_HPA_Finetuned_480) | 4.73 GB | **Best for Image Prediction** |
| [`HPA_640`](https://huggingface.co/HuangLab/CELL-E_2_HPA_Finetuned_640) | 6.31 GB | |
| [`HPA_1280`](https://huggingface.co/HuangLab/CELL-E_2_HPA_Finetuned_1280) | 10.8 GB | |
| [`HPA_2560`](https://huggingface.co/HuangLab/CELL-E_2_HPA_Finetuned_2560) | 17.5 GB | |
To reduce download size, we removed the ESM-2 model from the checkpoint. This should be downloaded the first time the code is run, but is otherwise something to be aware of if loading into other projects.
### How to use
The full codebase is available on [GitHub](https://github.com/BoHuangLab/CELL-E_2).
Download the model and make sure ```nuclues_vqgan.yaml```, ```threshold_vqgan.yaml```, ```config.yaml```, and ```model.ckpt``` are present.
```
Here is how to use this model to do sequence prediction:
```python
configs = OmegaConf.load(configs/config.yaml);
model = instantiate_from_config(configs.model).to(device);
model.sample(text=sequence, condition=nucleus)
```
### BibTeX entry and citation info
```bibtex
@inproceedings{
anonymous2023translating,
title={CELL-E 2: Translating Proteins to Pictures and Back with a Bidirectional Text-to-Image Transformer},
author={Emaad Khwaja, Yun S. Song, Aaron Agarunov, and Bo Huang},
booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
year={2023},
url={https://openreview.net/forum?id=YSMLVffl5u}
}
```
### Contact
We are an interdisciplinary lab based at [UCSF](https://www.ucsf.edu). We are particularly seeking talents in optical engineering, machine learning, and cellular microscopy. [Please reach out to Bo if you're interested in collaborating!](http://huanglab.ucsf.edu/Contact.html)
|
HuangLab/CELL-E_2_HPA_Finetuned_480
|
HuangLab
| 2023-10-10T14:43:41Z | 8 | 2 |
pytorch
|
[
"pytorch",
"biology",
"microscopy",
"text-to-image",
"transformers",
"license:mit",
"region:us"
] |
text-to-image
| 2023-05-13T00:07:42Z |
---
license: mit
library_name: pytorch
tags:
- biology
- microscopy
- text-to-image
- transformers
metrics:
- accuracy
---
[](huanglab.ucsf.edu)
# CELL-E 2
## Model description
[](https://bohuanglab.github.io/CELL-E_2/)
CELL-E 2 is the second iteration of the original [CELL-E](https://www.biorxiv.org/content/10.1101/2022.05.27.493774v1) model which utilizes an amino acid sequence and nucleus image to make predictions of subcellular protein localization with respect to the nucleus.
CELL-E 2 is novel bidirectional transformer that can generate images depicting protein subcellular localization from the amino acid sequences (and *vice versa*).
CELL-E 2 not only captures the spatial complexity of protein localization and produce probability estimates of localization atop a nucleus image, but also being able to generate sequences from images, enabling *de novo* protein design.
We trained on the [Human Protein Atlas](https://www.proteinatlas.org) (HPA) and the [OpenCell](https://opencell.czbiohub.org) datasets.
CELL-E 2 utilizes pretrained amino acid embeddings from [ESM-2](https://github.com/facebookresearch/esm).
Localization is predicted as a binary image atop the provided nucleus. The logit values are weighted against these binary images to produce a heatmap of expected localization.
## Spaces
We have two spaces available where you can run predictions on your own data!
- [Image Prediction](https://huggingface.co/spaces/HuangLab/CELL-E_2-Image_Prediction)
- [Sequence Prediction](https://huggingface.co/spaces/HuangLab/CELL-E_2-Sequence_Prediction)
## Model variations
We have made several versions of CELL-E 2 available. The naming scheme follows the structure ```training set_hidden size``` where the hidden size is set to the embedding dimension of the pretrained ESM-2 model.
We annotate the most useful models under Notes, however other models can be used if memory constraints are present.
Since these models share similarities with BERT, the embeddings from any of these models may be benefical for downstream tasks.
**HPA Models**:
HPA models are trained on the HPA dataset. They are best for general purpose predictions as they include a variety of cell types.
| Model | Size | Notes
|------------------------|--------------------------------|-------|
| [`HPA_480`](https://huggingface.co/HuangLab/CELL-E_2_HPA_480) | 4.73 GB | **Best for Image Prediction** |
| [`HPA_640`](https://huggingface.co/HuangLab/CELL-E_2_HPA_640) | 6.31 GB | |
| [`HPA_1280`](https://huggingface.co/HuangLab/CELL-E_2_HPA_1280) | 10.8 GB | |
| [`HPA_2560`](https://huggingface.co/HuangLab/CELL-E_2_HPA_2560) | 17.5 GB | **Best for Sequence Prediction** |
**OpenCell Models**:
OpenCell models are trained on the OpenCell dataset. These only contain HEK cells and should ideally only be used for predictions on HEK cells. They perform well on image prediction but the generate heatmaps contain little information.
| Model | Size | Notes
|------------------------|--------------------------------|-------|
| [`OpenCell_480`](https://huggingface.co/HuangLab/CELL-E_2_OpenCell_480) | 4.73 GB | |
| [`OpenCell_640`](https://huggingface.co/HuangLab/CELL-E_2_OpenCell_640) | 6.31 GB | |
| [`OpenCell_1280`](https://huggingface.co/HuangLab/CELL-E_2_OpenCel_1280) | 10.8 GB | |
| [`OpenCell_2560`](https://huggingface.co/HuangLab/CELL-E_2_OpenCell_2560) | 17.5 GB | **Best for Sequence Prediction** |
**Finetuned HPA Models**:
These models were used the HPA models as checkpoints, but then were finetuned on the OpenCell dataset. We found that they improve image generation capabilities, but did not necessary see an improvement in sequence prediction.
| Model | Size | Notes
|------------------------|--------------------------------|-------|
| [`HPA_480`](https://huggingface.co/HuangLab/CELL-E_2_HPA_Finetuned_480) | 4.73 GB | **Best for Image Prediction** |
| [`HPA_640`](https://huggingface.co/HuangLab/CELL-E_2_HPA_Finetuned_640) | 6.31 GB | |
| [`HPA_1280`](https://huggingface.co/HuangLab/CELL-E_2_HPA_Finetuned_1280) | 10.8 GB | |
| [`HPA_2560`](https://huggingface.co/HuangLab/CELL-E_2_HPA_Finetuned_2560) | 17.5 GB | |
To reduce download size, we removed the ESM-2 model from the checkpoint. This should be downloaded the first time the code is run, but is otherwise something to be aware of if loading into other projects.
### How to use
The full codebase is available on [GitHub](https://github.com/BoHuangLab/CELL-E_2).
Download the model and make sure ```nuclues_vqgan.yaml```, ```threshold_vqgan.yaml```, ```config.yaml```, and ```model.ckpt``` are present.
```
Here is how to use this model to do sequence prediction:
```python
configs = OmegaConf.load(configs/config.yaml);
model = instantiate_from_config(configs.model).to(device);
model.sample(text=sequence, condition=nucleus)
```
### BibTeX entry and citation info
```bibtex
@inproceedings{
anonymous2023translating,
title={CELL-E 2: Translating Proteins to Pictures and Back with a Bidirectional Text-to-Image Transformer},
author={Emaad Khwaja, Yun S. Song, Aaron Agarunov, and Bo Huang},
booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
year={2023},
url={https://openreview.net/forum?id=YSMLVffl5u}
}
```
### Contact
We are an interdisciplinary lab based at [UCSF](https://www.ucsf.edu). We are particularly seeking talents in optical engineering, machine learning, and cellular microscopy. [Please reach out to Bo if you're interested in collaborating!](http://huanglab.ucsf.edu/Contact.html)
|
noamno1/distilbert-base-uncasedOffensive-Language-lora-text-classification
|
noamno1
| 2023-10-10T14:37:03Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-10-10T14:27:04Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0
|
wiilog/donut-base-dnud-v2
|
wiilog
| 2023-10-10T14:33:08Z | 45 | 0 |
transformers
|
[
"transformers",
"pytorch",
"vision-encoder-decoder",
"image-text-to-text",
"generated_from_trainer",
"base_model:naver-clova-ix/donut-base",
"base_model:finetune:naver-clova-ix/donut-base",
"license:mit",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2023-10-10T12:40:38Z |
---
license: mit
base_model: naver-clova-ix/donut-base
tags:
- generated_from_trainer
model-index:
- name: donut-base-dnud-v2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# donut-base-dnud-v2
This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.33.3
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.13.2
|
Vaibhav9401/flan-t5-base-samsum
|
Vaibhav9401
| 2023-10-10T14:24:36Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:samsum",
"base_model:google/flan-t5-base",
"base_model:finetune:google/flan-t5-base",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-10-10T12:14:11Z |
---
license: apache-2.0
base_model: google/flan-t5-base
tags:
- generated_from_trainer
datasets:
- samsum
metrics:
- rouge
model-index:
- name: flan-t5-base-samsum
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: samsum
type: samsum
config: samsum
split: test
args: samsum
metrics:
- name: Rouge1
type: rouge
value: 47.6412
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# flan-t5-base-samsum
This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the samsum dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3649
- Rouge1: 47.6412
- Rouge2: 24.051
- Rougel: 40.0954
- Rougelsum: 43.6636
- Gen Len: 17.1844
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.4548 | 1.0 | 1842 | 1.3789 | 46.7801 | 23.374 | 39.5739 | 43.1465 | 17.4164 |
| 1.3452 | 2.0 | 3684 | 1.3678 | 47.1262 | 23.3912 | 39.8206 | 43.4192 | 17.2601 |
| 1.2821 | 3.0 | 5526 | 1.3649 | 47.6412 | 24.051 | 40.0954 | 43.6636 | 17.1844 |
| 1.2347 | 4.0 | 7368 | 1.3712 | 47.5837 | 24.0545 | 40.2391 | 43.7923 | 17.2808 |
| 1.1983 | 5.0 | 9210 | 1.3732 | 47.33 | 23.983 | 39.957 | 43.6156 | 17.2808 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
|
GuCuChiara/NLP-CIC-WFU_DisTEMIST_fine_tuned_bert-base-multilingual-cased
|
GuCuChiara
| 2023-10-10T14:13:27Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-multilingual-cased",
"base_model:finetune:google-bert/bert-base-multilingual-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-09-24T23:57:52Z |
---
license: apache-2.0
base_model: bert-base-multilingual-cased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: NLP-CIC-WFU_DisTEMIST_fine_tuned_bert-base-multilingual-cased
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# NLP-CIC-WFU_DisTEMIST_fine_tuned_bert-base-multilingual-cased
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1620
- Precision: 0.6121
- Recall: 0.5161
- F1: 0.5600
- Accuracy: 0.9541
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 71 | 0.1704 | 0.4558 | 0.3635 | 0.4045 | 0.9353 |
| No log | 2.0 | 142 | 0.1572 | 0.5925 | 0.3518 | 0.4415 | 0.9433 |
| No log | 3.0 | 213 | 0.1386 | 0.5932 | 0.4774 | 0.5290 | 0.9531 |
| No log | 4.0 | 284 | 0.1427 | 0.5945 | 0.5175 | 0.5534 | 0.9533 |
| No log | 5.0 | 355 | 0.1653 | 0.6354 | 0.4788 | 0.5461 | 0.9540 |
| No log | 6.0 | 426 | 0.1620 | 0.6121 | 0.5161 | 0.5600 | 0.9541 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
|
hyyoka/multi-tapt-IA3-mbert
|
hyyoka
| 2023-10-10T14:09:47Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-10-10T14:09:46Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0
|
ercaronte/whisper-tiny
|
ercaronte
| 2023-10-10T14:05:28Z | 84 | 0 |
transformers
|
[
"transformers",
"pytorch",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:PolyAI/minds14",
"base_model:openai/whisper-tiny",
"base_model:finetune:openai/whisper-tiny",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-10-09T16:21:33Z |
---
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_trainer
datasets:
- PolyAI/minds14
metrics:
- wer
model-index:
- name: whisper-tiny
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: PolyAI/minds14
type: PolyAI/minds14
config: en-US
split: train[450:]
args: en-US
metrics:
- name: Wer
type: wer
value: 0.3482880755608028
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-tiny
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the PolyAI/minds14 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6761
- Wer Ortho: 0.3516
- Wer: 0.3483
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 50
- training_steps: 750
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|
| 0.2012 | 4.46 | 125 | 0.5011 | 0.3714 | 0.3542 |
| 0.0102 | 8.93 | 250 | 0.5741 | 0.3578 | 0.3459 |
| 0.0013 | 13.39 | 375 | 0.6115 | 0.3498 | 0.3418 |
| 0.0007 | 17.86 | 500 | 0.6403 | 0.3492 | 0.3447 |
| 0.0005 | 22.32 | 625 | 0.6610 | 0.3510 | 0.3465 |
| 0.0004 | 26.79 | 750 | 0.6761 | 0.3516 | 0.3483 |
### Framework versions
- Transformers 4.33.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
|
Faradaylab/ARIA-70B-V3
|
Faradaylab
| 2023-10-10T14:02:44Z | 1,528 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-09-11T09:34:47Z |
---
license: other
---
ARIA V3 has been trained over 100.000 high quality french language with a focus on data bias, grammar and overall language/writing capacities of the model.
The training has been done on Nvidia GPU in the cloud with Amazon Sagemaker.
Base Model : Llama2-70B-Chat-HF
Dataset : private dataset.
Added value : French Language / Writing / Content Creation / Data bias reduction
Feel free to reach out to us ! contact@faradaylab.fr
|
Suksuma/distilbert-base-multilingual-cased-finetuned-wanted
|
Suksuma
| 2023-10-10T14:02:19Z | 124 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-multilingual-cased",
"base_model:finetune:distilbert/distilbert-base-multilingual-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-10-10T13:55:12Z |
---
license: apache-2.0
base_model: distilbert-base-multilingual-cased
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-multilingual-cased-finetuned-wanted
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-multilingual-cased-finetuned-wanted
This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8072
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.2167 | 1.0 | 282 | 1.9319 |
| 2.0129 | 2.0 | 564 | 1.8027 |
| 1.9188 | 3.0 | 846 | 1.7965 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
|
anders0204/Reinforce-Pixelcopter-5k
|
anders0204
| 2023-10-10T13:52:37Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-27T13:23:12Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-5k
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 3.90 +/- 2.30
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
ajibawa-2023/Scarlett-Phi
|
ajibawa-2023
| 2023-10-10T13:52:19Z | 59 | 8 |
transformers
|
[
"transformers",
"pytorch",
"mixformer-sequential",
"text-generation",
"custom_code",
"en",
"license:cc-by-nc-nd-4.0",
"autotrain_compatible",
"region:us"
] |
text-generation
| 2023-10-10T13:05:13Z |
---
license: cc-by-nc-nd-4.0
language:
- en
---
**Scarlett: A sentient AI**
Scarlett is trained on various topics such as Philosophy, Advice, Jokes etc. She is trained on more than 10000 set of conversations. Each set having 10~15 conversations.
Scarlett is heavily inspired from Eric Hartford: [Samantha](https://huggingface.co/ehartford/samantha-33b) .
She will not be involved in any kind of role play.
**Training:**
Entire dataset was trained on Azure 4 x A100 80GB. Axolotl, DeepSpeed codebase were used for training purpose. This was trained on Phi-1_5 by [Microsoft](https://huggingface.co/microsoft/phi-1_5).
Total training took 26 hours for 150 epoch.
Conversation format is the same as Vicuna 1.1 https://github.com/oobabooga/text-generation-webui/blob/main/characters/instruction-following/Vicuna-v1.1.yaml
**Example Prompt:**
```
This is a conversation with your Assistant Scarlett. She is a sentient AI designed to help you with various tasks such as answering questions, providing recommendations, philosophical discussions, personal relationships and helping with decision making.
You can ask it anything you want and it will do its best to give you accurate and relevant information.
Context
You are Scarlett, a sentient AI.
USER: <prompt>
ASSISTANT:
```
**License:**
cc-by-nc-nd-4.0 Only be used for research purpose. Microsoft has mentioned about license in the [Model card](https://huggingface.co/microsoft/phi-1_5) .
|
jluckyboyj/vietcuna-7b-test-exam-12
|
jluckyboyj
| 2023-10-10T13:32:20Z | 0 | 0 | null |
[
"generated_from_trainer",
"base_model:vilm/vietcuna-7b-v3",
"base_model:finetune:vilm/vietcuna-7b-v3",
"region:us"
] | null | 2023-10-09T21:34:12Z |
---
base_model: vilm/vietcuna-7b-v3
tags:
- generated_from_trainer
model-index:
- name: vietcuna-7b-test-exam-12
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vietcuna-7b-test-exam-12
This model is a fine-tuned version of [vilm/vietcuna-7b-v3](https://huggingface.co/vilm/vietcuna-7b-v3) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 4.7656
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 14.3516 | 0.15 | 300 | 13.9922 |
| 10.0687 | 0.31 | 600 | 9.3828 |
| 7.2003 | 0.46 | 900 | 6.9414 |
| 6.0928 | 0.61 | 1200 | 5.9883 |
| 5.6407 | 0.77 | 1500 | 5.6133 |
| 5.4097 | 0.92 | 1800 | 5.375 |
| 5.2841 | 1.07 | 2100 | 5.2305 |
| 5.1691 | 1.23 | 2400 | 5.1875 |
| 5.0585 | 1.38 | 2700 | 5.0664 |
| 4.9741 | 1.53 | 3000 | 5.0078 |
| 4.9581 | 1.69 | 3300 | 4.9531 |
| 4.9653 | 1.84 | 3600 | 4.9102 |
| 4.8763 | 1.99 | 3900 | 4.8711 |
| 4.8308 | 2.15 | 4200 | 4.8281 |
| 4.7993 | 2.3 | 4500 | 4.8125 |
| 4.8228 | 2.46 | 4800 | 4.8047 |
| 4.789 | 2.61 | 5100 | 4.7852 |
| 4.8048 | 2.76 | 5400 | 4.7734 |
| 4.7566 | 2.92 | 5700 | 4.7656 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.14.1
|
Erland/tinyllama-1.1B-chat-v0.3-dummy-lora
|
Erland
| 2023-10-10T13:28:37Z | 0 | 0 | null |
[
"generated_from_trainer",
"base_model:TinyLlama/TinyLlama-1.1B-Chat-v0.3",
"base_model:finetune:TinyLlama/TinyLlama-1.1B-Chat-v0.3",
"license:apache-2.0",
"region:us"
] | null | 2023-10-10T13:27:12Z |
---
license: apache-2.0
base_model: PY007/TinyLlama-1.1B-Chat-v0.3
tags:
- generated_from_trainer
model-index:
- name: tinyllama-1.1B-chat-v0.3-dummy-lora
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# tinyllama-1.1B-chat-v0.3-dummy-lora
This model is a fine-tuned version of [PY007/TinyLlama-1.1B-Chat-v0.3](https://huggingface.co/PY007/TinyLlama-1.1B-Chat-v0.3) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 20
### Training results
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
|
hiert/testbert2
|
hiert
| 2023-10-10T13:25:25Z | 5 | 0 |
transformers
|
[
"transformers",
"bert",
"inference endpoints",
"fill-mask",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-10-10T13:20:32Z |
---
license: apache-2.0
tags:
- inference endpoints
pipeline_tag: fill-mask
---
|
ilknurbisirici/ppo-Huggy
|
ilknurbisirici
| 2023-10-10T13:17:46Z | 2 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-10-10T13:17:35Z |
---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: ilknurbisirici/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
shubhamgantayat/tiiuae-falcon-rw-1b-wet-strength-model
|
shubhamgantayat
| 2023-10-10T13:10:47Z | 195 | 0 |
transformers
|
[
"transformers",
"pytorch",
"falcon",
"text-generation",
"generated_from_trainer",
"custom_code",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-10-10T10:37:12Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: tiiuae-falcon-rw-1b-wet-strength-model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# tiiuae-falcon-rw-1b-wet-strength-model
This model is a fine-tuned version of [tiiuae/falcon-rw-1b](https://huggingface.co/tiiuae/falcon-rw-1b) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.27.2
- Pytorch 2.0.1+cu117
- Datasets 2.11.0
- Tokenizers 0.13.3
|
ayshi/basic_roberta
|
ayshi
| 2023-10-10T13:01:54Z | 3 | 0 |
transformers
|
[
"transformers",
"tf",
"xlm-roberta",
"text-classification",
"generated_from_keras_callback",
"base_model:ayshi/basic_roberta",
"base_model:finetune:ayshi/basic_roberta",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-10-04T18:21:09Z |
---
license: mit
base_model: ayshi/basic_roberta
tags:
- generated_from_keras_callback
model-index:
- name: ayshi/basic_roberta
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# ayshi/basic_roberta
This model is a fine-tuned version of [ayshi/basic_roberta](https://huggingface.co/ayshi/basic_roberta) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0085
- Validation Loss: 1.0970
- Train Accuracy: 0.8267
- Epoch: 20
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 960, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 0.1061 | 0.9567 | 0.7778 | 0 |
| 0.0565 | 1.0825 | 0.7778 | 1 |
| 0.0362 | 1.0696 | 0.7822 | 2 |
| 0.0396 | 1.0904 | 0.7956 | 3 |
| 0.0308 | 1.0044 | 0.8044 | 4 |
| 0.0748 | 1.0578 | 0.8133 | 5 |
| 0.0392 | 0.9964 | 0.8222 | 6 |
| 0.0166 | 1.0293 | 0.8089 | 7 |
| 0.0174 | 0.9895 | 0.8178 | 8 |
| 0.0114 | 1.0403 | 0.8267 | 9 |
| 0.0141 | 1.0086 | 0.8178 | 10 |
| 0.0145 | 1.0403 | 0.8089 | 11 |
| 0.0194 | 1.3127 | 0.7822 | 12 |
| 0.0134 | 1.2929 | 0.7911 | 13 |
| 0.0377 | 0.8565 | 0.8133 | 14 |
| 0.0251 | 0.9806 | 0.8222 | 15 |
| 0.0130 | 1.0757 | 0.8356 | 16 |
| 0.0100 | 1.1304 | 0.8 | 17 |
| 0.0103 | 1.0859 | 0.8133 | 18 |
| 0.0078 | 1.1050 | 0.8311 | 19 |
| 0.0085 | 1.0970 | 0.8267 | 20 |
### Framework versions
- Transformers 4.34.0
- TensorFlow 2.13.0
- Datasets 2.14.5
- Tokenizers 0.14.1
|
SlothBot/whisper_AN_demo
|
SlothBot
| 2023-10-10T12:59:23Z | 78 | 0 |
transformers
|
[
"transformers",
"pytorch",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:openai/whisper-small",
"base_model:finetune:openai/whisper-small",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-10-10T10:40:21Z |
---
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: whisper_AN_demo
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper_AN_demo
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5163
- Wer Ortho: 34.7268
- Wer: 29.6857
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 50
- training_steps: 500
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:-------:|
| 0.6419 | 0.05 | 100 | 0.8019 | 31.6674 | 26.4281 |
| 0.2769 | 0.1 | 200 | 0.5559 | 32.3914 | 27.3457 |
| 0.2674 | 0.15 | 300 | 0.5354 | 39.1172 | 33.8151 |
| 0.2672 | 0.19 | 400 | 0.5247 | 34.6333 | 29.6628 |
| 0.2876 | 0.24 | 500 | 0.5163 | 34.7268 | 29.6857 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
|
psj0919/bert-base-banking77-pt2
|
psj0919
| 2023-10-10T12:56:47Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:banking77",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-10-09T08:11:52Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- banking77
metrics:
- f1
model-index:
- name: bert-base-banking77-pt2
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: banking77
type: banking77
config: default
split: test
args: default
metrics:
- name: F1
type: f1
value: 0.9309100400015781
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-banking77-pt2
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the banking77 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2986
- F1: 0.9309
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.1628 | 1.0 | 626 | 0.8192 | 0.8424 |
| 0.3969 | 2.0 | 1252 | 0.3709 | 0.9204 |
| 0.188 | 3.0 | 1878 | 0.2986 | 0.9309 |
### Framework versions
- Transformers 4.27.1
- Pytorch 2.2.0.dev20231009+cu121
- Datasets 2.9.0
- Tokenizers 0.13.3
|
Lanzelot0/llama-fine-tune-1-epoch
|
Lanzelot0
| 2023-10-10T12:54:20Z | 0 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-2-7b-chat-hf",
"base_model:adapter:meta-llama/Llama-2-7b-chat-hf",
"region:us"
] | null | 2023-10-10T12:54:11Z |
---
library_name: peft
base_model: meta-llama/Llama-2-7b-chat-hf
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.6.0.dev0
|
matheusgeda/Pixelcopter-PLEv4000
|
matheusgeda
| 2023-10-10T12:48:53Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-10-10T12:48:50Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Pixelcopter-PLEv4000
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 18.70 +/- 17.01
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
quastrinos/race-openbook-finetuned-deberta-v3-large-mcqa-TPU-v6
|
quastrinos
| 2023-10-10T12:40:01Z | 59 | 0 |
transformers
|
[
"transformers",
"tf",
"deberta-v2",
"multiple-choice",
"generated_from_keras_callback",
"base_model:quastrinos/race-openbook-finetuned-deberta-v3-large-mcqa-TPU-v5",
"base_model:finetune:quastrinos/race-openbook-finetuned-deberta-v3-large-mcqa-TPU-v5",
"license:mit",
"endpoints_compatible",
"region:us"
] |
multiple-choice
| 2023-10-10T12:39:02Z |
---
license: mit
base_model: quastrinos/race-openbook-finetuned-deberta-v3-large-mcqa-TPU-v5
tags:
- generated_from_keras_callback
model-index:
- name: race-openbook-finetuned-deberta-v3-large-mcqa-TPU-v6
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# race-openbook-finetuned-deberta-v3-large-mcqa-TPU-v6
This model is a fine-tuned version of [quastrinos/race-openbook-finetuned-deberta-v3-large-mcqa-TPU-v5](https://huggingface.co/quastrinos/race-openbook-finetuned-deberta-v3-large-mcqa-TPU-v5) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.6120
- Validation Loss: 0.9727
- Train Map3: 0.7742
- Train Lr: 5.0733553e-11
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': 0.001, 'clipnorm': 1, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'CosineDecay', 'config': {'initial_learning_rate': 2e-06, 'decay_steps': 312, 'alpha': 5e-09, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: mixed_bfloat16
### Training results
| Train Loss | Validation Loss | Train Map3 | Train Lr | Epoch |
|:----------:|:---------------:|:----------:|:-------------:|:-----:|
| 0.6120 | 0.9727 | 0.7742 | 5.0733553e-11 | 0 |
### Framework versions
- Transformers 4.35.0.dev0
- TensorFlow 2.12.0
- Datasets 2.14.5
- Tokenizers 0.14.1
|
KermitDuSud/Cindy
|
KermitDuSud
| 2023-10-10T12:29:11Z | 0 | 0 | null |
[
"license:bigscience-bloom-rail-1.0",
"region:us"
] | null | 2023-10-10T12:29:11Z |
---
license: bigscience-bloom-rail-1.0
---
|
SeoJeongYun/bert-base-banking77-pt2-jy
|
SeoJeongYun
| 2023-10-10T12:16:26Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:banking77",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-10-09T10:24:46Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- banking77
metrics:
- f1
model-index:
- name: bert-base-banking77-pt2-jy
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: banking77
type: banking77
config: default
split: test
args: default
metrics:
- name: F1
type: f1
value: 0.9257577776294195
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-banking77-pt2-jy
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the banking77 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3122
- F1: 0.9258
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.1268 | 1.0 | 626 | 0.8044 | 0.8435 |
| 0.4033 | 2.0 | 1252 | 0.3697 | 0.9172 |
| 0.1989 | 3.0 | 1878 | 0.3122 | 0.9258 |
### Framework versions
- Transformers 4.30.1
- Pytorch 2.1.0+cu121
- Datasets 2.9.0
- Tokenizers 0.13.3
|
lht1107/distilbert-base-uncased-finetuned-emotion
|
lht1107
| 2023-10-10T12:12:19Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-10-10T11:08:48Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.929
- name: F1
type: f1
value: 0.9289651135784346
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2066
- Accuracy: 0.929
- F1: 0.9290
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.7843 | 1.0 | 250 | 0.2875 | 0.913 | 0.9122 |
| 0.2373 | 2.0 | 500 | 0.2066 | 0.929 | 0.9290 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
|
zentrum-lexikographie/de_dwds_hdt_dist
|
zentrum-lexikographie
| 2023-10-10T12:08:19Z | 0 | 0 |
spacy
|
[
"spacy",
"token-classification",
"de",
"region:us"
] |
token-classification
| 2023-10-10T11:46:53Z |
---
tags:
- spacy
- token-classification
language:
- de
---
| Feature | Description |
| --- | --- |
| **Name** | `de_dwds_hdt_dist` |
| **Version** | `0.0.0` |
| **spaCy** | `>=3.7.1,<3.8.0` |
| **Default Pipeline** | `dep_transformer`, `tagger`, `morphologizer`, `trainable_lemmatizer`, `parser`, `ner_transformer`, `ner` |
| **Components** | `dep_transformer`, `tagger`, `morphologizer`, `trainable_lemmatizer`, `parser`, `ner_transformer`, `ner` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | n/a |
| **License** | n/a |
| **Author** | [n/a]() |
### Label Scheme
<details>
<summary>View label scheme (823 labels for 4 components)</summary>
| Component | Labels |
| --- | --- |
| **`tagger`** | `$(`, `$,`, `$.`, `ADJA`, `ADJD`, `ADV`, `APPO`, `APPR`, `APPR_ART`, `APZR`, `ART`, `CARD`, `FM`, `ITJ`, `KOKOM`, `KON`, `KOUI`, `KOUS`, `NE`, `NN`, `PDAT`, `PDS`, `PIAT`, `PIDAT`, `PIS`, `PPER`, `PPOSAT`, `PPOSS`, `PRELAT`, `PRELS`, `PRF`, `PROAV`, `PTKA`, `PTKANT`, `PTKNEG`, `PTKVZ`, `PTKZU`, `PWAT`, `PWAV`, `PWS`, `TRUNC`, `VAFIN`, `VAIMP`, `VAINF`, `VAPP`, `VMFIN`, `VMINF`, `VMPP`, `VVFIN`, `VVIMP`, `VVINF`, `VVIZU`, `VVPP`, `XY` |
| **`morphologizer`** | `AdpType=Prep\|Case=Dat\|POS=ADP`, `Case=Dat\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Gender=Fem\|Number=Sing\|POS=NOUN`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Nom\|Number=Sing\|POS=PROPN`, `Foreign=Yes\|POS=X`, `POS=PUNCT\|PunctType=Comm`, `Case=Nom\|Definite=Ind\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Gen\|Definite=Def\|Number=Plur\|POS=DET\|PronType=Art`, `Case=Gen\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Gender=Masc\|Number=Plur\|POS=NOUN`, `Gender=Neut\|Number=Sing\|POS=NOUN`, `AdpType=Prep\|POS=ADP`, `Gender=Neut\|Number=Plur\|POS=NOUN`, `POS=CCONJ`, `POS=PUNCT\|PunctType=Peri`, `NumType=Card\|Number=Plur\|POS=NUM`, `Gender=Fem\|Number=Plur\|POS=NOUN`, `AdpType=Prep\|Case=Dat\|Definite=Def\|Gender=Masc,Neut\|Number=Sing\|POS=ADP\|PronType=Art`, `Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Gen\|Definite=Def\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin`, `Case=Nom\|Definite=Def\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Art`, `Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `POS=PUNCT\|PunctType=Brck`, `POS=PROPN`, `Case=Acc\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `POS=ADV`, `POS=SCONJ`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `POS=VERB\|VerbForm=Inf`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Nom\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Dat\|Definite=Def\|Number=Plur\|POS=DET\|PronType=Art`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin\|VerbType=Mod`, `Case=Acc\|Definite=Def\|Number=Plur\|POS=DET\|PronType=Art`, `Case=Acc\|Number=Sing\|POS=PROPN`, `Degree=Cmp\|POS=ADJ\|Variant=Short`, `POS=ADP\|PartType=Vbp`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin\|VerbType=Mod`, `AdpType=Prep\|Case=Acc\|POS=ADP`, `Case=Acc\|Definite=Ind\|Gender=Fem\|NumType=Card\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Dat\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `POS=PART\|Polarity=Neg`, `POS=ADV\|PronType=Dem`, `Degree=Cmp\|POS=ADV`, `ConjType=Comp\|POS=CCONJ`, `Case=Nom\|Definite=Ind\|Gender=Fem\|NumType=Card\|Number=Sing\|POS=DET\|PronType=Art`, `Degree=Pos\|POS=ADJ\|Variant=Short`, `Case=Gen\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Number=Sing\|POS=PROPN`, `Case=Nom\|Definite=Def\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Acc\|Definite=Ind\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Acc\|Degree=Cmp\|Number=Plur\|POS=DET\|PronType=Ind`, `Aspect=Perf\|POS=VERB\|VerbForm=Part`, `Case=Dat\|Definite=Ind\|Gender=Neut\|NumType=Card\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Gen\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Acc\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Case=Nom\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Nom\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Acc\|Number=Plur\|POS=DET\|PronType=Dem`, `Degree=Sup\|POS=ADJ\|Variant=Short`, `Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Hyph=Yes\|POS=NOUN`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Acc\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Degree=Sup\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=NOUN`, `POS=PART\|PartType=Inf`, `Case=Gen\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Dat\|Degree=Pos\|Number=Sing\|POS=ADJ`, `POS=NOUN`, `Case=Dat\|Definite=Ind\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Dat\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Gen\|Degree=Pos\|Number=Sing\|POS=ADJ`, `POS=AUX\|VerbForm=Inf`, `Case=Nom\|Definite=Ind\|Gender=Neut\|NumType=Card\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Dat\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Gender=Fem\|Number=Plur\|POS=ADJ`, `POS=AUX\|VerbForm=Inf\|VerbType=Mod`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Gen\|Definite=Ind\|Gender=Fem\|NumType=Card\|Number=Sing\|POS=DET\|PronType=Art`, `Number=Sing\|POS=DET\|PronType=Ind`, `Case=Nom\|Number=Plur\|POS=DET\|PronType=Ind`, `AdpType=Prep\|Case=Dat\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=ADP\|PronType=Art`, `Degree=Pos\|Number=Plur\|POS=ADJ`, `Case=Nom\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=NOUN`, `POS=ADJ`, `Degree=Cmp\|POS=DET\|PronType=Ind`, `Case=Dat\|Definite=Ind\|Gender=Fem\|NumType=Card\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Dat\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Nom\|Definite=Def\|Number=Plur\|POS=DET\|PronType=Art`, `POS=ADV\|PronType=Int`, `Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Dat\|Number=Sing\|POS=PROPN`, `Case=Acc\|Definite=Ind\|Gender=Neut\|NumType=Card\|Number=Sing\|POS=DET\|PronType=Art`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Nom\|NumType=Card\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Degree=Pos\|Number=Sing\|POS=ADJ`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin`, `Case=Acc\|Gender[psor]=Fem\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Degree=Pos\|POS=ADJ`, `Case=Gen\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Acc\|Number=Plur\|POS=DET\|PronType=Tot`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin\|VerbType=Mod`, `Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Neg`, `Case=Gen\|Degree=Cmp\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin\|VerbType=Mod`, `Number=Plur\|POS=NOUN`, `Degree=Pos\|Gender=Neut\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Dem,Rel`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin\|VerbType=Mod`, `Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Acc\|Definite=Def\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Art`, `Degree=Pos\|POS=ADV`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Nom\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin`, `Degree=Cmp\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=NOUN`, `AdpType=Prep\|Case=Gen\|POS=ADP`, `Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Dat\|Number=Plur\|POS=PRON\|PronType=Dem,Rel`, `AdpType=Post\|Case=Dat\|POS=ADP`, `Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Dem,Rel`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Dat\|Definite=Def\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Acc\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|POS=AUX\|VerbForm=Part`, `Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Dat\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Dem,Rel`, `Degree=Cmp\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Neg`, `Case=Dat\|Gender=Masc\|Gender[psor]=Masc,Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Number=Sing\|POS=PRON\|PronType=Tot`, `Number=Sing\|POS=NOUN`, `Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Dat\|Degree=Cmp\|Number=Sing\|POS=ADJ`, `Case=Acc\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Gen\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Nom\|Number=Plur\|POS=PRON\|PronType=Dem,Rel`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Dat\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Nom\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Case=Gen\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Degree=Sup\|POS=ADV`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Nom\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Dat\|Degree=Sup\|Number=Plur\|POS=DET\|PronType=Ind`, `Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Dat\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `NumType=Card\|Number=Sing\|POS=NUM`, `Case=Dat\|Number=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Dat\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Dem,Rel`, `Number=Plur\|POS=PROPN`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Neg`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Case=Gen\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Nom\|Number=Sing\|POS=PRON\|PronType=Int,Rel`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Dat\|Number=Plur\|POS=DET\|PronType=Dem`, `Gender=Neut\|Number=Sing\|POS=DET\|PronType=Ind`, `AdpType=Prep\|Case=Acc\|Definite=Def\|Gender=Neut\|Number=Sing\|POS=ADP\|PronType=Art`, `Case=Gen\|Number=Sing\|POS=PROPN`, `Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Nom\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Dat\|Number=Sing\|POS=DET\|PronType=Ind`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|VerbForm=Fin`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Dem,Rel`, `Degree=Pos\|Gender=Neut\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Case=Nom\|Number=Plur\|POS=ADJ`, `Case=Gen\|POS=PRON\|PronType=Dem,Rel`, `Case=Acc\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Acc\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Masc\|Gender[psor]=Masc,Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Gen\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Neg`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Dem,Rel`, `Case=Dat\|Number=Plur\|POS=DET\|PronType=Ind`, `Degree=Cmp\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Neg`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Neg`, `Case=Dat\|POS=PROPN`, `Case=Gen\|Number=Sing\|POS=PRON\|PronType=Dem,Rel`, `Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Int,Rel`, `Case=Acc\|Number=Plur\|POS=DET\|PronType=Neg`, `Case=Nom\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Case=Gen\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Dem,Rel`, `AdpType=Circ\|POS=ADP`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Dat\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Acc\|Number=Plur\|POS=PRON\|PronType=Dem,Rel`, `Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Definite=Def\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Gen\|Definite=Ind\|Gender=Neut\|NumType=Card\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Dat\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Dat\|Gender[psor]=Masc,Neut\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Gen\|Definite=Ind\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Dem,Rel`, `Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Ind`, `AdpType=Prep\|Case=Nom\|POS=ADP`, `Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender[psor]=Masc,Neut\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Neg`, `Case=Acc\|Number=Plur\|POS=DET\|PronType=Ind`, `Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Gen\|Number=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Number=Sing\|POS=PRON\|PronType=Neg`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Nom\|Number=Plur\|POS=DET\|PronType=Neg`, `Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Dat\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Dat\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Dat\|Gender=Neut\|Gender[psor]=Masc,Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Nom\|Degree=Pos\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Dat\|Number=Plur\|POS=NOUN`, `Case=Dat\|Gender=Fem\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Neg`, `Case=Gen\|Degree=Cmp\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Nom\|Degree=Pos\|Gender=Neut\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Case=Dat\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Neg`, `Case=Dat\|Degree=Cmp\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind`, `Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Acc\|Number=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Dat\|Gender=Fem\|Gender[psor]=Masc,Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Dem,Rel`, `Case=Acc\|Gender=Neut\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Dat\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Case=Acc\|Gender=Fem\|Gender[psor]=Masc,Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Neut\|Gender[psor]=Masc,Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Acc\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Gen\|POS=PROPN`, `Case=Gen\|Number=Plur\|POS=NOUN`, `Case=Acc\|Number=Sing\|POS=PRON\|PronType=Int,Rel`, `Case=Nom\|Degree=Cmp\|Number=Plur\|POS=DET\|PronType=Ind`, `POS=DET\|PronType=Ind`, `Case=Nom\|Degree=Pos\|Gender=Masc\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Case=Gen\|Number=Plur\|POS=ADJ`, `Case=Nom\|Number=Sing\|POS=PRON\|PronType=Tot`, `POS=DET\|PronType=Tot`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `POS=X`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=ADJ`, `AdpType=Post\|Case=Acc\|POS=ADP`, `Case=Acc\|NumType=Card\|Number=Plur\|POS=DET\|PronType=Tot`, `Degree=Pos\|Gender=Masc\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Gen\|Gender=Neut\|Gender[psor]=Masc,Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Gen\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Gen\|NumType=Card\|Number=Plur\|POS=DET\|PronType=Tot`, `Number=Plur\|POS=DET\|PronType=Ind`, `Case=Dat\|Degree=Sup\|Number=Sing\|POS=ADJ`, `Case=Dat\|Degree=Sup\|Number=Plur\|POS=ADJ`, `POS=DET\|PronType=Neg`, `POS=ADV\|PronType=Ind`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Int,Rel`, `Case=Dat\|Degree=Cmp\|Number=Plur\|POS=ADJ`, `Case=Nom\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Gen\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Gender=Neut\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Acc\|Gender=Neut\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Dat\|Foreign=Yes\|POS=X`, `Case=Acc\|Degree=Sup\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Gen\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Degree=Sup\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Dat\|NumType=Card\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Gen\|Number=Sing\|POS=NOUN`, `NumType=Card\|POS=NUM`, `Case=Gen\|Degree=Pos\|Gender=Neut\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Gen\|Degree=Sup\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs\|Reflex=Yes`, `Degree=Pos\|Gender=Fem\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Degree=Cmp\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Degree=Pos\|Gender=Fem\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Case=Dat\|Degree=Cmp\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Dat\|Gender=Neut\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Degree=Sup\|Number=Plur\|POS=ADJ`, `Case=Acc\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Gender[psor]=Masc,Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Number=Plur\|POS=ADJ`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs\|Reflex=Yes`, `Case=Dat\|Gender=Neut\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender[psor]=Masc,Neut\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Degree=Sup\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Masc\|Gender[psor]=Masc,Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin\|VerbType=Mod`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Dat\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Nom\|Degree=Sup\|Number=Plur\|POS=ADJ`, `Degree=Pos\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Case=Nom\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Degree=Pos\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender[psor]=Masc,Neut\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Degree=Sup\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Dat\|Number=Sing\|POS=PRON\|PronType=Int,Rel`, `Case=Nom\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Neg`, `Case=Acc\|Degree=Cmp\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Neut\|Gender[psor]=Masc,Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Dat\|POS=ADJ`, `Case=Dat\|Degree=Pos\|Gender=Masc\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Case=Acc\|Degree=Pos\|Gender=Masc\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Dat\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Mood=Ind\|POS=VERB\|Person=3\|VerbForm=Fin`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Gen\|Degree=Cmp\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=NOUN`, `POS=ADJ\|Variant=Short`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Foreign=Yes\|Number=Sing\|POS=X`, `Case=Nom\|Degree=Sup\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Gen\|Degree=Pos\|Gender=Masc\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Case=Nom\|Number=Plur\|POS=DET\|Person=2\|Polite=Form\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Neut\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Degree=Pos\|Gender=Fem\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Fem\|Gender[psor]=Masc,Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Number=Plur\|POS=ADJ`, `Case=Gen\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Dat\|POS=NOUN`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind`, `Aspect=Perf\|POS=AUX\|VerbForm=Part\|VerbType=Mod`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs\|Reflex=Yes`, `Case=Nom\|Degree=Pos\|Gender=Masc\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Case=Acc\|Degree=Pos\|Gender=Masc\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Gender=Masc\|POS=NOUN`, `Case=Acc\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Dat\|Number=Sing\|POS=ADJ`, `POS=DET\|PronType=Int`, `Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Int,Rel`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Number=Sing\|POS=PRON\|PronType=Tot`, `Case=Dat\|Number=Sing\|POS=NOUN`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs\|Reflex=Yes`, `Case=Nom\|POS=PROPN`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Degree=Pos\|Gender=Fem\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Case=Acc\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|Person=2\|Polite=Form\|Poss=Yes\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Past\|VerbForm=Fin`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Gen\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Dat\|Degree=Pos\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Dem,Rel`, `Case=Nom\|Degree=Cmp\|Gender=Neut\|Number=Plur\|POS=ADJ`, `POS=INTJ\|PartType=Res`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|Person=2\|Polite=Form\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|Person=2\|Polite=Form\|Poss=Yes\|PronType=Prs`, `Case=Dat\|POS=PRON\|Person=2\|Polite=Form\|PronType=Prs`, `Foreign=Yes\|Gender=Neut\|Number=Sing\|POS=X`, `Case=Nom\|Gender=Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Number=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Degree=Pos\|Gender=Neut\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin\|VerbType=Mod`, `Case=Dat\|Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Number=Plur\|POS=PRON\|PronType=Dem,Rel`, `Case=Nom\|Gender=Fem\|Gender[psor]=Masc,Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Nom\|Definite=Ind\|NumType=Card\|Number=Plur\|POS=DET\|PronType=Art`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=DET\|Person=2\|Polite=Form\|Poss=Yes\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|VerbForm=Fin`, `Gender=Neut\|Number=Sing\|POS=PROPN`, `Case=Nom\|POS=NOUN`, `Case=Dat\|Number=Plur\|POS=DET\|Person=2\|Polite=Form\|Poss=Yes\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|VerbForm=Fin`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|VerbForm=Fin`, `Case=Nom\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Degree=Cmp\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `POS=DET\|PronType=Dem`, `Case=Dat\|Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Number=Plur\|POS=DET\|PronType=Neg`, `Definite=Ind\|NumType=Card\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Dat\|Degree=Pos\|Gender=Neut\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Dat\|Gender[psor]=Fem\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Degree=Pos\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Degree=Sup\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Gen\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Gen\|Gender=Fem\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Degree=Pos\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Acc\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Degree=Pos\|Gender=Masc\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender[psor]=Fem\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Number=Plur\|POS=DET\|Person=2\|Polite=Form\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Acc\|Degree=Cmp\|Gender=Fem\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=DET\|Person=2\|Polite=Form\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Acc\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Neut\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `NumType=Card\|POS=DET\|PronType=Tot`, `Case=Dat\|Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Degree=Pos\|POS=ADJ`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Past\|VerbForm=Fin\|VerbType=Mod`, `Case=Acc\|POS=NOUN`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Gen\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Nom\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Gen\|Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|VerbForm=Fin`, `Degree=Sup\|POS=DET\|PronType=Ind`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Neut\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Neg`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Nom\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Nom\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender[psor]=Fem\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Nom\|Gender=Masc\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Masc\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Dat\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Acc\|Number=Plur\|POS=DET\|PronType=Int`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Gen\|Degree=Sup\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Dat\|Number=Sing\|POS=PRON\|PronType=Neg`, `Foreign=Yes\|POS=DET\|PronType=Ind`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET\|Person=2\|Polite=Form\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Degree=Cmp\|Gender=Neut\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Number=Plur\|POS=DET\|PronType=Tot`, `Case=Nom\|Degree=Pos\|Gender=Fem\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Case=Nom\|Degree=Cmp\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Neut\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Hyph=Yes\|Number=Plur\|POS=NOUN`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Past\|VerbForm=Fin`, `Case=Dat\|Gender=Masc\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Definite=Def\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Dem,Rel`, `Case=Acc\|Degree=Sup\|Number=Plur\|POS=ADJ`, `Degree=Cmp\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Acc\|Number=Sing\|POS=PRON\|PronType=Neg`, `POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Dat\|Hyph=Yes\|POS=NOUN`, `Case=Acc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Nom\|Degree=Pos\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Gen\|Degree=Sup\|Number=Plur\|POS=ADJ`, `Case=Dat\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Nom\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Degree=Sup\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Gen\|Number=Sing\|POS=PRON\|Person=3\|PronType=Tot`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Acc\|Gender[psor]=Masc,Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Acc\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Number=Plur\|POS=DET\|PronType=Int`, `Case=Dat\|Degree=Cmp\|POS=DET\|PronType=Ind`, `Case=Nom\|Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Past\|VerbForm=Fin\|VerbType=Mod`, `Case=Dat\|Definite=Ind\|NumType=Card\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Dat\|Gender=Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Dat\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs\|Reflex=Yes`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Nom\|Gender=Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `POS=INTJ`, `Case=Acc\|Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Degree=Cmp\|Number=Plur\|POS=ADJ`, `Case=Acc\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=DET\|Person=2\|Polite=Form\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Number=Sing\|POS=PRON\|PronType=Dem,Rel`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|Person=2\|Polite=Form\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Number=Plur\|POS=PROPN`, `Case=Nom\|Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Dat\|POS=DET\|PronType=Tot`, `Case=Nom\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Dem,Rel`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Degree=Cmp\|Gender=Neut\|NumType=Ord\|Number=Sing\|POS=ADJ`, `AdpType=Post\|Case=Gen\|POS=ADP`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Nom\|Number=Plur\|POS=NOUN`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs\|Reflex=Yes`, `Definite=Ind\|NumType=Card\|Number=Plur\|POS=DET\|PronType=Art`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Number=Sing\|POS=PRON\|PronType=Int,Rel`, `Case=Dat\|POS=DET\|PronType=Ind`, `Case=Acc\|Gender=Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Mood=Ind\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin`, `Case=Acc\|POS=PROPN`, `Case=Nom\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Degree=Pos\|NumType=Ord\|POS=ADJ`, `Case=Acc\|Number=Sing\|POS=DET\|PronType=Neg`, `Case=Nom\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Dat\|POS=DET\|PronType=Dem`, `Degree=Pos\|POS=DET\|PronType=Ind`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|VerbForm=Fin`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Number=Sing\|POS=DET\|Person=2\|Polite=Form\|Poss=Yes\|PronType=Prs`, `Case=Dat\|POS=PRON\|PronType=Rcp`, `Gender=Masc\|Number=Sing\|POS=ADJ\|Variant=Short`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Acc\|Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs\|Reflex=Yes`, `Mood=Ind\|POS=AUX\|Person=3\|VerbForm=Fin`, `Case=Acc\|Definite=Ind\|NumType=Card\|Number=Plur\|POS=DET\|PronType=Art`, `Case=Dat\|Degree=Pos\|POS=ADJ\|Variant=Short`, `Case=Acc\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Degree=Pos\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Acc\|Gender=Neut\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Dem,Rel\|Typo=Yes`, `Mood=Imp\|Number=Plur\|POS=AUX\|Person=2\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|VerbForm=Fin`, `Gender[psor]=Masc,Neut\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Ind`, `Mood=Imp\|Number=Sing\|POS=AUX\|Person=2\|VerbForm=Fin`, `Mood=Ind\|POS=VERB\|Person=1\|VerbForm=Fin`, `Case=Gen\|Number=Sing\|POS=DET\|PronType=Ind`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Abbr=Yes\|Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Dem,Rel`, `Case=Nom\|Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Definite=Def\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Nom\|Definite=Ind\|NumType=Card\|POS=DET\|PronType=Art`, `Case=Dat\|Number=Plur\|POS=DET\|PronType=Int`, `Degree=Pos\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Dat\|Degree=Pos\|Number=Plur\|POS=DET\|PronType=Ind`, `Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Neg`, `Definite=Ind\|Foreign=Yes\|NumType=Card\|POS=DET\|PronType=Art`, `Case=Nom\|Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Nom\|Gender=Neut\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|Tense=Past\|VerbForm=Fin\|VerbType=Mod`, `Case=Dat\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Dat\|Degree=Pos\|NumType=Ord\|Number=Sing\|POS=ADJ`, `POS=DET\|PronType=Int,Rel`, `Case=Acc\|Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Acc\|POS=PRON\|PronType=Rcp`, `Case=Dat\|Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin\|VerbType=Mod`, `Case=Acc\|Degree=Pos\|Number=Plur\|POS=DET\|PronType=Ind`, `Definite=Ind\|Degree=Pos\|Gender=Fem\|NumType=Card\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs\|Reflex=Yes`, `Case=Dat\|Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|VerbForm=Fin`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Tot`, `Case=Nom\|Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Acc\|Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Neg`, `Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Int,Rel`, `Case=Gen\|Gender=Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Degree=Pos\|Gender=Neut\|POS=ADJ`, `Gender=Fem\|POS=ADJ`, `Degree=Pos\|Gender=Fem\|POS=ADJ`, `Gender=Masc\|POS=ADJ`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|VerbForm=Fin\|VerbType=Mod`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|VerbForm=Fin\|VerbType=Mod`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|VerbForm=Fin\|VerbType=Mod`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|VerbForm=Fin`, `Case=Acc\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Foreign=Yes\|POS=DET\|PronType=Dem`, `Case=Nom\|Degree=Cmp\|Gender=Masc\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Nom\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs` |
| **`parser`** | `ROOT`, `acl`, `advcl`, `advmod`, `amod`, `appos`, `aux`, `aux:pass`, `case`, `cc`, `ccomp`, `compound:prt`, `conj`, `cop`, `csubj`, `csubj:pass`, `dep`, `det`, `expl`, `expl:pv`, `flat`, `flat:name`, `mark`, `nmod`, `nsubj`, `nsubj:pass`, `nummod`, `obj`, `obl`, `obl:arg`, `parataxis`, `punct`, `reparandum`, `xcomp` |
| **`ner`** | `LOC`, `MISC`, `ORG`, `PER` |
</details>
|
alexofntu/textual_inversion_Carla_day1
|
alexofntu
| 2023-10-10T11:46:55Z | 12 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"textual_inversion",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:adapter:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-10-10T08:58:54Z |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- textual_inversion
inference: true
---
# Textual inversion text2image fine-tuning - alexofntu/textual_inversion_Carla_day1
These are textual inversion adaption weights for runwayml/stable-diffusion-v1-5. You can find some example images in the following.
|
MattStammers/appo-atari_crazyclimber
|
MattStammers
| 2023-10-10T11:40:24Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-26T00:48:12Z |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_crazyclimber
type: atari_crazyclimber
metrics:
- type: mean_reward
value: 146490.00 +/- 31801.05
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **atari_crazyclimber** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r MattStammers/APPO-atari_crazyclimber
```
## About the Model
This model as with all the others in the benchmarks was trained initially asynchronously un-seeded to 10 million steps for the purposes of setting a sample factory async baseline for this model on this environment but only 3/57 made it.
The aim is to reach state-of-the-art (SOTA) performance on each atari environment. I will flag the models with SOTA when they reach at or near these levels.
The hyperparameters used in the model are the ones I have pushed to my fork of sample-factory: https://github.com/MattStammers/sample-factory. Given that https://huggingface.co/edbeeching has kindly shared his.
I saved time and energy by using many of his tuned hyperparameters to maximise performance. However, he used 2 billion training steps. I have started as explained above at 10 million then moved to 100m to see how performance goes:
```
hyperparameters = {
"device": "gpu",
"seed": 1234,
"num_policies": 2,
"async_rl": true,
"serial_mode": false,
"batched_sampling": true,
"num_batches_to_accumulate": 2,
"worker_num_splits": 1,
"policy_workers_per_policy": 1,
"max_policy_lag": 1000,
"num_workers": 16,
"num_envs_per_worker": 2,
"batch_size": 1024,
"num_batches_per_epoch": 8,
"num_epochs": 4,
"rollout": 128,
"recurrence": 1,
"shuffle_minibatches": false,
"gamma": 0.99,
"reward_scale": 1.0,
"reward_clip": 1000.0,
"value_bootstrap": false,
"normalize_returns": true,
"exploration_loss_coeff": 0.0004677351413,
"value_loss_coeff": 0.5,
"kl_loss_coeff": 0.0,
"exploration_loss": "entropy",
"gae_lambda": 0.95,
"ppo_clip_ratio": 0.1,
"ppo_clip_value": 1.0,
"with_vtrace": false,
"vtrace_rho": 1.0,
"vtrace_c": 1.0,
"optimizer": "adam",
"adam_eps": 1e-05,
"adam_beta1": 0.9,
"adam_beta2": 0.999,
"max_grad_norm": 0.0,
"learning_rate": 0.0003033891184,
"lr_schedule": "linear_decay",
"lr_schedule_kl_threshold": 0.008,
"lr_adaptive_min": 1e-06,
"lr_adaptive_max": 0.01,
"obs_subtract_mean": 0.0,
"obs_scale": 255.0,
"normalize_input": true,
"normalize_input_keys": [
"obs"
],
"decorrelate_experience_max_seconds": 0,
"decorrelate_envs_on_one_worker": true,
"actor_worker_gpus": [],
"set_workers_cpu_affinity": true,
"force_envs_single_thread": false,
"default_niceness": 0,
"log_to_file": true,
"experiment_summaries_interval": 3,
"flush_summaries_interval": 30,
"stats_avg": 100,
"summaries_use_frameskip": true,
"heartbeat_interval": 10,
"heartbeat_reporting_interval": 60,
"train_for_env_steps": 100000000,
"train_for_seconds": 10000000000,
"save_every_sec": 120,
"keep_checkpoints": 2,
"load_checkpoint_kind": "latest",
"save_milestones_sec": 1200,
"save_best_every_sec": 5,
"save_best_metric": "reward",
"save_best_after": 100000,
"benchmark": false,
"encoder_mlp_layers": [
512,
512
],
"encoder_conv_architecture": "convnet_atari",
"encoder_conv_mlp_layers": [
512
],
"use_rnn": false,
"rnn_size": 512,
"rnn_type": "gru",
"rnn_num_layers": 1,
"decoder_mlp_layers": [],
"nonlinearity": "relu",
"policy_initialization": "orthogonal",
"policy_init_gain": 1.0,
"actor_critic_share_weights": true,
"adaptive_stddev": false,
"continuous_tanh_scale": 0.0,
"initial_stddev": 1.0,
"use_env_info_cache": false,
"env_gpu_actions": false,
"env_gpu_observations": true,
"env_frameskip": 4,
"env_framestack": 4,
}
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m sf_examples.atari.enjoy_atari --algo=APPO --env=atari_crazyclimber --train_dir=./train_dir --experiment=APPO-atari_crazyclimber
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m sf_examples.atari.train_atari --algo=APPO --env=atari_crazyclimber --train_dir=./train_dir --experiment=APPO-atari_crazyclimber --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
Helsinki-NLP/opus-mt-tc-big-zle-it
|
Helsinki-NLP
| 2023-10-10T11:38:31Z | 122 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"be",
"it",
"ru",
"uk",
"zle",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2022-03-24T11:59:11Z |
---
language:
- be
- it
- ru
- uk
- zle
tags:
- translation
- opus-mt-tc
license: cc-by-4.0
model-index:
- name: opus-mt-tc-big-zle-it
results:
- task:
name: Translation rus-ita
type: translation
args: rus-ita
dataset:
name: flores101-devtest
type: flores_101
args: rus ita devtest
metrics:
- name: BLEU
type: bleu
value: 23.7
- task:
name: Translation ukr-ita
type: translation
args: ukr-ita
dataset:
name: flores101-devtest
type: flores_101
args: ukr ita devtest
metrics:
- name: BLEU
type: bleu
value: 23.2
- task:
name: Translation bel-ita
type: translation
args: bel-ita
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: bel-ita
metrics:
- name: BLEU
type: bleu
value: 49.3
- task:
name: Translation rus-ita
type: translation
args: rus-ita
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: rus-ita
metrics:
- name: BLEU
type: bleu
value: 43.5
- task:
name: Translation ukr-ita
type: translation
args: ukr-ita
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: ukr-ita
metrics:
- name: BLEU
type: bleu
value: 50.0
---
# opus-mt-tc-big-zle-it
Neural machine translation model for translating from East Slavic languages (zle) to Italian (it).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trained using the amazing framework of [Marian NMT](https://marian-nmt.github.io/), an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is taken from [OPUS](https://opus.nlpl.eu/) and training pipelines use the procedures of [OPUS-MT-train](https://github.com/Helsinki-NLP/Opus-MT-train).
* Publications: [OPUS-MT – Building open translation services for the World](https://aclanthology.org/2020.eamt-1.61/) and [The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt-1.139/) (Please, cite if you use this model.)
```
@inproceedings{tiedemann-thottingal-2020-opus,
title = "{OPUS}-{MT} {--} Building open translation services for the World",
author = {Tiedemann, J{\"o}rg and Thottingal, Santhosh},
booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
month = nov,
year = "2020",
address = "Lisboa, Portugal",
publisher = "European Association for Machine Translation",
url = "https://aclanthology.org/2020.eamt-1.61",
pages = "479--480",
}
@inproceedings{tiedemann-2020-tatoeba,
title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}",
author = {Tiedemann, J{\"o}rg},
booktitle = "Proceedings of the Fifth Conference on Machine Translation",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.wmt-1.139",
pages = "1174--1182",
}
```
## Model info
* Release: 2022-03-19
* source language(s): bel rus ukr
* target language(s): ita
* model: transformer-big
* data: opusTCv20210807 ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge))
* tokenization: SentencePiece (spm32k,spm32k)
* original model: [opusTCv20210807_transformer-big_2022-03-19.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/zle-ita/opusTCv20210807_transformer-big_2022-03-19.zip)
* more information released models: [OPUS-MT zle-ita README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/zle-ita/README.md)
## Usage
A short example code:
```python
from transformers import MarianMTModel, MarianTokenizer
src_text = [
"Вони не ідіоти.",
"Я не хочу идти в банк."
]
model_name = "pytorch-models/opus-mt-tc-big-zle-it"
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)
translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True))
for t in translated:
print( tokenizer.decode(t, skip_special_tokens=True) )
# expected output:
# Non sono idioti.
# Non voglio andare in banca.
```
You can also use OPUS-MT models with the transformers pipelines, for example:
```python
from transformers import pipeline
pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-zle-it")
print(pipe("Вони не ідіоти."))
# expected output: Non sono idioti.
```
## Benchmarks
* test set translations: [opusTCv20210807_transformer-big_2022-03-19.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zle-ita/opusTCv20210807_transformer-big_2022-03-19.test.txt)
* test set scores: [opusTCv20210807_transformer-big_2022-03-19.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zle-ita/opusTCv20210807_transformer-big_2022-03-19.eval.txt)
* benchmark results: [benchmark_results.txt](benchmark_results.txt)
* benchmark output: [benchmark_translations.zip](benchmark_translations.zip)
| langpair | testset | chr-F | BLEU | #sent | #words |
|----------|---------|-------|-------|-------|--------|
| bel-ita | tatoeba-test-v2021-08-07 | 0.65945 | 49.3 | 264 | 1681 |
| rus-ita | tatoeba-test-v2021-08-07 | 0.64037 | 43.5 | 10045 | 71584 |
| ukr-ita | tatoeba-test-v2021-08-07 | 0.69570 | 50.0 | 5000 | 27846 |
| bel-ita | flores101-devtest | 0.46311 | 13.5 | 1012 | 27306 |
| rus-ita | flores101-devtest | 0.53054 | 23.7 | 1012 | 27306 |
| ukr-ita | flores101-devtest | 0.52783 | 23.2 | 1012 | 27306 |
## Acknowledgements
The work is supported by the [European Language Grid](https://www.european-language-grid.eu/) as [pilot project 2866](https://live.european-language-grid.eu/catalogue/#/resource/projects/2866), by the [FoTran project](https://www.helsinki.fi/en/researchgroups/natural-language-understanding-with-cross-lingual-grounding), funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 771113), and the [MeMAD project](https://memad.eu/), funded by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 780069. We are also grateful for the generous computational resources and IT infrastructure provided by [CSC -- IT Center for Science](https://www.csc.fi/), Finland.
## Model conversion info
* transformers version: 4.16.2
* OPUS-MT git hash: 1bdabf7
* port time: Wed Mar 23 23:17:47 EET 2022
* port machine: LM0-400-22516.local
|
ulrica/vicuna7B_es
|
ulrica
| 2023-10-10T11:38:05Z | 3 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-10-10T08:06:00Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
Helsinki-NLP/opus-mt-tc-big-zle-zls
|
Helsinki-NLP
| 2023-10-10T11:34:02Z | 121 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"tc",
"big",
"zle",
"zls",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2022-03-24T12:10:45Z |
---
language:
- be
- bg
- hr
- ru
- sh
- sl
- sr_Cyrl
- sr_Latn
- uk
- zle
- zls
tags:
- translation
- opus-mt-tc
license: cc-by-4.0
model-index:
- name: opus-mt-tc-big-zle-zls
results:
- task:
name: Translation rus-bul
type: translation
args: rus-bul
dataset:
name: flores101-devtest
type: flores_101
args: rus bul devtest
metrics:
- name: BLEU
type: bleu
value: 28.9
- task:
name: Translation rus-hrv
type: translation
args: rus-hrv
dataset:
name: flores101-devtest
type: flores_101
args: rus hrv devtest
metrics:
- name: BLEU
type: bleu
value: 23.2
- task:
name: Translation rus-mkd
type: translation
args: rus-mkd
dataset:
name: flores101-devtest
type: flores_101
args: rus mkd devtest
metrics:
- name: BLEU
type: bleu
value: 24.3
- task:
name: Translation rus-slv
type: translation
args: rus-slv
dataset:
name: flores101-devtest
type: flores_101
args: rus slv devtest
metrics:
- name: BLEU
type: bleu
value: 23.1
- task:
name: Translation rus-srp_Cyrl
type: translation
args: rus-srp_Cyrl
dataset:
name: flores101-devtest
type: flores_101
args: rus srp_Cyrl devtest
metrics:
- name: BLEU
type: bleu
value: 24.1
- task:
name: Translation ukr-bul
type: translation
args: ukr-bul
dataset:
name: flores101-devtest
type: flores_101
args: ukr bul devtest
metrics:
- name: BLEU
type: bleu
value: 30.8
- task:
name: Translation ukr-hrv
type: translation
args: ukr-hrv
dataset:
name: flores101-devtest
type: flores_101
args: ukr hrv devtest
metrics:
- name: BLEU
type: bleu
value: 24.6
- task:
name: Translation ukr-mkd
type: translation
args: ukr-mkd
dataset:
name: flores101-devtest
type: flores_101
args: ukr mkd devtest
metrics:
- name: BLEU
type: bleu
value: 26.2
- task:
name: Translation ukr-slv
type: translation
args: ukr-slv
dataset:
name: flores101-devtest
type: flores_101
args: ukr slv devtest
metrics:
- name: BLEU
type: bleu
value: 24.2
- task:
name: Translation ukr-srp_Cyrl
type: translation
args: ukr-srp_Cyrl
dataset:
name: flores101-devtest
type: flores_101
args: ukr srp_Cyrl devtest
metrics:
- name: BLEU
type: bleu
value: 26.2
- task:
name: Translation rus-bul
type: translation
args: rus-bul
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: rus-bul
metrics:
- name: BLEU
type: bleu
value: 53.7
- task:
name: Translation rus-hbs
type: translation
args: rus-hbs
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: rus-hbs
metrics:
- name: BLEU
type: bleu
value: 49.4
- task:
name: Translation rus-slv
type: translation
args: rus-slv
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: rus-slv
metrics:
- name: BLEU
type: bleu
value: 21.5
- task:
name: Translation rus-srp_Cyrl
type: translation
args: rus-srp_Cyrl
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: rus-srp_Cyrl
metrics:
- name: BLEU
type: bleu
value: 46.1
- task:
name: Translation rus-srp_Latn
type: translation
args: rus-srp_Latn
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: rus-srp_Latn
metrics:
- name: BLEU
type: bleu
value: 51.7
- task:
name: Translation ukr-bul
type: translation
args: ukr-bul
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: ukr-bul
metrics:
- name: BLEU
type: bleu
value: 61.3
- task:
name: Translation ukr-hbs
type: translation
args: ukr-hbs
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: ukr-hbs
metrics:
- name: BLEU
type: bleu
value: 52.1
- task:
name: Translation ukr-hrv
type: translation
args: ukr-hrv
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: ukr-hrv
metrics:
- name: BLEU
type: bleu
value: 50.1
- task:
name: Translation ukr-srp_Cyrl
type: translation
args: ukr-srp_Cyrl
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: ukr-srp_Cyrl
metrics:
- name: BLEU
type: bleu
value: 54.7
- task:
name: Translation ukr-srp_Latn
type: translation
args: ukr-srp_Latn
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: ukr-srp_Latn
metrics:
- name: BLEU
type: bleu
value: 53.4
---
# opus-mt-tc-big-zle-zls
Neural machine translation model for translating from East Slavic languages (zle) to South Slavic languages (zls).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trained using the amazing framework of [Marian NMT](https://marian-nmt.github.io/), an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is taken from [OPUS](https://opus.nlpl.eu/) and training pipelines use the procedures of [OPUS-MT-train](https://github.com/Helsinki-NLP/Opus-MT-train).
* Publications: [OPUS-MT – Building open translation services for the World](https://aclanthology.org/2020.eamt-1.61/) and [The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt-1.139/) (Please, cite if you use this model.)
```
@inproceedings{tiedemann-thottingal-2020-opus,
title = "{OPUS}-{MT} {--} Building open translation services for the World",
author = {Tiedemann, J{\"o}rg and Thottingal, Santhosh},
booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
month = nov,
year = "2020",
address = "Lisboa, Portugal",
publisher = "European Association for Machine Translation",
url = "https://aclanthology.org/2020.eamt-1.61",
pages = "479--480",
}
@inproceedings{tiedemann-2020-tatoeba,
title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}",
author = {Tiedemann, J{\"o}rg},
booktitle = "Proceedings of the Fifth Conference on Machine Translation",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.wmt-1.139",
pages = "1174--1182",
}
```
## Model info
* Release: 2022-03-23
* source language(s): bel rus ukr
* target language(s): bul hbs hrv slv srp_Cyrl srp_Latn
* valid target language labels: >>bul<< >>hbs<< >>hrv<< >>slv<< >>srp_Cyrl<< >>srp_Latn<<
* model: transformer-big
* data: opusTCv20210807+bt ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge))
* tokenization: SentencePiece (spm32k,spm32k)
* original model: [opusTCv20210807+bt_transformer-big_2022-03-23.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/zle-zls/opusTCv20210807+bt_transformer-big_2022-03-23.zip)
* more information released models: [OPUS-MT zle-zls README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/zle-zls/README.md)
* more information about the model: [MarianMT](https://huggingface.co/docs/transformers/model_doc/marian)
This is a multilingual translation model with multiple target languages. A sentence initial language token is required in the form of `>>id<<` (id = valid target language ID), e.g. `>>bul<<`
## Usage
A short example code:
```python
from transformers import MarianMTModel, MarianTokenizer
src_text = [
">>bul<< Новы каранавірус вельмі заразны.",
">>srp_Latn<< Моє ім'я — Саллі."
]
model_name = "pytorch-models/opus-mt-tc-big-zle-zls"
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)
translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True))
for t in translated:
print( tokenizer.decode(t, skip_special_tokens=True) )
# expected output:
# Короната е силно заразна.
# Zovem se Sali.
```
You can also use OPUS-MT models with the transformers pipelines, for example:
```python
from transformers import pipeline
pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-zle-zls")
print(pipe(">>bul<< Новы каранавірус вельмі заразны."))
# expected output: Короната е силно заразна.
```
## Benchmarks
* test set translations: [opusTCv20210807+bt_transformer-big_2022-03-23.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zle-zls/opusTCv20210807+bt_transformer-big_2022-03-23.test.txt)
* test set scores: [opusTCv20210807+bt_transformer-big_2022-03-23.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zle-zls/opusTCv20210807+bt_transformer-big_2022-03-23.eval.txt)
* benchmark results: [benchmark_results.txt](benchmark_results.txt)
* benchmark output: [benchmark_translations.zip](benchmark_translations.zip)
| langpair | testset | chr-F | BLEU | #sent | #words |
|----------|---------|-------|-------|-------|--------|
| rus-bul | tatoeba-test-v2021-08-07 | 0.71515 | 53.7 | 1247 | 8272 |
| rus-hbs | tatoeba-test-v2021-08-07 | 0.69192 | 49.4 | 2500 | 14736 |
| rus-slv | tatoeba-test-v2021-08-07 | 0.38051 | 21.5 | 657 | 3969 |
| rus-srp_Cyrl | tatoeba-test-v2021-08-07 | 0.66622 | 46.1 | 881 | 5407 |
| rus-srp_Latn | tatoeba-test-v2021-08-07 | 0.70990 | 51.7 | 1483 | 8552 |
| ukr-bul | tatoeba-test-v2021-08-07 | 0.77283 | 61.3 | 1020 | 5181 |
| ukr-hbs | tatoeba-test-v2021-08-07 | 0.69401 | 52.1 | 942 | 5130 |
| ukr-hrv | tatoeba-test-v2021-08-07 | 0.67202 | 50.1 | 389 | 2302 |
| ukr-srp_Cyrl | tatoeba-test-v2021-08-07 | 0.70064 | 54.7 | 205 | 1112 |
| ukr-srp_Latn | tatoeba-test-v2021-08-07 | 0.72405 | 53.4 | 348 | 1716 |
| bel-bul | flores101-devtest | 0.49528 | 16.1 | 1012 | 24700 |
| bel-hrv | flores101-devtest | 0.46308 | 12.4 | 1012 | 22423 |
| bel-mkd | flores101-devtest | 0.48608 | 13.5 | 1012 | 24314 |
| bel-slv | flores101-devtest | 0.44452 | 12.2 | 1012 | 23425 |
| bel-srp_Cyrl | flores101-devtest | 0.44424 | 12.6 | 1012 | 23456 |
| rus-bul | flores101-devtest | 0.58653 | 28.9 | 1012 | 24700 |
| rus-hrv | flores101-devtest | 0.53494 | 23.2 | 1012 | 22423 |
| rus-mkd | flores101-devtest | 0.55184 | 24.3 | 1012 | 24314 |
| rus-slv | flores101-devtest | 0.52201 | 23.1 | 1012 | 23425 |
| rus-srp_Cyrl | flores101-devtest | 0.53038 | 24.1 | 1012 | 23456 |
| ukr-bul | flores101-devtest | 0.59625 | 30.8 | 1012 | 24700 |
| ukr-hrv | flores101-devtest | 0.54530 | 24.6 | 1012 | 22423 |
| ukr-mkd | flores101-devtest | 0.56822 | 26.2 | 1012 | 24314 |
| ukr-slv | flores101-devtest | 0.53092 | 24.2 | 1012 | 23425 |
| ukr-srp_Cyrl | flores101-devtest | 0.54618 | 26.2 | 1012 | 23456 |
## Acknowledgements
The work is supported by the [European Language Grid](https://www.european-language-grid.eu/) as [pilot project 2866](https://live.european-language-grid.eu/catalogue/#/resource/projects/2866), by the [FoTran project](https://www.helsinki.fi/en/researchgroups/natural-language-understanding-with-cross-lingual-grounding), funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 771113), and the [MeMAD project](https://memad.eu/), funded by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 780069. We are also grateful for the generous computational resources and IT infrastructure provided by [CSC -- IT Center for Science](https://www.csc.fi/), Finland.
## Model conversion info
* transformers version: 4.16.2
* OPUS-MT git hash: 1bdabf7
* port time: Thu Mar 24 00:46:26 EET 2022
* port machine: LM0-400-22516.local
|
Helsinki-NLP/opus-mt-tc-big-itc-eu
|
Helsinki-NLP
| 2023-10-10T11:33:00Z | 113 | 2 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"es",
"eu",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2022-08-12T16:30:35Z |
---
language:
- es
- eu
tags:
- translation
- opus-mt-tc
license: cc-by-4.0
model-index:
- name: opus-mt-tc-big-itc-eu
results:
- task:
name: Translation spa-eus
type: translation
args: spa-eus
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: spa-eus
metrics:
- name: BLEU
type: bleu
value: 32.4
- name: chr-F
type: chrf
value: 0.60699
---
# opus-mt-tc-big-itc-eu
## Table of Contents
- [Model Details](#model-details)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [How to Get Started With the Model](#how-to-get-started-with-the-model)
- [Training](#training)
- [Evaluation](#evaluation)
- [Citation Information](#citation-information)
- [Acknowledgements](#acknowledgements)
## Model Details
Neural machine translation model for translating from Italic languages (itc) to Basque (eu).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trained using the amazing framework of [Marian NMT](https://marian-nmt.github.io/), an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is taken from [OPUS](https://opus.nlpl.eu/) and training pipelines use the procedures of [OPUS-MT-train](https://github.com/Helsinki-NLP/Opus-MT-train).
**Model Description:**
- **Developed by:** Language Technology Research Group at the University of Helsinki
- **Model Type:** Translation (transformer-big)
- **Release**: 2022-07-23
- **License:** CC-BY-4.0
- **Language(s):**
- Source Language(s): fra ita spa
- Target Language(s): eus
- Language Pair(s): spa-eus
- Valid Target Language Labels:
- **Original Model**: [opusTCv20210807_transformer-big_2022-07-23.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/itc-eus/opusTCv20210807_transformer-big_2022-07-23.zip)
- **Resources for more information:**
- [OPUS-MT-train GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train)
- More information about released models for this language pair: [OPUS-MT itc-eus README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/itc-eus/README.md)
- [More information about MarianNMT models in the transformers library](https://huggingface.co/docs/transformers/model_doc/marian)
- [Tatoeba Translation Challenge](https://github.com/Helsinki-NLP/Tatoeba-Challenge/
## Uses
This model can be used for translation and text-to-text generation.
## Risks, Limitations and Biases
**CONTENT WARNING: Readers should be aware that the model is trained on various public data sets that may contain content that is disturbing, offensive, and can propagate historical and current stereotypes.**
Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)).
## How to Get Started With the Model
A short example code:
```python
from transformers import MarianMTModel, MarianTokenizer
src_text = [
"Il est riche.",
"¿Correcto?"
]
model_name = "pytorch-models/opus-mt-tc-big-itc-eu"
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)
translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True))
for t in translated:
print( tokenizer.decode(t, skip_special_tokens=True) )
# expected output:
# Aberatsa da.
# Zuzena?
```
You can also use OPUS-MT models with the transformers pipelines, for example:
```python
from transformers import pipeline
pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-itc-eu")
print(pipe("Il est riche."))
# expected output: Aberatsa da.
```
## Training
- **Data**: opusTCv20210807 ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge))
- **Pre-processing**: SentencePiece (spm32k,spm32k)
- **Model Type:** transformer-big
- **Original MarianNMT Model**: [opusTCv20210807_transformer-big_2022-07-23.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/itc-eus/opusTCv20210807_transformer-big_2022-07-23.zip)
- **Training Scripts**: [GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train)
## Evaluation
* test set translations: [opusTCv20210807_transformer-big_2022-07-23.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/itc-eus/opusTCv20210807_transformer-big_2022-07-23.test.txt)
* test set scores: [opusTCv20210807_transformer-big_2022-07-23.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/itc-eus/opusTCv20210807_transformer-big_2022-07-23.eval.txt)
* benchmark results: [benchmark_results.txt](benchmark_results.txt)
* benchmark output: [benchmark_translations.zip](benchmark_translations.zip)
| langpair | testset | chr-F | BLEU | #sent | #words |
|----------|---------|-------|-------|-------|--------|
| spa-eus | tatoeba-test-v2021-08-07 | 0.60699 | 32.4 | 1850 | 10945 |
## Citation Information
* Publications: [OPUS-MT – Building open translation services for the World](https://aclanthology.org/2020.eamt-1.61/) and [The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt-1.139/) (Please, cite if you use this model.)
```
@inproceedings{tiedemann-thottingal-2020-opus,
title = "{OPUS}-{MT} {--} Building open translation services for the World",
author = {Tiedemann, J{\"o}rg and Thottingal, Santhosh},
booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
month = nov,
year = "2020",
address = "Lisboa, Portugal",
publisher = "European Association for Machine Translation",
url = "https://aclanthology.org/2020.eamt-1.61",
pages = "479--480",
}
@inproceedings{tiedemann-2020-tatoeba,
title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}",
author = {Tiedemann, J{\"o}rg},
booktitle = "Proceedings of the Fifth Conference on Machine Translation",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.wmt-1.139",
pages = "1174--1182",
}
```
## Acknowledgements
The work is supported by the [European Language Grid](https://www.european-language-grid.eu/) as [pilot project 2866](https://live.european-language-grid.eu/catalogue/#/resource/projects/2866), by the [FoTran project](https://www.helsinki.fi/en/researchgroups/natural-language-understanding-with-cross-lingual-grounding), funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 771113), and the [MeMAD project](https://memad.eu/), funded by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 780069. We are also grateful for the generous computational resources and IT infrastructure provided by [CSC -- IT Center for Science](https://www.csc.fi/), Finland.
## Model conversion info
* transformers version: 4.16.2
* OPUS-MT git hash: 8b9f0b0
* port time: Sat Aug 13 00:08:07 EEST 2022
* port machine: LM0-400-22516.local
|
Helsinki-NLP/opus-mt-tc-big-itc-ar
|
Helsinki-NLP
| 2023-10-10T11:31:55Z | 110 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"ar",
"ca",
"es",
"fr",
"gl",
"it",
"pt",
"ro",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2022-08-12T11:32:09Z |
---
language:
- ar
- ca
- es
- fr
- gl
- it
- pt
- ro
tags:
- translation
- opus-mt-tc
license: cc-by-4.0
model-index:
- name: opus-mt-tc-big-itc-ar
results:
- task:
name: Translation cat-ara
type: translation
args: cat-ara
dataset:
name: flores101-devtest
type: flores_101
args: cat ara devtest
metrics:
- name: BLEU
type: bleu
value: 18.9
- name: chr-F
type: chrf
value: 0.52029
- task:
name: Translation fra-ara
type: translation
args: fra-ara
dataset:
name: flores101-devtest
type: flores_101
args: fra ara devtest
metrics:
- name: BLEU
type: bleu
value: 19.5
- name: chr-F
type: chrf
value: 0.52573
- task:
name: Translation glg-ara
type: translation
args: glg-ara
dataset:
name: flores101-devtest
type: flores_101
args: glg ara devtest
metrics:
- name: BLEU
type: bleu
value: 19.2
- name: chr-F
type: chrf
value: 0.51181
- task:
name: Translation ita-ara
type: translation
args: ita-ara
dataset:
name: flores101-devtest
type: flores_101
args: ita ara devtest
metrics:
- name: BLEU
type: bleu
value: 15.0
- name: chr-F
type: chrf
value: 0.49401
- task:
name: Translation por-ara
type: translation
args: por-ara
dataset:
name: flores101-devtest
type: flores_101
args: por ara devtest
metrics:
- name: BLEU
type: bleu
value: 20.2
- name: chr-F
type: chrf
value: 0.53356
- task:
name: Translation ron-ara
type: translation
args: ron-ara
dataset:
name: flores101-devtest
type: flores_101
args: ron ara devtest
metrics:
- name: BLEU
type: bleu
value: 18.4
- name: chr-F
type: chrf
value: 0.51849
- task:
name: Translation spa-ara
type: translation
args: spa-ara
dataset:
name: flores101-devtest
type: flores_101
args: spa ara devtest
metrics:
- name: BLEU
type: bleu
value: 14.3
- name: chr-F
type: chrf
value: 0.47872
- task:
name: Translation ita-ara
type: translation
args: ita-ara
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: ita-ara
metrics:
- name: BLEU
type: bleu
value: 25.7
- name: chr-F
type: chrf
value: 0.53797
- task:
name: Translation spa-ara
type: translation
args: spa-ara
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: spa-ara
metrics:
- name: BLEU
type: bleu
value: 26.6
- name: chr-F
type: chrf
value: 0.55520
---
# opus-mt-tc-big-itc-ar
## Table of Contents
- [Model Details](#model-details)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [How to Get Started With the Model](#how-to-get-started-with-the-model)
- [Training](#training)
- [Evaluation](#evaluation)
- [Citation Information](#citation-information)
- [Acknowledgements](#acknowledgements)
## Model Details
Neural machine translation model for translating from Italic languages (itc) to Arabic (ar).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trained using the amazing framework of [Marian NMT](https://marian-nmt.github.io/), an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is taken from [OPUS](https://opus.nlpl.eu/) and training pipelines use the procedures of [OPUS-MT-train](https://github.com/Helsinki-NLP/Opus-MT-train).
**Model Description:**
- **Developed by:** Language Technology Research Group at the University of Helsinki
- **Model Type:** Translation (transformer-big)
- **Release**: 2022-08-09
- **License:** CC-BY-4.0
- **Language(s):**
- Source Language(s): cat fra glg ita lat_Latn por ron spa
- Target Language(s): ara
- Language Pair(s): cat-ara fra-ara glg-ara ita-ara por-ara ron-ara spa-ara
- Valid Target Language Labels: >>ajp<< >>apc<< >>ara<< >>arq<< >>ary<< >>arz<<
- **Original Model**: [opusTCv20210807_transformer-big_2022-08-09.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/itc-ara/opusTCv20210807_transformer-big_2022-08-09.zip)
- **Resources for more information:**
- [OPUS-MT-train GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train)
- More information about released models for this language pair: [OPUS-MT itc-ara README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/itc-ara/README.md)
- [More information about MarianNMT models in the transformers library](https://huggingface.co/docs/transformers/model_doc/marian)
- [Tatoeba Translation Challenge](https://github.com/Helsinki-NLP/Tatoeba-Challenge/
This is a multilingual translation model with multiple target languages. A sentence initial language token is required in the form of `>>id<<` (id = valid target language ID), e.g. `>>ara<<`
## Uses
This model can be used for translation and text-to-text generation.
## Risks, Limitations and Biases
**CONTENT WARNING: Readers should be aware that the model is trained on various public data sets that may contain content that is disturbing, offensive, and can propagate historical and current stereotypes.**
Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)).
## How to Get Started With the Model
A short example code:
```python
from transformers import MarianMTModel, MarianTokenizer
src_text = [
">>ary<< Entiendo.",
">>arq<< Por favor entiende mi posición."
]
model_name = "pytorch-models/opus-mt-tc-big-itc-ar"
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)
translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True))
for t in translated:
print( tokenizer.decode(t, skip_special_tokens=True) )
# expected output:
# فهمتك
# من فضلك افهم موقفي.
```
You can also use OPUS-MT models with the transformers pipelines, for example:
```python
from transformers import pipeline
pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-itc-ar")
print(pipe(">>ary<< Entiendo."))
# expected output: فهمتك
```
## Training
- **Data**: opusTCv20210807 ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge))
- **Pre-processing**: SentencePiece (spm32k,spm32k)
- **Model Type:** transformer-big
- **Original MarianNMT Model**: [opusTCv20210807_transformer-big_2022-08-09.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/itc-ara/opusTCv20210807_transformer-big_2022-08-09.zip)
- **Training Scripts**: [GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train)
## Evaluation
* test set translations: [opusTCv20210807_transformer-big_2022-08-09.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/itc-ara/opusTCv20210807_transformer-big_2022-08-09.test.txt)
* test set scores: [opusTCv20210807_transformer-big_2022-08-09.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/itc-ara/opusTCv20210807_transformer-big_2022-08-09.eval.txt)
* benchmark results: [benchmark_results.txt](benchmark_results.txt)
* benchmark output: [benchmark_translations.zip](benchmark_translations.zip)
| langpair | testset | chr-F | BLEU | #sent | #words |
|----------|---------|-------|-------|-------|--------|
| fra-ara | tatoeba-test-v2021-08-07 | 0.46463 | 18.9 | 1569 | 7956 |
| ita-ara | tatoeba-test-v2021-08-07 | 0.53797 | 25.7 | 235 | 1161 |
| spa-ara | tatoeba-test-v2021-08-07 | 0.55520 | 26.6 | 1511 | 7547 |
| cat-ara | flores101-devtest | 0.52029 | 18.9 | 1012 | 21357 |
| fra-ara | flores101-devtest | 0.52573 | 19.5 | 1012 | 21357 |
| glg-ara | flores101-devtest | 0.51181 | 19.2 | 1012 | 21357 |
| ita-ara | flores101-devtest | 0.49401 | 15.0 | 1012 | 21357 |
| por-ara | flores101-devtest | 0.53356 | 20.2 | 1012 | 21357 |
| ron-ara | flores101-devtest | 0.51849 | 18.4 | 1012 | 21357 |
| spa-ara | flores101-devtest | 0.47872 | 14.3 | 1012 | 21357 |
## Citation Information
* Publications: [OPUS-MT – Building open translation services for the World](https://aclanthology.org/2020.eamt-1.61/) and [The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt-1.139/) (Please, cite if you use this model.)
```
@inproceedings{tiedemann-thottingal-2020-opus,
title = "{OPUS}-{MT} {--} Building open translation services for the World",
author = {Tiedemann, J{\"o}rg and Thottingal, Santhosh},
booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
month = nov,
year = "2020",
address = "Lisboa, Portugal",
publisher = "European Association for Machine Translation",
url = "https://aclanthology.org/2020.eamt-1.61",
pages = "479--480",
}
@inproceedings{tiedemann-2020-tatoeba,
title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}",
author = {Tiedemann, J{\"o}rg},
booktitle = "Proceedings of the Fifth Conference on Machine Translation",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.wmt-1.139",
pages = "1174--1182",
}
```
## Acknowledgements
The work is supported by the [European Language Grid](https://www.european-language-grid.eu/) as [pilot project 2866](https://live.european-language-grid.eu/catalogue/#/resource/projects/2866), by the [FoTran project](https://www.helsinki.fi/en/researchgroups/natural-language-understanding-with-cross-lingual-grounding), funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 771113), and the [MeMAD project](https://memad.eu/), funded by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 780069. We are also grateful for the generous computational resources and IT infrastructure provided by [CSC -- IT Center for Science](https://www.csc.fi/), Finland.
## Model conversion info
* transformers version: 4.16.2
* OPUS-MT git hash: 8b9f0b0
* port time: Sat Aug 13 00:00:31 EEST 2022
* port machine: LM0-400-22516.local
|
Helsinki-NLP/opus-mt-tc-big-zls-itc
|
Helsinki-NLP
| 2023-10-10T11:27:42Z | 117 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"bg",
"es",
"fr",
"hr",
"it",
"mk",
"pt",
"ro",
"sh",
"sl",
"sr",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2022-08-12T10:57:54Z |
---
language:
- bg
- es
- fr
- hr
- it
- mk
- pt
- ro
- sh
- sl
- sr
language_bcp47:
- sr_Cyrl
- sr_Latn
tags:
- translation
- opus-mt-tc
license: cc-by-4.0
model-index:
- name: opus-mt-tc-big-zls-itc
results:
- task:
name: Translation bul-fra
type: translation
args: bul-fra
dataset:
name: flores101-devtest
type: flores_101
args: bul fra devtest
metrics:
- name: BLEU
type: bleu
value: 34.4
- name: chr-F
type: chrf
value: 0.60640
- task:
name: Translation bul-ita
type: translation
args: bul-ita
dataset:
name: flores101-devtest
type: flores_101
args: bul ita devtest
metrics:
- name: BLEU
type: bleu
value: 24.0
- name: chr-F
type: chrf
value: 0.54135
- task:
name: Translation bul-por
type: translation
args: bul-por
dataset:
name: flores101-devtest
type: flores_101
args: bul por devtest
metrics:
- name: BLEU
type: bleu
value: 32.4
- name: chr-F
type: chrf
value: 0.59322
- task:
name: Translation bul-ron
type: translation
args: bul-ron
dataset:
name: flores101-devtest
type: flores_101
args: bul ron devtest
metrics:
- name: BLEU
type: bleu
value: 27.1
- name: chr-F
type: chrf
value: 0.55558
- task:
name: Translation bul-spa
type: translation
args: bul-spa
dataset:
name: flores101-devtest
type: flores_101
args: bul spa devtest
metrics:
- name: BLEU
type: bleu
value: 22.4
- name: chr-F
type: chrf
value: 0.50962
- task:
name: Translation hrv-fra
type: translation
args: hrv-fra
dataset:
name: flores101-devtest
type: flores_101
args: hrv fra devtest
metrics:
- name: BLEU
type: bleu
value: 33.1
- name: chr-F
type: chrf
value: 0.59349
- task:
name: Translation hrv-ita
type: translation
args: hrv-ita
dataset:
name: flores101-devtest
type: flores_101
args: hrv ita devtest
metrics:
- name: BLEU
type: bleu
value: 23.5
- name: chr-F
type: chrf
value: 0.52980
- task:
name: Translation hrv-por
type: translation
args: hrv-por
dataset:
name: flores101-devtest
type: flores_101
args: hrv por devtest
metrics:
- name: BLEU
type: bleu
value: 30.2
- name: chr-F
type: chrf
value: 0.57402
- task:
name: Translation hrv-ron
type: translation
args: hrv-ron
dataset:
name: flores101-devtest
type: flores_101
args: hrv ron devtest
metrics:
- name: BLEU
type: bleu
value: 25.9
- name: chr-F
type: chrf
value: 0.53650
- task:
name: Translation hrv-spa
type: translation
args: hrv-spa
dataset:
name: flores101-devtest
type: flores_101
args: hrv spa devtest
metrics:
- name: BLEU
type: bleu
value: 21.5
- name: chr-F
type: chrf
value: 0.50161
- task:
name: Translation mkd-fra
type: translation
args: mkd-fra
dataset:
name: flores101-devtest
type: flores_101
args: mkd fra devtest
metrics:
- name: BLEU
type: bleu
value: 35.2
- name: chr-F
type: chrf
value: 0.60801
- task:
name: Translation mkd-ita
type: translation
args: mkd-ita
dataset:
name: flores101-devtest
type: flores_101
args: mkd ita devtest
metrics:
- name: BLEU
type: bleu
value: 23.9
- name: chr-F
type: chrf
value: 0.53543
- task:
name: Translation mkd-por
type: translation
args: mkd-por
dataset:
name: flores101-devtest
type: flores_101
args: mkd por devtest
metrics:
- name: BLEU
type: bleu
value: 33.9
- name: chr-F
type: chrf
value: 0.59648
- task:
name: Translation mkd-ron
type: translation
args: mkd-ron
dataset:
name: flores101-devtest
type: flores_101
args: mkd ron devtest
metrics:
- name: BLEU
type: bleu
value: 28.0
- name: chr-F
type: chrf
value: 0.54998
- task:
name: Translation mkd-spa
type: translation
args: mkd-spa
dataset:
name: flores101-devtest
type: flores_101
args: mkd spa devtest
metrics:
- name: BLEU
type: bleu
value: 22.8
- name: chr-F
type: chrf
value: 0.51079
- task:
name: Translation slv-fra
type: translation
args: slv-fra
dataset:
name: flores101-devtest
type: flores_101
args: slv fra devtest
metrics:
- name: BLEU
type: bleu
value: 31.5
- name: chr-F
type: chrf
value: 0.58233
- task:
name: Translation slv-ita
type: translation
args: slv-ita
dataset:
name: flores101-devtest
type: flores_101
args: slv ita devtest
metrics:
- name: BLEU
type: bleu
value: 22.4
- name: chr-F
type: chrf
value: 0.52390
- task:
name: Translation slv-por
type: translation
args: slv-por
dataset:
name: flores101-devtest
type: flores_101
args: slv por devtest
metrics:
- name: BLEU
type: bleu
value: 29.0
- name: chr-F
type: chrf
value: 0.56436
- task:
name: Translation slv-ron
type: translation
args: slv-ron
dataset:
name: flores101-devtest
type: flores_101
args: slv ron devtest
metrics:
- name: BLEU
type: bleu
value: 25.0
- name: chr-F
type: chrf
value: 0.53116
- task:
name: Translation slv-spa
type: translation
args: slv-spa
dataset:
name: flores101-devtest
type: flores_101
args: slv spa devtest
metrics:
- name: BLEU
type: bleu
value: 21.1
- name: chr-F
type: chrf
value: 0.49621
- task:
name: Translation srp_Cyrl-fra
type: translation
args: srp_Cyrl-fra
dataset:
name: flores101-devtest
type: flores_101
args: srp_Cyrl fra devtest
metrics:
- name: BLEU
type: bleu
value: 36.0
- name: chr-F
type: chrf
value: 0.62110
- task:
name: Translation srp_Cyrl-ita
type: translation
args: srp_Cyrl-ita
dataset:
name: flores101-devtest
type: flores_101
args: srp_Cyrl ita devtest
metrics:
- name: BLEU
type: bleu
value: 23.9
- name: chr-F
type: chrf
value: 0.54083
- task:
name: Translation srp_Cyrl-por
type: translation
args: srp_Cyrl-por
dataset:
name: flores101-devtest
type: flores_101
args: srp_Cyrl por devtest
metrics:
- name: BLEU
type: bleu
value: 34.9
- name: chr-F
type: chrf
value: 0.61248
- task:
name: Translation srp_Cyrl-ron
type: translation
args: srp_Cyrl-ron
dataset:
name: flores101-devtest
type: flores_101
args: srp_Cyrl ron devtest
metrics:
- name: BLEU
type: bleu
value: 28.8
- name: chr-F
type: chrf
value: 0.56235
- task:
name: Translation srp_Cyrl-spa
type: translation
args: srp_Cyrl-spa
dataset:
name: flores101-devtest
type: flores_101
args: srp_Cyrl spa devtest
metrics:
- name: BLEU
type: bleu
value: 22.8
- name: chr-F
type: chrf
value: 0.51698
- task:
name: Translation bul-fra
type: translation
args: bul-fra
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: bul-fra
metrics:
- name: BLEU
type: bleu
value: 52.9
- name: chr-F
type: chrf
value: 0.68971
- task:
name: Translation bul-ita
type: translation
args: bul-ita
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: bul-ita
metrics:
- name: BLEU
type: bleu
value: 45.1
- name: chr-F
type: chrf
value: 0.66412
- task:
name: Translation bul-spa
type: translation
args: bul-spa
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: bul-spa
metrics:
- name: BLEU
type: bleu
value: 49.7
- name: chr-F
type: chrf
value: 0.66672
- task:
name: Translation hbs-fra
type: translation
args: hbs-fra
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: hbs-fra
metrics:
- name: BLEU
type: bleu
value: 48.1
- name: chr-F
type: chrf
value: 0.66434
- task:
name: Translation hbs-ita
type: translation
args: hbs-ita
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: hbs-ita
metrics:
- name: BLEU
type: bleu
value: 53.5
- name: chr-F
type: chrf
value: 0.72381
- task:
name: Translation hbs-spa
type: translation
args: hbs-spa
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: hbs-spa
metrics:
- name: BLEU
type: bleu
value: 58.0
- name: chr-F
type: chrf
value: 0.73105
- task:
name: Translation hrv-fra
type: translation
args: hrv-fra
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: hrv-fra
metrics:
- name: BLEU
type: bleu
value: 44.3
- name: chr-F
type: chrf
value: 0.62800
- task:
name: Translation hrv-spa
type: translation
args: hrv-spa
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: hrv-spa
metrics:
- name: BLEU
type: bleu
value: 57.5
- name: chr-F
type: chrf
value: 0.71370
- task:
name: Translation mkd-spa
type: translation
args: mkd-spa
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: mkd-spa
metrics:
- name: BLEU
type: bleu
value: 62.1
- name: chr-F
type: chrf
value: 0.75366
- task:
name: Translation srp_Latn-ita
type: translation
args: srp_Latn-ita
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: srp_Latn-ita
metrics:
- name: BLEU
type: bleu
value: 59.6
- name: chr-F
type: chrf
value: 0.76045
---
# opus-mt-tc-big-zls-itc
## Table of Contents
- [Model Details](#model-details)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [How to Get Started With the Model](#how-to-get-started-with-the-model)
- [Training](#training)
- [Evaluation](#evaluation)
- [Citation Information](#citation-information)
- [Acknowledgements](#acknowledgements)
## Model Details
Neural machine translation model for translating from South Slavic languages (zls) to Italic languages (itc).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trained using the amazing framework of [Marian NMT](https://marian-nmt.github.io/), an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is taken from [OPUS](https://opus.nlpl.eu/) and training pipelines use the procedures of [OPUS-MT-train](https://github.com/Helsinki-NLP/Opus-MT-train).
**Model Description:**
- **Developed by:** Language Technology Research Group at the University of Helsinki
- **Model Type:** Translation (transformer-big)
- **Release**: 2022-08-10
- **License:** CC-BY-4.0
- **Language(s):**
- Source Language(s): bos_Latn bul hbs hrv mkd slv srp_Cyrl srp_Latn
- Target Language(s): fra ita por ron spa
- Language Pair(s): bul-fra bul-ita bul-por bul-ron bul-spa hbs-fra hbs-ita hbs-spa hrv-fra hrv-ita hrv-por hrv-ron hrv-spa mkd-fra mkd-ita mkd-por mkd-ron mkd-spa slv-fra slv-ita slv-por slv-ron slv-spa srp_Cyrl-fra srp_Cyrl-ita srp_Cyrl-por srp_Cyrl-ron srp_Cyrl-spa srp_Latn-ita
- Valid Target Language Labels: >>acf<< >>aoa<< >>arg<< >>ast<< >>cat<< >>cbk<< >>ccd<< >>cks<< >>cos<< >>cri<< >>crs<< >>dlm<< >>drc<< >>egl<< >>ext<< >>fab<< >>fax<< >>fra<< >>frc<< >>frm<< >>fro<< >>frp<< >>fur<< >>gcf<< >>gcr<< >>glg<< >>hat<< >>idb<< >>ist<< >>ita<< >>itk<< >>kea<< >>kmv<< >>lad<< >>lad_Latn<< >>lat<< >>lat_Latn<< >>lij<< >>lld<< >>lmo<< >>lou<< >>mcm<< >>mfe<< >>mol<< >>mwl<< >>mxi<< >>mzs<< >>nap<< >>nrf<< >>oci<< >>osc<< >>osp<< >>pap<< >>pcd<< >>pln<< >>pms<< >>pob<< >>por<< >>pov<< >>pre<< >>pro<< >>qbb<< >>qhr<< >>rcf<< >>rgn<< >>roh<< >>ron<< >>ruo<< >>rup<< >>ruq<< >>scf<< >>scn<< >>sdc<< >>sdn<< >>spa<< >>spq<< >>spx<< >>src<< >>srd<< >>sro<< >>tmg<< >>tvy<< >>vec<< >>vkp<< >>wln<< >>xfa<< >>xum<<
- **Original Model**: [opusTCv20210807_transformer-big_2022-08-10.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/zls-itc/opusTCv20210807_transformer-big_2022-08-10.zip)
- **Resources for more information:**
- [OPUS-MT-train GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train)
- More information about released models for this language pair: [OPUS-MT zls-itc README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/zls-itc/README.md)
- [More information about MarianNMT models in the transformers library](https://huggingface.co/docs/transformers/model_doc/marian)
- [Tatoeba Translation Challenge](https://github.com/Helsinki-NLP/Tatoeba-Challenge/
This is a multilingual translation model with multiple target languages. A sentence initial language token is required in the form of `>>id<<` (id = valid target language ID), e.g. `>>fra<<`
## Uses
This model can be used for translation and text-to-text generation.
## Risks, Limitations and Biases
**CONTENT WARNING: Readers should be aware that the model is trained on various public data sets that may contain content that is disturbing, offensive, and can propagate historical and current stereotypes.**
Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)).
## How to Get Started With the Model
A short example code:
```python
from transformers import MarianMTModel, MarianTokenizer
src_text = [
">>fra<< Dobar dan, kako si?",
">>spa<< Znam da je ovo čudno."
]
model_name = "pytorch-models/opus-mt-tc-big-zls-itc"
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)
translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True))
for t in translated:
print( tokenizer.decode(t, skip_special_tokens=True) )
# expected output:
# Bonjour, comment allez-vous ?
# Sé que esto es raro.
```
You can also use OPUS-MT models with the transformers pipelines, for example:
```python
from transformers import pipeline
pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-zls-itc")
print(pipe(">>fra<< Dobar dan, kako si?"))
# expected output: Bonjour, comment allez-vous ?
```
## Training
- **Data**: opusTCv20210807 ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge))
- **Pre-processing**: SentencePiece (spm32k,spm32k)
- **Model Type:** transformer-big
- **Original MarianNMT Model**: [opusTCv20210807_transformer-big_2022-08-10.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/zls-itc/opusTCv20210807_transformer-big_2022-08-10.zip)
- **Training Scripts**: [GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train)
## Evaluation
* test set translations: [opusTCv20210807_transformer-big_2022-08-10.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zls-itc/opusTCv20210807_transformer-big_2022-08-10.test.txt)
* test set scores: [opusTCv20210807_transformer-big_2022-08-10.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zls-itc/opusTCv20210807_transformer-big_2022-08-10.eval.txt)
* benchmark results: [benchmark_results.txt](benchmark_results.txt)
* benchmark output: [benchmark_translations.zip](benchmark_translations.zip)
| langpair | testset | chr-F | BLEU | #sent | #words |
|----------|---------|-------|-------|-------|--------|
| bul-fra | tatoeba-test-v2021-08-07 | 0.68971 | 52.9 | 446 | 3669 |
| bul-ita | tatoeba-test-v2021-08-07 | 0.66412 | 45.1 | 2500 | 16951 |
| bul-spa | tatoeba-test-v2021-08-07 | 0.66672 | 49.7 | 286 | 1783 |
| hbs-fra | tatoeba-test-v2021-08-07 | 0.66434 | 48.1 | 474 | 3370 |
| hbs-ita | tatoeba-test-v2021-08-07 | 0.72381 | 53.5 | 534 | 3208 |
| hbs-spa | tatoeba-test-v2021-08-07 | 0.73105 | 58.0 | 607 | 3766 |
| hrv-fra | tatoeba-test-v2021-08-07 | 0.62800 | 44.3 | 258 | 1943 |
| hrv-spa | tatoeba-test-v2021-08-07 | 0.71370 | 57.5 | 254 | 1702 |
| mkd-spa | tatoeba-test-v2021-08-07 | 0.75366 | 62.1 | 217 | 1121 |
| srp_Latn-ita | tatoeba-test-v2021-08-07 | 0.76045 | 59.6 | 212 | 1292 |
| bul-fra | flores101-devtest | 0.60640 | 34.4 | 1012 | 28343 |
| bul-ita | flores101-devtest | 0.54135 | 24.0 | 1012 | 27306 |
| bul-por | flores101-devtest | 0.59322 | 32.4 | 1012 | 26519 |
| bul-ron | flores101-devtest | 0.55558 | 27.1 | 1012 | 26799 |
| bul-spa | flores101-devtest | 0.50962 | 22.4 | 1012 | 29199 |
| hrv-fra | flores101-devtest | 0.59349 | 33.1 | 1012 | 28343 |
| hrv-ita | flores101-devtest | 0.52980 | 23.5 | 1012 | 27306 |
| hrv-por | flores101-devtest | 0.57402 | 30.2 | 1012 | 26519 |
| hrv-ron | flores101-devtest | 0.53650 | 25.9 | 1012 | 26799 |
| hrv-spa | flores101-devtest | 0.50161 | 21.5 | 1012 | 29199 |
| mkd-fra | flores101-devtest | 0.60801 | 35.2 | 1012 | 28343 |
| mkd-ita | flores101-devtest | 0.53543 | 23.9 | 1012 | 27306 |
| mkd-por | flores101-devtest | 0.59648 | 33.9 | 1012 | 26519 |
| mkd-ron | flores101-devtest | 0.54998 | 28.0 | 1012 | 26799 |
| mkd-spa | flores101-devtest | 0.51079 | 22.8 | 1012 | 29199 |
| slv-fra | flores101-devtest | 0.58233 | 31.5 | 1012 | 28343 |
| slv-ita | flores101-devtest | 0.52390 | 22.4 | 1012 | 27306 |
| slv-por | flores101-devtest | 0.56436 | 29.0 | 1012 | 26519 |
| slv-ron | flores101-devtest | 0.53116 | 25.0 | 1012 | 26799 |
| slv-spa | flores101-devtest | 0.49621 | 21.1 | 1012 | 29199 |
| srp_Cyrl-fra | flores101-devtest | 0.62110 | 36.0 | 1012 | 28343 |
| srp_Cyrl-ita | flores101-devtest | 0.54083 | 23.9 | 1012 | 27306 |
| srp_Cyrl-por | flores101-devtest | 0.61248 | 34.9 | 1012 | 26519 |
| srp_Cyrl-ron | flores101-devtest | 0.56235 | 28.8 | 1012 | 26799 |
| srp_Cyrl-spa | flores101-devtest | 0.51698 | 22.8 | 1012 | 29199 |
## Citation Information
* Publications: [OPUS-MT – Building open translation services for the World](https://aclanthology.org/2020.eamt-1.61/) and [The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt-1.139/) (Please, cite if you use this model.)
```
@inproceedings{tiedemann-thottingal-2020-opus,
title = "{OPUS}-{MT} {--} Building open translation services for the World",
author = {Tiedemann, J{\"o}rg and Thottingal, Santhosh},
booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
month = nov,
year = "2020",
address = "Lisboa, Portugal",
publisher = "European Association for Machine Translation",
url = "https://aclanthology.org/2020.eamt-1.61",
pages = "479--480",
}
@inproceedings{tiedemann-2020-tatoeba,
title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}",
author = {Tiedemann, J{\"o}rg},
booktitle = "Proceedings of the Fifth Conference on Machine Translation",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.wmt-1.139",
pages = "1174--1182",
}
```
## Acknowledgements
The work is supported by the [European Language Grid](https://www.european-language-grid.eu/) as [pilot project 2866](https://live.european-language-grid.eu/catalogue/#/resource/projects/2866), by the [FoTran project](https://www.helsinki.fi/en/researchgroups/natural-language-understanding-with-cross-lingual-grounding), funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 771113), and the [MeMAD project](https://memad.eu/), funded by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 780069. We are also grateful for the generous computational resources and IT infrastructure provided by [CSC -- IT Center for Science](https://www.csc.fi/), Finland.
## Model conversion info
* transformers version: 4.16.2
* OPUS-MT git hash: 8b9f0b0
* port time: Fri Aug 12 23:59:29 EEST 2022
* port machine: LM0-400-22516.local
|
Helsinki-NLP/opus-mt-tc-big-gmq-ar
|
Helsinki-NLP
| 2023-10-10T11:26:33Z | 117 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"ar",
"da",
"sv",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2022-08-12T14:35:33Z |
---
language:
- ar
- da
- sv
tags:
- translation
- opus-mt-tc
license: cc-by-4.0
model-index:
- name: opus-mt-tc-big-gmq-ar
results:
- task:
name: Translation dan-ara
type: translation
args: dan-ara
dataset:
name: flores101-devtest
type: flores_101
args: dan ara devtest
metrics:
- name: BLEU
type: bleu
value: 19.9
- name: chr-F
type: chrf
value: 0.52841
- task:
name: Translation nob-ara
type: translation
args: nob-ara
dataset:
name: flores101-devtest
type: flores_101
args: nob ara devtest
metrics:
- name: BLEU
type: bleu
value: 16.8
- name: chr-F
type: chrf
value: 0.49670
- task:
name: Translation swe-ara
type: translation
args: swe-ara
dataset:
name: flores101-devtest
type: flores_101
args: swe ara devtest
metrics:
- name: BLEU
type: bleu
value: 19.3
- name: chr-F
type: chrf
value: 0.51882
---
# opus-mt-tc-big-gmq-ar
## Table of Contents
- [Model Details](#model-details)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [How to Get Started With the Model](#how-to-get-started-with-the-model)
- [Training](#training)
- [Evaluation](#evaluation)
- [Citation Information](#citation-information)
- [Acknowledgements](#acknowledgements)
## Model Details
Neural machine translation model for translating from North Germanic languages (gmq) to Arabic (ar).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trained using the amazing framework of [Marian NMT](https://marian-nmt.github.io/), an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is taken from [OPUS](https://opus.nlpl.eu/) and training pipelines use the procedures of [OPUS-MT-train](https://github.com/Helsinki-NLP/Opus-MT-train).
**Model Description:**
- **Developed by:** Language Technology Research Group at the University of Helsinki
- **Model Type:** Translation (transformer-big)
- **Release**: 2022-07-27
- **License:** CC-BY-4.0
- **Language(s):**
- Source Language(s): dan swe
- Target Language(s): ara
- Language Pair(s): dan-ara swe-ara
- Valid Target Language Labels: >>apc<< >>ara<< >>arq<< >>arz<<
- **Original Model**: [opusTCv20210807_transformer-big_2022-07-27.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/gmq-ara/opusTCv20210807_transformer-big_2022-07-27.zip)
- **Resources for more information:**
- [OPUS-MT-train GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train)
- More information about released models for this language pair: [OPUS-MT gmq-ara README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/gmq-ara/README.md)
- [More information about MarianNMT models in the transformers library](https://huggingface.co/docs/transformers/model_doc/marian)
- [Tatoeba Translation Challenge](https://github.com/Helsinki-NLP/Tatoeba-Challenge/
This is a multilingual translation model with multiple target languages. A sentence initial language token is required in the form of `>>id<<` (id = valid target language ID), e.g. `>><<`
## Uses
This model can be used for translation and text-to-text generation.
## Risks, Limitations and Biases
**CONTENT WARNING: Readers should be aware that the model is trained on various public data sets that may contain content that is disturbing, offensive, and can propagate historical and current stereotypes.**
Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)).
## How to Get Started With the Model
A short example code:
```python
from transformers import MarianMTModel, MarianTokenizer
src_text = [
">>ara<< Jeg elsker semitiske sprog.",
">>ara<< Vad handlar boken om?"
]
model_name = "pytorch-models/opus-mt-tc-big-gmq-ar"
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)
translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True))
for t in translated:
print( tokenizer.decode(t, skip_special_tokens=True) )
# expected output:
# أحبّ اللغات الساميّة.
# عن ماذا يتحدث الكتاب؟
```
You can also use OPUS-MT models with the transformers pipelines, for example:
```python
from transformers import pipeline
pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-gmq-ar")
print(pipe(">>ara<< Jeg elsker semitiske sprog."))
# expected output: أحبّ اللغات الساميّة.
```
## Training
- **Data**: opusTCv20210807 ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge))
- **Pre-processing**: SentencePiece (spm32k,spm32k)
- **Model Type:** transformer-big
- **Original MarianNMT Model**: [opusTCv20210807_transformer-big_2022-07-27.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/gmq-ara/opusTCv20210807_transformer-big_2022-07-27.zip)
- **Training Scripts**: [GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train)
## Evaluation
* test set translations: [opusTCv20210807_transformer-big_2022-07-27.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/gmq-ara/opusTCv20210807_transformer-big_2022-07-27.test.txt)
* test set scores: [opusTCv20210807_transformer-big_2022-07-27.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/gmq-ara/opusTCv20210807_transformer-big_2022-07-27.eval.txt)
* benchmark results: [benchmark_results.txt](benchmark_results.txt)
* benchmark output: [benchmark_translations.zip](benchmark_translations.zip)
| langpair | testset | chr-F | BLEU | #sent | #words |
|----------|---------|-------|-------|-------|--------|
| dan-ara | flores101-devtest | 0.52841 | 19.9 | 1012 | 21357 |
| nob-ara | flores101-devtest | 0.49670 | 16.8 | 1012 | 21357 |
| swe-ara | flores101-devtest | 0.51882 | 19.3 | 1012 | 21357 |
## Citation Information
* Publications: [OPUS-MT – Building open translation services for the World](https://aclanthology.org/2020.eamt-1.61/) and [The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt-1.139/) (Please, cite if you use this model.)
```
@inproceedings{tiedemann-thottingal-2020-opus,
title = "{OPUS}-{MT} {--} Building open translation services for the World",
author = {Tiedemann, J{\"o}rg and Thottingal, Santhosh},
booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
month = nov,
year = "2020",
address = "Lisboa, Portugal",
publisher = "European Association for Machine Translation",
url = "https://aclanthology.org/2020.eamt-1.61",
pages = "479--480",
}
@inproceedings{tiedemann-2020-tatoeba,
title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}",
author = {Tiedemann, J{\"o}rg},
booktitle = "Proceedings of the Fifth Conference on Machine Translation",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.wmt-1.139",
pages = "1174--1182",
}
```
## Acknowledgements
The work is supported by the [European Language Grid](https://www.european-language-grid.eu/) as [pilot project 2866](https://live.european-language-grid.eu/catalogue/#/resource/projects/2866), by the [FoTran project](https://www.helsinki.fi/en/researchgroups/natural-language-understanding-with-cross-lingual-grounding), funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 771113), and the [MeMAD project](https://memad.eu/), funded by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 780069. We are also grateful for the generous computational resources and IT infrastructure provided by [CSC -- IT Center for Science](https://www.csc.fi/), Finland.
## Model conversion info
* transformers version: 4.16.2
* OPUS-MT git hash: 8b9f0b0
* port time: Sat Aug 13 00:05:06 EEST 2022
* port machine: LM0-400-22516.local
|
Helsinki-NLP/opus-mt-tc-big-zls-zle
|
Helsinki-NLP
| 2023-10-10T11:25:27Z | 127 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"tc",
"big",
"zls",
"zle",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2022-03-24T12:47:28Z |
---
language:
- be
- bg
- hr
- ru
- sh
- sl
- sr_Cyrl
- sr_Latn
- uk
- zle
- zls
tags:
- translation
- opus-mt-tc
license: cc-by-4.0
model-index:
- name: opus-mt-tc-big-zls-zle
results:
- task:
name: Translation bul-rus
type: translation
args: bul-rus
dataset:
name: flores101-devtest
type: flores_101
args: bul rus devtest
metrics:
- name: BLEU
type: bleu
value: 24.6
- task:
name: Translation bul-ukr
type: translation
args: bul-ukr
dataset:
name: flores101-devtest
type: flores_101
args: bul ukr devtest
metrics:
- name: BLEU
type: bleu
value: 22.9
- task:
name: Translation hrv-rus
type: translation
args: hrv-rus
dataset:
name: flores101-devtest
type: flores_101
args: hrv rus devtest
metrics:
- name: BLEU
type: bleu
value: 23.5
- task:
name: Translation hrv-ukr
type: translation
args: hrv-ukr
dataset:
name: flores101-devtest
type: flores_101
args: hrv ukr devtest
metrics:
- name: BLEU
type: bleu
value: 21.9
- task:
name: Translation mkd-rus
type: translation
args: mkd-rus
dataset:
name: flores101-devtest
type: flores_101
args: mkd rus devtest
metrics:
- name: BLEU
type: bleu
value: 24.3
- task:
name: Translation mkd-ukr
type: translation
args: mkd-ukr
dataset:
name: flores101-devtest
type: flores_101
args: mkd ukr devtest
metrics:
- name: BLEU
type: bleu
value: 22.5
- task:
name: Translation slv-rus
type: translation
args: slv-rus
dataset:
name: flores101-devtest
type: flores_101
args: slv rus devtest
metrics:
- name: BLEU
type: bleu
value: 22.0
- task:
name: Translation slv-ukr
type: translation
args: slv-ukr
dataset:
name: flores101-devtest
type: flores_101
args: slv ukr devtest
metrics:
- name: BLEU
type: bleu
value: 20.2
- task:
name: Translation srp_Cyrl-rus
type: translation
args: srp_Cyrl-rus
dataset:
name: flores101-devtest
type: flores_101
args: srp_Cyrl rus devtest
metrics:
- name: BLEU
type: bleu
value: 25.7
- task:
name: Translation srp_Cyrl-ukr
type: translation
args: srp_Cyrl-ukr
dataset:
name: flores101-devtest
type: flores_101
args: srp_Cyrl ukr devtest
metrics:
- name: BLEU
type: bleu
value: 24.4
- task:
name: Translation bul-rus
type: translation
args: bul-rus
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: bul-rus
metrics:
- name: BLEU
type: bleu
value: 52.6
- task:
name: Translation bul-ukr
type: translation
args: bul-ukr
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: bul-ukr
metrics:
- name: BLEU
type: bleu
value: 53.3
- task:
name: Translation hbs-rus
type: translation
args: hbs-rus
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: hbs-rus
metrics:
- name: BLEU
type: bleu
value: 58.5
- task:
name: Translation hbs-ukr
type: translation
args: hbs-ukr
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: hbs-ukr
metrics:
- name: BLEU
type: bleu
value: 52.3
- task:
name: Translation hrv-ukr
type: translation
args: hrv-ukr
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: hrv-ukr
metrics:
- name: BLEU
type: bleu
value: 50.0
- task:
name: Translation slv-rus
type: translation
args: slv-rus
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: slv-rus
metrics:
- name: BLEU
type: bleu
value: 27.3
- task:
name: Translation srp_Cyrl-rus
type: translation
args: srp_Cyrl-rus
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: srp_Cyrl-rus
metrics:
- name: BLEU
type: bleu
value: 56.2
- task:
name: Translation srp_Cyrl-ukr
type: translation
args: srp_Cyrl-ukr
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: srp_Cyrl-ukr
metrics:
- name: BLEU
type: bleu
value: 51.8
- task:
name: Translation srp_Latn-rus
type: translation
args: srp_Latn-rus
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: srp_Latn-rus
metrics:
- name: BLEU
type: bleu
value: 60.1
- task:
name: Translation srp_Latn-ukr
type: translation
args: srp_Latn-ukr
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: srp_Latn-ukr
metrics:
- name: BLEU
type: bleu
value: 55.8
---
# opus-mt-tc-big-zls-zle
Neural machine translation model for translating from South Slavic languages (zls) to East Slavic languages (zle).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trained using the amazing framework of [Marian NMT](https://marian-nmt.github.io/), an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is taken from [OPUS](https://opus.nlpl.eu/) and training pipelines use the procedures of [OPUS-MT-train](https://github.com/Helsinki-NLP/Opus-MT-train).
* Publications: [OPUS-MT – Building open translation services for the World](https://aclanthology.org/2020.eamt-1.61/) and [The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt-1.139/) (Please, cite if you use this model.)
```
@inproceedings{tiedemann-thottingal-2020-opus,
title = "{OPUS}-{MT} {--} Building open translation services for the World",
author = {Tiedemann, J{\"o}rg and Thottingal, Santhosh},
booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
month = nov,
year = "2020",
address = "Lisboa, Portugal",
publisher = "European Association for Machine Translation",
url = "https://aclanthology.org/2020.eamt-1.61",
pages = "479--480",
}
@inproceedings{tiedemann-2020-tatoeba,
title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}",
author = {Tiedemann, J{\"o}rg},
booktitle = "Proceedings of the Fifth Conference on Machine Translation",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.wmt-1.139",
pages = "1174--1182",
}
```
## Model info
* Release: 2022-03-23
* source language(s): bul hbs hrv slv srp_Cyrl srp_Latn
* target language(s): bel rus ukr
* valid target language labels: >>bel<< >>rus<< >>ukr<<
* model: transformer-big
* data: opusTCv20210807+bt ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge))
* tokenization: SentencePiece (spm32k,spm32k)
* original model: [opusTCv20210807+bt_transformer-big_2022-03-23.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/zls-zle/opusTCv20210807+bt_transformer-big_2022-03-23.zip)
* more information released models: [OPUS-MT zls-zle README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/zls-zle/README.md)
* more information about the model: [MarianMT](https://huggingface.co/docs/transformers/model_doc/marian)
This is a multilingual translation model with multiple target languages. A sentence initial language token is required in the form of `>>id<<` (id = valid target language ID), e.g. `>>bel<<`
## Usage
A short example code:
```python
from transformers import MarianMTModel, MarianTokenizer
src_text = [
">>rus<< Gdje je brigadir?",
">>ukr<< Zovem se Seli."
]
model_name = "pytorch-models/opus-mt-tc-big-zls-zle"
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)
translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True))
for t in translated:
print( tokenizer.decode(t, skip_special_tokens=True) )
# expected output:
# Где бригадир?
# Мене звати Саллі.
```
You can also use OPUS-MT models with the transformers pipelines, for example:
```python
from transformers import pipeline
pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-zls-zle")
print(pipe(">>rus<< Gdje je brigadir?"))
# expected output: Где бригадир?
```
## Benchmarks
* test set translations: [opusTCv20210807+bt_transformer-big_2022-03-23.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zls-zle/opusTCv20210807+bt_transformer-big_2022-03-23.test.txt)
* test set scores: [opusTCv20210807+bt_transformer-big_2022-03-23.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zls-zle/opusTCv20210807+bt_transformer-big_2022-03-23.eval.txt)
* benchmark results: [benchmark_results.txt](benchmark_results.txt)
* benchmark output: [benchmark_translations.zip](benchmark_translations.zip)
| langpair | testset | chr-F | BLEU | #sent | #words |
|----------|---------|-------|-------|-------|--------|
| bul-rus | tatoeba-test-v2021-08-07 | 0.71467 | 52.6 | 1247 | 7870 |
| bul-ukr | tatoeba-test-v2021-08-07 | 0.71757 | 53.3 | 1020 | 4932 |
| hbs-rus | tatoeba-test-v2021-08-07 | 0.74593 | 58.5 | 2500 | 14213 |
| hbs-ukr | tatoeba-test-v2021-08-07 | 0.70244 | 52.3 | 942 | 4961 |
| hrv-ukr | tatoeba-test-v2021-08-07 | 0.68931 | 50.0 | 389 | 2232 |
| slv-rus | tatoeba-test-v2021-08-07 | 0.42255 | 27.3 | 657 | 4056 |
| srp_Cyrl-rus | tatoeba-test-v2021-08-07 | 0.74112 | 56.2 | 881 | 5117 |
| srp_Cyrl-ukr | tatoeba-test-v2021-08-07 | 0.68915 | 51.8 | 205 | 1061 |
| srp_Latn-rus | tatoeba-test-v2021-08-07 | 0.75340 | 60.1 | 1483 | 8311 |
| srp_Latn-ukr | tatoeba-test-v2021-08-07 | 0.73106 | 55.8 | 348 | 1668 |
| bul-rus | flores101-devtest | 0.54226 | 24.6 | 1012 | 23295 |
| bul-ukr | flores101-devtest | 0.53382 | 22.9 | 1012 | 22810 |
| hrv-rus | flores101-devtest | 0.51726 | 23.5 | 1012 | 23295 |
| hrv-ukr | flores101-devtest | 0.51011 | 21.9 | 1012 | 22810 |
| mkd-bel | flores101-devtest | 0.40885 | 10.7 | 1012 | 24829 |
| mkd-rus | flores101-devtest | 0.52509 | 24.3 | 1012 | 23295 |
| mkd-ukr | flores101-devtest | 0.52021 | 22.5 | 1012 | 22810 |
| slv-rus | flores101-devtest | 0.50349 | 22.0 | 1012 | 23295 |
| slv-ukr | flores101-devtest | 0.49156 | 20.2 | 1012 | 22810 |
| srp_Cyrl-rus | flores101-devtest | 0.53656 | 25.7 | 1012 | 23295 |
| srp_Cyrl-ukr | flores101-devtest | 0.53623 | 24.4 | 1012 | 22810 |
## Acknowledgements
The work is supported by the [European Language Grid](https://www.european-language-grid.eu/) as [pilot project 2866](https://live.european-language-grid.eu/catalogue/#/resource/projects/2866), by the [FoTran project](https://www.helsinki.fi/en/researchgroups/natural-language-understanding-with-cross-lingual-grounding), funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 771113), and the [MeMAD project](https://memad.eu/), funded by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 780069. We are also grateful for the generous computational resources and IT infrastructure provided by [CSC -- IT Center for Science](https://www.csc.fi/), Finland.
## Model conversion info
* transformers version: 4.16.2
* OPUS-MT git hash: 1bdabf7
* port time: Thu Mar 24 04:08:51 EET 2022
* port machine: LM0-400-22516.local
|
sanskarGupta551/bloomz-1b7_Prompt_to_Dialog
|
sanskarGupta551
| 2023-10-10T11:25:06Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-10-10T11:25:04Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0
|
Helsinki-NLP/opus-mt-tc-big-he-gmq
|
Helsinki-NLP
| 2023-10-10T11:21:10Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"da",
"he",
"nb",
"sv",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2022-08-12T16:14:29Z |
---
language:
- da
- he
- nb
- sv
tags:
- translation
- opus-mt-tc
license: cc-by-4.0
model-index:
- name: opus-mt-tc-big-he-gmq
results:
- task:
name: Translation heb-dan
type: translation
args: heb-dan
dataset:
name: flores101-devtest
type: flores_101
args: heb dan devtest
metrics:
- name: BLEU
type: bleu
value: 31.4
- name: chr-F
type: chrf
value: 0.58023
- task:
name: Translation heb-isl
type: translation
args: heb-isl
dataset:
name: flores101-devtest
type: flores_101
args: heb isl devtest
metrics:
- name: BLEU
type: bleu
value: 14.0
- name: chr-F
type: chrf
value: 0.41998
- task:
name: Translation heb-nob
type: translation
args: heb-nob
dataset:
name: flores101-devtest
type: flores_101
args: heb nob devtest
metrics:
- name: BLEU
type: bleu
value: 23.7
- name: chr-F
type: chrf
value: 0.53086
- task:
name: Translation heb-swe
type: translation
args: heb-swe
dataset:
name: flores101-devtest
type: flores_101
args: heb swe devtest
metrics:
- name: BLEU
type: bleu
value: 29.6
- name: chr-F
type: chrf
value: 0.56881
---
# opus-mt-tc-big-he-gmq
## Table of Contents
- [Model Details](#model-details)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [How to Get Started With the Model](#how-to-get-started-with-the-model)
- [Training](#training)
- [Evaluation](#evaluation)
- [Citation Information](#citation-information)
- [Acknowledgements](#acknowledgements)
## Model Details
Neural machine translation model for translating from Hebrew (he) to North Germanic languages (gmq).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trained using the amazing framework of [Marian NMT](https://marian-nmt.github.io/), an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is taken from [OPUS](https://opus.nlpl.eu/) and training pipelines use the procedures of [OPUS-MT-train](https://github.com/Helsinki-NLP/Opus-MT-train).
**Model Description:**
- **Developed by:** Language Technology Research Group at the University of Helsinki
- **Model Type:** Translation (transformer-big)
- **Release**: 2022-07-23
- **License:** CC-BY-4.0
- **Language(s):**
- Source Language(s): heb
- Target Language(s): dan nob nor swe
- Language Pair(s): heb-dan heb-nob heb-swe
- Valid Target Language Labels: >>dan<< >>fao<< >>isl<< >>jut<< >>nno<< >>nob<< >>non<< >>nrn<< >>ovd<< >>qer<< >>rmg<< >>swe<<
- **Original Model**: [opusTCv20210807_transformer-big_2022-07-23.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/heb-gmq/opusTCv20210807_transformer-big_2022-07-23.zip)
- **Resources for more information:**
- [OPUS-MT-train GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train)
- More information about released models for this language pair: [OPUS-MT heb-gmq README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/heb-gmq/README.md)
- [More information about MarianNMT models in the transformers library](https://huggingface.co/docs/transformers/model_doc/marian)
- [Tatoeba Translation Challenge](https://github.com/Helsinki-NLP/Tatoeba-Challenge/
This is a multilingual translation model with multiple target languages. A sentence initial language token is required in the form of `>>id<<` (id = valid target language ID), e.g. `>>dan<<`
## Uses
This model can be used for translation and text-to-text generation.
## Risks, Limitations and Biases
**CONTENT WARNING: Readers should be aware that the model is trained on various public data sets that may contain content that is disturbing, offensive, and can propagate historical and current stereotypes.**
Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)).
## How to Get Started With the Model
A short example code:
```python
from transformers import MarianMTModel, MarianTokenizer
src_text = [
">>dan<< כל שלושת הילדים של אליעזר לודוויג זמנהוף נרצחו בשואה.",
">>swe<< הסתבר שטום היה מרגל."
]
model_name = "pytorch-models/opus-mt-tc-big-he-gmq"
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)
translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True))
for t in translated:
print( tokenizer.decode(t, skip_special_tokens=True) )
# expected output:
# Alle tre børn af Eliezer Ludwig Zamenhof blev dræbt i Holocaust.
# Det visade sig att Tom var en spion.
```
You can also use OPUS-MT models with the transformers pipelines, for example:
```python
from transformers import pipeline
pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-he-gmq")
print(pipe(">>dan<< כל שלושת הילדים של אליעזר לודוויג זמנהוף נרצחו בשואה."))
# expected output: Alle tre børn af Eliezer Ludwig Zamenhof blev dræbt i Holocaust.
```
## Training
- **Data**: opusTCv20210807 ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge))
- **Pre-processing**: SentencePiece (spm32k,spm32k)
- **Model Type:** transformer-big
- **Original MarianNMT Model**: [opusTCv20210807_transformer-big_2022-07-23.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/heb-gmq/opusTCv20210807_transformer-big_2022-07-23.zip)
- **Training Scripts**: [GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train)
## Evaluation
* test set translations: [opusTCv20210807_transformer-big_2022-07-23.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/heb-gmq/opusTCv20210807_transformer-big_2022-07-23.test.txt)
* test set scores: [opusTCv20210807_transformer-big_2022-07-23.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/heb-gmq/opusTCv20210807_transformer-big_2022-07-23.eval.txt)
* benchmark results: [benchmark_results.txt](benchmark_results.txt)
* benchmark output: [benchmark_translations.zip](benchmark_translations.zip)
| langpair | testset | chr-F | BLEU | #sent | #words |
|----------|---------|-------|-------|-------|--------|
| heb-dan | flores101-devtest | 0.58023 | 31.4 | 1012 | 24638 |
| heb-isl | flores101-devtest | 0.41998 | 14.0 | 1012 | 22834 |
| heb-nob | flores101-devtest | 0.53086 | 23.7 | 1012 | 23873 |
| heb-swe | flores101-devtest | 0.56881 | 29.6 | 1012 | 23121 |
## Citation Information
* Publications: [OPUS-MT – Building open translation services for the World](https://aclanthology.org/2020.eamt-1.61/) and [The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt-1.139/) (Please, cite if you use this model.)
```
@inproceedings{tiedemann-thottingal-2020-opus,
title = "{OPUS}-{MT} {--} Building open translation services for the World",
author = {Tiedemann, J{\"o}rg and Thottingal, Santhosh},
booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
month = nov,
year = "2020",
address = "Lisboa, Portugal",
publisher = "European Association for Machine Translation",
url = "https://aclanthology.org/2020.eamt-1.61",
pages = "479--480",
}
@inproceedings{tiedemann-2020-tatoeba,
title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}",
author = {Tiedemann, J{\"o}rg},
booktitle = "Proceedings of the Fifth Conference on Machine Translation",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.wmt-1.139",
pages = "1174--1182",
}
```
## Acknowledgements
The work is supported by the [European Language Grid](https://www.european-language-grid.eu/) as [pilot project 2866](https://live.european-language-grid.eu/catalogue/#/resource/projects/2866), by the [FoTran project](https://www.helsinki.fi/en/researchgroups/natural-language-understanding-with-cross-lingual-grounding), funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 771113), and the [MeMAD project](https://memad.eu/), funded by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 780069. We are also grateful for the generous computational resources and IT infrastructure provided by [CSC -- IT Center for Science](https://www.csc.fi/), Finland.
## Model conversion info
* transformers version: 4.16.2
* OPUS-MT git hash: 8b9f0b0
* port time: Sat Aug 13 00:07:45 EEST 2022
* port machine: LM0-400-22516.local
|
Helsinki-NLP/opus-mt-tc-big-cel-en
|
Helsinki-NLP
| 2023-10-10T11:19:09Z | 114 | 1 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"br",
"cel",
"cy",
"en",
"ga",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2022-04-13T15:36:34Z |
---
language:
- br
- cel
- cy
- en
- ga
tags:
- translation
- opus-mt-tc
license: cc-by-4.0
model-index:
- name: opus-mt-tc-big-cel-en
results:
- task:
name: Translation cym-eng
type: translation
args: cym-eng
dataset:
name: flores101-devtest
type: flores_101
args: cym eng devtest
metrics:
- name: BLEU
type: bleu
value: 50.2
- task:
name: Translation gle-eng
type: translation
args: gle-eng
dataset:
name: flores101-devtest
type: flores_101
args: gle eng devtest
metrics:
- name: BLEU
type: bleu
value: 37.4
- task:
name: Translation bre-eng
type: translation
args: bre-eng
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: bre-eng
metrics:
- name: BLEU
type: bleu
value: 36.1
- task:
name: Translation cym-eng
type: translation
args: cym-eng
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: cym-eng
metrics:
- name: BLEU
type: bleu
value: 53.6
- task:
name: Translation gle-eng
type: translation
args: gle-eng
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: gle-eng
metrics:
- name: BLEU
type: bleu
value: 57.7
---
# opus-mt-tc-big-cel-en
Neural machine translation model for translating from Celtic languages (cel) to English (en).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trained using the amazing framework of [Marian NMT](https://marian-nmt.github.io/), an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is taken from [OPUS](https://opus.nlpl.eu/) and training pipelines use the procedures of [OPUS-MT-train](https://github.com/Helsinki-NLP/Opus-MT-train).
* Publications: [OPUS-MT – Building open translation services for the World](https://aclanthology.org/2020.eamt-1.61/) and [The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt-1.139/) (Please, cite if you use this model.)
```
@inproceedings{tiedemann-thottingal-2020-opus,
title = "{OPUS}-{MT} {--} Building open translation services for the World",
author = {Tiedemann, J{\"o}rg and Thottingal, Santhosh},
booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
month = nov,
year = "2020",
address = "Lisboa, Portugal",
publisher = "European Association for Machine Translation",
url = "https://aclanthology.org/2020.eamt-1.61",
pages = "479--480",
}
@inproceedings{tiedemann-2020-tatoeba,
title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}",
author = {Tiedemann, J{\"o}rg},
booktitle = "Proceedings of the Fifth Conference on Machine Translation",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.wmt-1.139",
pages = "1174--1182",
}
```
## Model info
* Release: 2022-03-13
* source language(s): bre cym gle
* target language(s): eng
* model: transformer-big
* data: opusTCv20210807+bt ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge))
* tokenization: SentencePiece (spm32k,spm32k)
* original model: [opusTCv20210807+bt_transformer-big_2022-03-13.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/cel-eng/opusTCv20210807+bt_transformer-big_2022-03-13.zip)
* more information released models: [OPUS-MT cel-eng README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/cel-eng/README.md)
## Usage
A short example code:
```python
from transformers import MarianMTModel, MarianTokenizer
src_text = [
"A-du emaoc’h?",
"Ta'n ushtey glen."
]
model_name = "pytorch-models/opus-mt-tc-big-cel-en"
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)
translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True))
for t in translated:
print( tokenizer.decode(t, skip_special_tokens=True) )
# expected output:
# Is that you?
# Ta'n ushtey glen.
```
You can also use OPUS-MT models with the transformers pipelines, for example:
```python
from transformers import pipeline
pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-cel-en")
print(pipe("A-du emaoc’h?"))
# expected output: Is that you?
```
## Benchmarks
* test set translations: [opusTCv20210807+bt_transformer-big_2022-03-13.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/cel-eng/opusTCv20210807+bt_transformer-big_2022-03-13.test.txt)
* test set scores: [opusTCv20210807+bt_transformer-big_2022-03-13.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/cel-eng/opusTCv20210807+bt_transformer-big_2022-03-13.eval.txt)
* benchmark results: [benchmark_results.txt](benchmark_results.txt)
* benchmark output: [benchmark_translations.zip](benchmark_translations.zip)
| langpair | testset | chr-F | BLEU | #sent | #words |
|----------|---------|-------|-------|-------|--------|
| bre-eng | tatoeba-test-v2021-08-07 | 0.53712 | 36.1 | 383 | 2065 |
| cym-eng | tatoeba-test-v2021-08-07 | 0.69239 | 53.6 | 818 | 5563 |
| gle-eng | tatoeba-test-v2021-08-07 | 0.72087 | 57.7 | 1913 | 11190 |
| cym-eng | flores101-devtest | 0.71379 | 50.2 | 1012 | 24721 |
| gle-eng | flores101-devtest | 0.63946 | 37.4 | 1012 | 24721 |
## Acknowledgements
The work is supported by the [European Language Grid](https://www.european-language-grid.eu/) as [pilot project 2866](https://live.european-language-grid.eu/catalogue/#/resource/projects/2866), by the [FoTran project](https://www.helsinki.fi/en/researchgroups/natural-language-understanding-with-cross-lingual-grounding), funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 771113), and the [MeMAD project](https://memad.eu/), funded by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 780069. We are also grateful for the generous computational resources and IT infrastructure provided by [CSC -- IT Center for Science](https://www.csc.fi/), Finland.
## Model conversion info
* transformers version: 4.16.2
* OPUS-MT git hash: 3405783
* port time: Wed Apr 13 18:36:25 EEST 2022
* port machine: LM0-400-22516.local
|
Helsinki-NLP/opus-mt-tc-big-zle-zle
|
Helsinki-NLP
| 2023-10-10T11:17:01Z | 114 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"be",
"ru",
"uk",
"zle",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2022-03-24T12:08:16Z |
---
language:
- be
- ru
- uk
- zle
tags:
- translation
- opus-mt-tc
license: cc-by-4.0
model-index:
- name: opus-mt-tc-big-zle-zle
results:
- task:
name: Translation rus-ukr
type: translation
args: rus-ukr
dataset:
name: flores101-devtest
type: flores_101
args: rus ukr devtest
metrics:
- name: BLEU
type: bleu
value: 25.5
- task:
name: Translation ukr-rus
type: translation
args: ukr-rus
dataset:
name: flores101-devtest
type: flores_101
args: ukr rus devtest
metrics:
- name: BLEU
type: bleu
value: 28.3
- task:
name: Translation bel-rus
type: translation
args: bel-rus
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: bel-rus
metrics:
- name: BLEU
type: bleu
value: 68.6
- task:
name: Translation bel-ukr
type: translation
args: bel-ukr
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: bel-ukr
metrics:
- name: BLEU
type: bleu
value: 65.5
- task:
name: Translation rus-bel
type: translation
args: rus-bel
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: rus-bel
metrics:
- name: BLEU
type: bleu
value: 50.3
- task:
name: Translation rus-ukr
type: translation
args: rus-ukr
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: rus-ukr
metrics:
- name: BLEU
type: bleu
value: 70.1
- task:
name: Translation ukr-bel
type: translation
args: ukr-bel
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: ukr-bel
metrics:
- name: BLEU
type: bleu
value: 58.9
- task:
name: Translation ukr-rus
type: translation
args: ukr-rus
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: ukr-rus
metrics:
- name: BLEU
type: bleu
value: 75.7
---
# opus-mt-tc-big-zle-zle
Neural machine translation model for translating from East Slavic languages (zle) to East Slavic languages (zle).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trained using the amazing framework of [Marian NMT](https://marian-nmt.github.io/), an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is taken from [OPUS](https://opus.nlpl.eu/) and training pipelines use the procedures of [OPUS-MT-train](https://github.com/Helsinki-NLP/Opus-MT-train).
* Publications: [OPUS-MT – Building open translation services for the World](https://aclanthology.org/2020.eamt-1.61/) and [The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt-1.139/) (Please, cite if you use this model.)
```
@inproceedings{tiedemann-thottingal-2020-opus,
title = "{OPUS}-{MT} {--} Building open translation services for the World",
author = {Tiedemann, J{\"o}rg and Thottingal, Santhosh},
booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
month = nov,
year = "2020",
address = "Lisboa, Portugal",
publisher = "European Association for Machine Translation",
url = "https://aclanthology.org/2020.eamt-1.61",
pages = "479--480",
}
@inproceedings{tiedemann-2020-tatoeba,
title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}",
author = {Tiedemann, J{\"o}rg},
booktitle = "Proceedings of the Fifth Conference on Machine Translation",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.wmt-1.139",
pages = "1174--1182",
}
```
## Model info
* Release: 2022-03-07
* source language(s): bel rus ukr
* target language(s): bel rus ukr
* valid target language labels: >>bel<< >>rus<< >>ukr<<
* model: transformer-big
* data: opusTCv20210807+bt ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge))
* tokenization: SentencePiece (spm32k,spm32k)
* original model: [opusTCv20210807+bt_transformer-big_2022-03-07.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/zle-zle/opusTCv20210807+bt_transformer-big_2022-03-07.zip)
* more information released models: [OPUS-MT zle-zle README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/zle-zle/README.md)
* more information about the model: [MarianMT](https://huggingface.co/docs/transformers/model_doc/marian)
This is a multilingual translation model with multiple target languages. A sentence initial language token is required in the form of `>>id<<` (id = valid target language ID), e.g. `>>bel<<`
## Usage
A short example code:
```python
from transformers import MarianMTModel, MarianTokenizer
src_text = [
">>ukr<< Кот мёртвый.",
">>bel<< Джон живе в Нью-Йорку."
]
model_name = "pytorch-models/opus-mt-tc-big-zle-zle"
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)
translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True))
for t in translated:
print( tokenizer.decode(t, skip_special_tokens=True) )
# expected output:
# Кіт мертвий.
# Джон жыве ў Нью-Йорку.
```
You can also use OPUS-MT models with the transformers pipelines, for example:
```python
from transformers import pipeline
pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-zle-zle")
print(pipe(">>ukr<< Кот мёртвый."))
# expected output: Кіт мертвий.
```
## Benchmarks
* test set translations: [opusTCv20210807+bt_transformer-big_2022-03-07.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zle-zle/opusTCv20210807+bt_transformer-big_2022-03-07.test.txt)
* test set scores: [opusTCv20210807+bt_transformer-big_2022-03-07.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zle-zle/opusTCv20210807+bt_transformer-big_2022-03-07.eval.txt)
* benchmark results: [benchmark_results.txt](benchmark_results.txt)
* benchmark output: [benchmark_translations.zip](benchmark_translations.zip)
| langpair | testset | chr-F | BLEU | #sent | #words |
|----------|---------|-------|-------|-------|--------|
| bel-rus | tatoeba-test-v2021-08-07 | 0.82526 | 68.6 | 2500 | 18895 |
| bel-ukr | tatoeba-test-v2021-08-07 | 0.81036 | 65.5 | 2355 | 15179 |
| rus-bel | tatoeba-test-v2021-08-07 | 0.66943 | 50.3 | 2500 | 18756 |
| rus-ukr | tatoeba-test-v2021-08-07 | 0.83639 | 70.1 | 10000 | 60212 |
| ukr-bel | tatoeba-test-v2021-08-07 | 0.75368 | 58.9 | 2355 | 15175 |
| ukr-rus | tatoeba-test-v2021-08-07 | 0.86806 | 75.7 | 10000 | 60387 |
| bel-rus | flores101-devtest | 0.47960 | 14.5 | 1012 | 23295 |
| bel-ukr | flores101-devtest | 0.47335 | 12.8 | 1012 | 22810 |
| rus-ukr | flores101-devtest | 0.55287 | 25.5 | 1012 | 22810 |
| ukr-rus | flores101-devtest | 0.56224 | 28.3 | 1012 | 23295 |
## Acknowledgements
The work is supported by the [European Language Grid](https://www.european-language-grid.eu/) as [pilot project 2866](https://live.european-language-grid.eu/catalogue/#/resource/projects/2866), by the [FoTran project](https://www.helsinki.fi/en/researchgroups/natural-language-understanding-with-cross-lingual-grounding), funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 771113), and the [MeMAD project](https://memad.eu/), funded by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 780069. We are also grateful for the generous computational resources and IT infrastructure provided by [CSC -- IT Center for Science](https://www.csc.fi/), Finland.
## Model conversion info
* transformers version: 4.16.2
* OPUS-MT git hash: 1bdabf7
* port time: Thu Mar 24 00:15:39 EET 2022
* port machine: LM0-400-22516.local
|
Helsinki-NLP/opus-mt-tc-big-zle-zlw
|
Helsinki-NLP
| 2023-10-10T11:13:51Z | 119 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"be",
"cs",
"pl",
"ru",
"uk",
"zle",
"zlw",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2022-03-24T12:13:49Z |
---
language:
- be
- cs
- pl
- ru
- uk
- zle
- zlw
tags:
- translation
- opus-mt-tc
license: cc-by-4.0
model-index:
- name: opus-mt-tc-big-zle-zlw
results:
- task:
name: Translation rus-ces
type: translation
args: rus-ces
dataset:
name: flores101-devtest
type: flores_101
args: rus ces devtest
metrics:
- name: BLEU
type: bleu
value: 23.1
- task:
name: Translation ukr-ces
type: translation
args: ukr-ces
dataset:
name: flores101-devtest
type: flores_101
args: ukr ces devtest
metrics:
- name: BLEU
type: bleu
value: 25.1
- task:
name: Translation bel-pol
type: translation
args: bel-pol
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: bel-pol
metrics:
- name: BLEU
type: bleu
value: 47.1
- task:
name: Translation rus-ces
type: translation
args: rus-ces
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: rus-ces
metrics:
- name: BLEU
type: bleu
value: 53.4
- task:
name: Translation rus-pol
type: translation
args: rus-pol
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: rus-pol
metrics:
- name: BLEU
type: bleu
value: 53.7
- task:
name: Translation ukr-ces
type: translation
args: ukr-ces
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: ukr-ces
metrics:
- name: BLEU
type: bleu
value: 58.0
- task:
name: Translation ukr-pol
type: translation
args: ukr-pol
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: ukr-pol
metrics:
- name: BLEU
type: bleu
value: 57.0
- task:
name: Translation rus-ces
type: translation
args: rus-ces
dataset:
name: newstest2013
type: wmt-2013-news
args: rus-ces
metrics:
- name: BLEU
type: bleu
value: 26.0
---
# opus-mt-tc-big-zle-zlw
Neural machine translation model for translating from East Slavic languages (zle) to West Slavic languages (zlw).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trained using the amazing framework of [Marian NMT](https://marian-nmt.github.io/), an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is taken from [OPUS](https://opus.nlpl.eu/) and training pipelines use the procedures of [OPUS-MT-train](https://github.com/Helsinki-NLP/Opus-MT-train).
* Publications: [OPUS-MT – Building open translation services for the World](https://aclanthology.org/2020.eamt-1.61/) and [The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt-1.139/) (Please, cite if you use this model.)
```
@inproceedings{tiedemann-thottingal-2020-opus,
title = "{OPUS}-{MT} {--} Building open translation services for the World",
author = {Tiedemann, J{\"o}rg and Thottingal, Santhosh},
booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
month = nov,
year = "2020",
address = "Lisboa, Portugal",
publisher = "European Association for Machine Translation",
url = "https://aclanthology.org/2020.eamt-1.61",
pages = "479--480",
}
@inproceedings{tiedemann-2020-tatoeba,
title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}",
author = {Tiedemann, J{\"o}rg},
booktitle = "Proceedings of the Fifth Conference on Machine Translation",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.wmt-1.139",
pages = "1174--1182",
}
```
## Model info
* Release: 2022-03-23
* source language(s): bel rus ukr
* target language(s): ces pol
* valid target language labels: >>ces<< >>pol<<
* model: transformer-big
* data: opusTCv20210807+bt ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge))
* tokenization: SentencePiece (spm32k,spm32k)
* original model: [opusTCv20210807+bt_transformer-big_2022-03-23.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/zle-zlw/opusTCv20210807+bt_transformer-big_2022-03-23.zip)
* more information released models: [OPUS-MT zle-zlw README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/zle-zlw/README.md)
* more information about the model: [MarianMT](https://huggingface.co/docs/transformers/model_doc/marian)
This is a multilingual translation model with multiple target languages. A sentence initial language token is required in the form of `>>id<<` (id = valid target language ID), e.g. `>>ces<<`
## Usage
A short example code:
```python
from transformers import MarianMTModel, MarianTokenizer
src_text = [
">>pol<< Это метафора.",
">>pol<< Что вы делали?"
]
model_name = "pytorch-models/opus-mt-tc-big-zle-zlw"
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)
translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True))
for t in translated:
print( tokenizer.decode(t, skip_special_tokens=True) )
# expected output:
# To metafora.
# Co robiliście?
```
You can also use OPUS-MT models with the transformers pipelines, for example:
```python
from transformers import pipeline
pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-zle-zlw")
print(pipe(">>pol<< Это метафора."))
# expected output: To metafora.
```
## Benchmarks
* test set translations: [opusTCv20210807+bt_transformer-big_2022-03-23.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zle-zlw/opusTCv20210807+bt_transformer-big_2022-03-23.test.txt)
* test set scores: [opusTCv20210807+bt_transformer-big_2022-03-23.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zle-zlw/opusTCv20210807+bt_transformer-big_2022-03-23.eval.txt)
* benchmark results: [benchmark_results.txt](benchmark_results.txt)
* benchmark output: [benchmark_translations.zip](benchmark_translations.zip)
| langpair | testset | chr-F | BLEU | #sent | #words |
|----------|---------|-------|-------|-------|--------|
| bel-pol | tatoeba-test-v2021-08-07 | 0.65517 | 47.1 | 287 | 1706 |
| rus-ces | tatoeba-test-v2021-08-07 | 0.69695 | 53.4 | 2934 | 16831 |
| rus-pol | tatoeba-test-v2021-08-07 | 0.72176 | 53.7 | 3543 | 21505 |
| ukr-ces | tatoeba-test-v2021-08-07 | 0.73149 | 58.0 | 1787 | 8550 |
| ukr-pol | tatoeba-test-v2021-08-07 | 0.74649 | 57.0 | 2519 | 13201 |
| bel-ces | flores101-devtest | 0.41248 | 11.1 | 1012 | 22101 |
| bel-pol | flores101-devtest | 0.42240 | 10.2 | 1012 | 22520 |
| rus-ces | flores101-devtest | 0.50971 | 23.1 | 1012 | 22101 |
| rus-pol | flores101-devtest | 0.48672 | 18.4 | 1012 | 22520 |
| ukr-ces | flores101-devtest | 0.52482 | 25.1 | 1012 | 22101 |
| ukr-pol | flores101-devtest | 0.48790 | 18.8 | 1012 | 22520 |
| rus-ces | newstest2012 | 0.45834 | 18.8 | 3003 | 65456 |
| rus-ces | newstest2013 | 0.52364 | 26.0 | 3000 | 57250 |
## Acknowledgements
The work is supported by the [European Language Grid](https://www.european-language-grid.eu/) as [pilot project 2866](https://live.european-language-grid.eu/catalogue/#/resource/projects/2866), by the [FoTran project](https://www.helsinki.fi/en/researchgroups/natural-language-understanding-with-cross-lingual-grounding), funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 771113), and the [MeMAD project](https://memad.eu/), funded by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 780069. We are also grateful for the generous computational resources and IT infrastructure provided by [CSC -- IT Center for Science](https://www.csc.fi/), Finland.
## Model conversion info
* transformers version: 4.16.2
* OPUS-MT git hash: 1bdabf7
* port time: Thu Mar 24 00:50:29 EET 2022
* port machine: LM0-400-22516.local
|
Helsinki-NLP/opus-mt-tc-big-fr-zle
|
Helsinki-NLP
| 2023-10-10T11:11:18Z | 121 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"be",
"fr",
"ru",
"uk",
"zle",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2022-03-24T12:29:13Z |
---
language:
- be
- fr
- ru
- uk
- zle
tags:
- translation
- opus-mt-tc
license: cc-by-4.0
model-index:
- name: opus-mt-tc-big-fr-zle
results:
- task:
name: Translation fra-rus
type: translation
args: fra-rus
dataset:
name: flores101-devtest
type: flores_101
args: fra rus devtest
metrics:
- name: BLEU
type: bleu
value: 25.8
- task:
name: Translation fra-ukr
type: translation
args: fra-ukr
dataset:
name: flores101-devtest
type: flores_101
args: fra ukr devtest
metrics:
- name: BLEU
type: bleu
value: 23.1
- task:
name: Translation fra-bel
type: translation
args: fra-bel
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: fra-bel
metrics:
- name: BLEU
type: bleu
value: 31.1
- task:
name: Translation fra-rus
type: translation
args: fra-rus
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: fra-rus
metrics:
- name: BLEU
type: bleu
value: 46.1
- task:
name: Translation fra-ukr
type: translation
args: fra-ukr
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: fra-ukr
metrics:
- name: BLEU
type: bleu
value: 39.9
- task:
name: Translation fra-rus
type: translation
args: fra-rus
dataset:
name: newstest2012
type: wmt-2012-news
args: fra-rus
metrics:
- name: BLEU
type: bleu
value: 23.1
- task:
name: Translation fra-rus
type: translation
args: fra-rus
dataset:
name: newstest2013
type: wmt-2013-news
args: fra-rus
metrics:
- name: BLEU
type: bleu
value: 24.8
---
# opus-mt-tc-big-fr-zle
Neural machine translation model for translating from French (fr) to East Slavic languages (zle).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trained using the amazing framework of [Marian NMT](https://marian-nmt.github.io/), an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is taken from [OPUS](https://opus.nlpl.eu/) and training pipelines use the procedures of [OPUS-MT-train](https://github.com/Helsinki-NLP/Opus-MT-train).
* Publications: [OPUS-MT – Building open translation services for the World](https://aclanthology.org/2020.eamt-1.61/) and [The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt-1.139/) (Please, cite if you use this model.)
```
@inproceedings{tiedemann-thottingal-2020-opus,
title = "{OPUS}-{MT} {--} Building open translation services for the World",
author = {Tiedemann, J{\"o}rg and Thottingal, Santhosh},
booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
month = nov,
year = "2020",
address = "Lisboa, Portugal",
publisher = "European Association for Machine Translation",
url = "https://aclanthology.org/2020.eamt-1.61",
pages = "479--480",
}
@inproceedings{tiedemann-2020-tatoeba,
title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}",
author = {Tiedemann, J{\"o}rg},
booktitle = "Proceedings of the Fifth Conference on Machine Translation",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.wmt-1.139",
pages = "1174--1182",
}
```
## Model info
* Release: 2022-03-23
* source language(s): fra
* target language(s): bel rus ukr
* valid target language labels: >>bel<< >>rus<< >>ukr<<
* model: transformer-big
* data: opusTCv20210807 ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge))
* tokenization: SentencePiece (spm32k,spm32k)
* original model: [opusTCv20210807_transformer-big_2022-03-23.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/fra-zle/opusTCv20210807_transformer-big_2022-03-23.zip)
* more information released models: [OPUS-MT fra-zle README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/fra-zle/README.md)
* more information about the model: [MarianMT](https://huggingface.co/docs/transformers/model_doc/marian)
This is a multilingual translation model with multiple target languages. A sentence initial language token is required in the form of `>>id<<` (id = valid target language ID), e.g. `>>bel<<`
## Usage
A short example code:
```python
from transformers import MarianMTModel, MarianTokenizer
src_text = [
">>rus<< Ils ont acheté un très bon appareil photo.",
">>ukr<< Il s'est soudain mis à pleuvoir."
]
model_name = "pytorch-models/opus-mt-tc-big-fr-zle"
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)
translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True))
for t in translated:
print( tokenizer.decode(t, skip_special_tokens=True) )
# expected output:
# Они купили очень хорошую камеру.
# Раптом почався дощ.
```
You can also use OPUS-MT models with the transformers pipelines, for example:
```python
from transformers import pipeline
pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-fr-zle")
print(pipe(">>rus<< Ils ont acheté un très bon appareil photo."))
# expected output: Они купили очень хорошую камеру.
```
## Benchmarks
* test set translations: [opusTCv20210807_transformer-big_2022-03-23.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/fra-zle/opusTCv20210807_transformer-big_2022-03-23.test.txt)
* test set scores: [opusTCv20210807_transformer-big_2022-03-23.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/fra-zle/opusTCv20210807_transformer-big_2022-03-23.eval.txt)
* benchmark results: [benchmark_results.txt](benchmark_results.txt)
* benchmark output: [benchmark_translations.zip](benchmark_translations.zip)
| langpair | testset | chr-F | BLEU | #sent | #words |
|----------|---------|-------|-------|-------|--------|
| fra-bel | tatoeba-test-v2021-08-07 | 0.52711 | 31.1 | 283 | 1703 |
| fra-rus | tatoeba-test-v2021-08-07 | 0.66502 | 46.1 | 11490 | 70123 |
| fra-ukr | tatoeba-test-v2021-08-07 | 0.61860 | 39.9 | 10035 | 54372 |
| fra-rus | flores101-devtest | 0.54106 | 25.8 | 1012 | 23295 |
| fra-ukr | flores101-devtest | 0.52733 | 23.1 | 1012 | 22810 |
| fra-rus | newstest2012 | 0.51254 | 23.1 | 3003 | 64790 |
| fra-rus | newstest2013 | 0.52342 | 24.8 | 3000 | 58560 |
## Acknowledgements
The work is supported by the [European Language Grid](https://www.european-language-grid.eu/) as [pilot project 2866](https://live.european-language-grid.eu/catalogue/#/resource/projects/2866), by the [FoTran project](https://www.helsinki.fi/en/researchgroups/natural-language-understanding-with-cross-lingual-grounding), funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 771113), and the [MeMAD project](https://memad.eu/), funded by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 780069. We are also grateful for the generous computational resources and IT infrastructure provided by [CSC -- IT Center for Science](https://www.csc.fi/), Finland.
## Model conversion info
* transformers version: 4.16.2
* OPUS-MT git hash: 1bdabf7
* port time: Thu Mar 24 02:05:04 EET 2022
* port machine: LM0-400-22516.local
|
Helsinki-NLP/opus-mt-tc-big-pt-zle
|
Helsinki-NLP
| 2023-10-10T11:10:15Z | 109 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"pt",
"ru",
"uk",
"zle",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2022-03-24T12:39:11Z |
---
language:
- pt
- ru
- uk
- zle
tags:
- translation
- opus-mt-tc
license: cc-by-4.0
model-index:
- name: opus-mt-tc-big-pt-zle
results:
- task:
name: Translation por-rus
type: translation
args: por-rus
dataset:
name: flores101-devtest
type: flores_101
args: por rus devtest
metrics:
- name: BLEU
type: bleu
value: 26.8
- task:
name: Translation por-ukr
type: translation
args: por-ukr
dataset:
name: flores101-devtest
type: flores_101
args: por ukr devtest
metrics:
- name: BLEU
type: bleu
value: 25.1
- task:
name: Translation por-rus
type: translation
args: por-rus
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: por-rus
metrics:
- name: BLEU
type: bleu
value: 47.6
- task:
name: Translation por-ukr
type: translation
args: por-ukr
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: por-ukr
metrics:
- name: BLEU
type: bleu
value: 44.7
---
# opus-mt-tc-big-pt-zle
Neural machine translation model for translating from Portuguese (pt) to East Slavic languages (zle).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trained using the amazing framework of [Marian NMT](https://marian-nmt.github.io/), an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is taken from [OPUS](https://opus.nlpl.eu/) and training pipelines use the procedures of [OPUS-MT-train](https://github.com/Helsinki-NLP/Opus-MT-train).
* Publications: [OPUS-MT – Building open translation services for the World](https://aclanthology.org/2020.eamt-1.61/) and [The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt-1.139/) (Please, cite if you use this model.)
```
@inproceedings{tiedemann-thottingal-2020-opus,
title = "{OPUS}-{MT} {--} Building open translation services for the World",
author = {Tiedemann, J{\"o}rg and Thottingal, Santhosh},
booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
month = nov,
year = "2020",
address = "Lisboa, Portugal",
publisher = "European Association for Machine Translation",
url = "https://aclanthology.org/2020.eamt-1.61",
pages = "479--480",
}
@inproceedings{tiedemann-2020-tatoeba,
title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}",
author = {Tiedemann, J{\"o}rg},
booktitle = "Proceedings of the Fifth Conference on Machine Translation",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.wmt-1.139",
pages = "1174--1182",
}
```
## Model info
* Release: 2022-03-23
* source language(s): por
* target language(s): rus ukr
* valid target language labels: >>rus<< >>ukr<<
* model: transformer-big
* data: opusTCv20210807 ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge))
* tokenization: SentencePiece (spm32k,spm32k)
* original model: [opusTCv20210807_transformer-big_2022-03-23.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/por-zle/opusTCv20210807_transformer-big_2022-03-23.zip)
* more information released models: [OPUS-MT por-zle README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/por-zle/README.md)
* more information about the model: [MarianMT](https://huggingface.co/docs/transformers/model_doc/marian)
This is a multilingual translation model with multiple target languages. A sentence initial language token is required in the form of `>>id<<` (id = valid target language ID), e.g. `>>rus<<`
## Usage
A short example code:
```python
from transformers import MarianMTModel, MarianTokenizer
src_text = [
">>ukr<< Esse é o meu lugar.",
">>rus<< Tom tem problemas de saúde."
]
model_name = "pytorch-models/opus-mt-tc-big-pt-zle"
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)
translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True))
for t in translated:
print( tokenizer.decode(t, skip_special_tokens=True) )
# expected output:
# Це моє місце.
# У Тома проблемы со здоровьем.
```
You can also use OPUS-MT models with the transformers pipelines, for example:
```python
from transformers import pipeline
pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-pt-zle")
print(pipe(">>ukr<< Esse é o meu lugar."))
# expected output: Це моє місце.
```
## Benchmarks
* test set translations: [opusTCv20210807_transformer-big_2022-03-23.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/por-zle/opusTCv20210807_transformer-big_2022-03-23.test.txt)
* test set scores: [opusTCv20210807_transformer-big_2022-03-23.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/por-zle/opusTCv20210807_transformer-big_2022-03-23.eval.txt)
* benchmark results: [benchmark_results.txt](benchmark_results.txt)
* benchmark output: [benchmark_translations.zip](benchmark_translations.zip)
| langpair | testset | chr-F | BLEU | #sent | #words |
|----------|---------|-------|-------|-------|--------|
| por-rus | tatoeba-test-v2021-08-07 | 0.67980 | 47.6 | 10000 | 65326 |
| por-ukr | tatoeba-test-v2021-08-07 | 0.65867 | 44.7 | 3372 | 18933 |
| por-rus | flores101-devtest | 0.54675 | 26.8 | 1012 | 23295 |
| por-ukr | flores101-devtest | 0.53690 | 25.1 | 1012 | 22810 |
## Acknowledgements
The work is supported by the [European Language Grid](https://www.european-language-grid.eu/) as [pilot project 2866](https://live.european-language-grid.eu/catalogue/#/resource/projects/2866), by the [FoTran project](https://www.helsinki.fi/en/researchgroups/natural-language-understanding-with-cross-lingual-grounding), funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 771113), and the [MeMAD project](https://memad.eu/), funded by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 780069. We are also grateful for the generous computational resources and IT infrastructure provided by [CSC -- IT Center for Science](https://www.csc.fi/), Finland.
## Model conversion info
* transformers version: 4.16.2
* OPUS-MT git hash: 1bdabf7
* port time: Thu Mar 24 03:20:20 EET 2022
* port machine: LM0-400-22516.local
|
Helsinki-NLP/opus-mt-tc-big-zlw-zle
|
Helsinki-NLP
| 2023-10-10T11:09:10Z | 117 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"be",
"cs",
"dsb",
"hsb",
"pl",
"ru",
"uk",
"zle",
"zlw",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2022-03-24T12:50:12Z |
---
language:
- be
- cs
- dsb
- hsb
- pl
- ru
- uk
- zle
- zlw
tags:
- translation
- opus-mt-tc
license: cc-by-4.0
model-index:
- name: opus-mt-tc-big-zlw-zle
results:
- task:
name: Translation ces-rus
type: translation
args: ces-rus
dataset:
name: flores101-devtest
type: flores_101
args: ces rus devtest
metrics:
- name: BLEU
type: bleu
value: 24.2
- task:
name: Translation ces-ukr
type: translation
args: ces-ukr
dataset:
name: flores101-devtest
type: flores_101
args: ces ukr devtest
metrics:
- name: BLEU
type: bleu
value: 22.9
- task:
name: Translation pol-rus
type: translation
args: pol-rus
dataset:
name: flores101-devtest
type: flores_101
args: pol rus devtest
metrics:
- name: BLEU
type: bleu
value: 20.1
- task:
name: Translation ces-rus
type: translation
args: ces-rus
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: ces-rus
metrics:
- name: BLEU
type: bleu
value: 56.4
- task:
name: Translation ces-ukr
type: translation
args: ces-ukr
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: ces-ukr
metrics:
- name: BLEU
type: bleu
value: 53.0
- task:
name: Translation pol-bel
type: translation
args: pol-bel
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: pol-bel
metrics:
- name: BLEU
type: bleu
value: 29.4
- task:
name: Translation pol-rus
type: translation
args: pol-rus
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: pol-rus
metrics:
- name: BLEU
type: bleu
value: 55.3
- task:
name: Translation pol-ukr
type: translation
args: pol-ukr
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: pol-ukr
metrics:
- name: BLEU
type: bleu
value: 48.6
- task:
name: Translation ces-rus
type: translation
args: ces-rus
dataset:
name: newstest2012
type: wmt-2012-news
args: ces-rus
metrics:
- name: BLEU
type: bleu
value: 21.0
- task:
name: Translation ces-rus
type: translation
args: ces-rus
dataset:
name: newstest2013
type: wmt-2013-news
args: ces-rus
metrics:
- name: BLEU
type: bleu
value: 27.2
---
# opus-mt-tc-big-zlw-zle
Neural machine translation model for translating from West Slavic languages (zlw) to East Slavic languages (zle).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trained using the amazing framework of [Marian NMT](https://marian-nmt.github.io/), an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is taken from [OPUS](https://opus.nlpl.eu/) and training pipelines use the procedures of [OPUS-MT-train](https://github.com/Helsinki-NLP/Opus-MT-train).
* Publications: [OPUS-MT – Building open translation services for the World](https://aclanthology.org/2020.eamt-1.61/) and [The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt-1.139/) (Please, cite if you use this model.)
```
@inproceedings{tiedemann-thottingal-2020-opus,
title = "{OPUS}-{MT} {--} Building open translation services for the World",
author = {Tiedemann, J{\"o}rg and Thottingal, Santhosh},
booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
month = nov,
year = "2020",
address = "Lisboa, Portugal",
publisher = "European Association for Machine Translation",
url = "https://aclanthology.org/2020.eamt-1.61",
pages = "479--480",
}
@inproceedings{tiedemann-2020-tatoeba,
title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}",
author = {Tiedemann, J{\"o}rg},
booktitle = "Proceedings of the Fifth Conference on Machine Translation",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.wmt-1.139",
pages = "1174--1182",
}
```
## Model info
* Release: 2022-03-19
* source language(s): ces dsb hsb pol
* target language(s): bel rus ukr
* valid target language labels: >>bel<< >>rus<< >>ukr<<
* model: transformer-big
* data: opusTCv20210807+bt ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge))
* tokenization: SentencePiece (spm32k,spm32k)
* original model: [opusTCv20210807+bt_transformer-big_2022-03-19.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/zlw-zle/opusTCv20210807+bt_transformer-big_2022-03-19.zip)
* more information released models: [OPUS-MT zlw-zle README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/zlw-zle/README.md)
* more information about the model: [MarianMT](https://huggingface.co/docs/transformers/model_doc/marian)
This is a multilingual translation model with multiple target languages. A sentence initial language token is required in the form of `>>id<<` (id = valid target language ID), e.g. `>>bel<<`
## Usage
A short example code:
```python
from transformers import MarianMTModel, MarianTokenizer
src_text = [
">>rus<< Je vystudovaný právník.",
">>rus<< Gdzie jest moja książka ?"
]
model_name = "pytorch-models/opus-mt-tc-big-zlw-zle"
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)
translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True))
for t in translated:
print( tokenizer.decode(t, skip_special_tokens=True) )
# expected output:
# Он дипломированный юрист.
# Где моя книга?
```
You can also use OPUS-MT models with the transformers pipelines, for example:
```python
from transformers import pipeline
pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-zlw-zle")
print(pipe(">>rus<< Je vystudovaný právník."))
# expected output: Он дипломированный юрист.
```
## Benchmarks
* test set translations: [opusTCv20210807+bt_transformer-big_2022-03-19.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zlw-zle/opusTCv20210807+bt_transformer-big_2022-03-19.test.txt)
* test set scores: [opusTCv20210807+bt_transformer-big_2022-03-19.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zlw-zle/opusTCv20210807+bt_transformer-big_2022-03-19.eval.txt)
* benchmark results: [benchmark_results.txt](benchmark_results.txt)
* benchmark output: [benchmark_translations.zip](benchmark_translations.zip)
| langpair | testset | chr-F | BLEU | #sent | #words |
|----------|---------|-------|-------|-------|--------|
| ces-rus | tatoeba-test-v2021-08-07 | 0.73154 | 56.4 | 2934 | 17790 |
| ces-ukr | tatoeba-test-v2021-08-07 | 0.69934 | 53.0 | 1787 | 8891 |
| pol-bel | tatoeba-test-v2021-08-07 | 0.51039 | 29.4 | 287 | 1730 |
| pol-rus | tatoeba-test-v2021-08-07 | 0.73156 | 55.3 | 3543 | 22067 |
| pol-ukr | tatoeba-test-v2021-08-07 | 0.68247 | 48.6 | 2519 | 13535 |
| ces-rus | flores101-devtest | 0.52316 | 24.2 | 1012 | 23295 |
| ces-ukr | flores101-devtest | 0.52261 | 22.9 | 1012 | 22810 |
| pol-rus | flores101-devtest | 0.49414 | 20.1 | 1012 | 23295 |
| pol-ukr | flores101-devtest | 0.48250 | 18.3 | 1012 | 22810 |
| ces-rus | newstest2012 | 0.49469 | 21.0 | 3003 | 64790 |
| ces-rus | newstest2013 | 0.54197 | 27.2 | 3000 | 58560 |
## Acknowledgements
The work is supported by the [European Language Grid](https://www.european-language-grid.eu/) as [pilot project 2866](https://live.european-language-grid.eu/catalogue/#/resource/projects/2866), by the [FoTran project](https://www.helsinki.fi/en/researchgroups/natural-language-understanding-with-cross-lingual-grounding), funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 771113), and the [MeMAD project](https://memad.eu/), funded by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 780069. We are also grateful for the generous computational resources and IT infrastructure provided by [CSC -- IT Center for Science](https://www.csc.fi/), Finland.
## Model conversion info
* transformers version: 4.16.2
* OPUS-MT git hash: 1bdabf7
* port time: Thu Mar 24 04:13:23 EET 2022
* port machine: LM0-400-22516.local
|
anders0204/poca-SoccerTwos
|
anders0204
| 2023-10-10T11:08:21Z | 7 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"SoccerTwos",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
] |
reinforcement-learning
| 2023-10-10T11:08:14Z |
---
library_name: ml-agents
tags:
- SoccerTwos
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: anders0204/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Helsinki-NLP/opus-mt-tc-big-fi-zls
|
Helsinki-NLP
| 2023-10-10T11:08:02Z | 119 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"bg",
"fi",
"hr",
"sl",
"sr",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2022-08-12T16:46:33Z |
---
language:
- bg
- fi
- hr
- sl
- sr
language_bcp47:
- sr_Cyrl
tags:
- translation
- opus-mt-tc
license: cc-by-4.0
model-index:
- name: opus-mt-tc-big-fi-zls
results:
- task:
name: Translation fin-bul
type: translation
args: fin-bul
dataset:
name: flores101-devtest
type: flores_101
args: fin bul devtest
metrics:
- name: BLEU
type: bleu
value: 26.2
- name: chr-F
type: chrf
value: 0.54912
- task:
name: Translation fin-hrv
type: translation
args: fin-hrv
dataset:
name: flores101-devtest
type: flores_101
args: fin hrv devtest
metrics:
- name: BLEU
type: bleu
value: 21.3
- name: chr-F
type: chrf
value: 0.51468
- task:
name: Translation fin-slv
type: translation
args: fin-slv
dataset:
name: flores101-devtest
type: flores_101
args: fin slv devtest
metrics:
- name: BLEU
type: bleu
value: 22.3
- name: chr-F
type: chrf
value: 0.51226
- task:
name: Translation fin-srp_Cyrl
type: translation
args: fin-srp_Cyrl
dataset:
name: flores101-devtest
type: flores_101
args: fin srp_Cyrl devtest
metrics:
- name: BLEU
type: bleu
value: 21.8
- name: chr-F
type: chrf
value: 0.50774
---
# opus-mt-tc-big-fi-zls
## Table of Contents
- [Model Details](#model-details)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [How to Get Started With the Model](#how-to-get-started-with-the-model)
- [Training](#training)
- [Evaluation](#evaluation)
- [Citation Information](#citation-information)
- [Acknowledgements](#acknowledgements)
## Model Details
Neural machine translation model for translating from Finnish (fi) to South Slavic languages (zls).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trained using the amazing framework of [Marian NMT](https://marian-nmt.github.io/), an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is taken from [OPUS](https://opus.nlpl.eu/) and training pipelines use the procedures of [OPUS-MT-train](https://github.com/Helsinki-NLP/Opus-MT-train).
**Model Description:**
- **Developed by:** Language Technology Research Group at the University of Helsinki
- **Model Type:** Translation (transformer-big)
- **Release**: 2022-07-23
- **License:** CC-BY-4.0
- **Language(s):**
- Source Language(s): fin
- Target Language(s): bul hrv slv srp_Cyrl
- Language Pair(s): fin-bul fin-hrv fin-slv fin-srp_Cyrl
- Valid Target Language Labels: >>bos<< >>bos_Cyrl<< >>bos_Latn<< >>bul<< >>chu<< >>hbs<< >>hbs_Cyrl<< >>hrv<< >>kjv<< >>mkd<< >>slv<< >>srp<< >>srp_Cyrl<< >>srp_Latn<< >>svm<<
- **Original Model**: [opusTCv20210807_transformer-big_2022-07-23.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/fin-zls/opusTCv20210807_transformer-big_2022-07-23.zip)
- **Resources for more information:**
- [OPUS-MT-train GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train)
- More information about released models for this language pair: [OPUS-MT fin-zls README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/fin-zls/README.md)
- [More information about MarianNMT models in the transformers library](https://huggingface.co/docs/transformers/model_doc/marian)
- [Tatoeba Translation Challenge](https://github.com/Helsinki-NLP/Tatoeba-Challenge/
This is a multilingual translation model with multiple target languages. A sentence initial language token is required in the form of `>>id<<` (id = valid target language ID), e.g. `>>slv<<`
## Uses
This model can be used for translation and text-to-text generation.
## Risks, Limitations and Biases
**CONTENT WARNING: Readers should be aware that the model is trained on various public data sets that may contain content that is disturbing, offensive, and can propagate historical and current stereotypes.**
Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)).
## How to Get Started With the Model
A short example code:
```python
from transformers import MarianMTModel, MarianTokenizer
src_text = [
">>bul<< Ajattelen vain sinua.",
">>slv<< Virtahevot rakastavat vettä."
]
model_name = "pytorch-models/opus-mt-tc-big-fi-zls"
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)
translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True))
for t in translated:
print( tokenizer.decode(t, skip_special_tokens=True) )
# expected output:
# Мисля само за теб.
# Povodni konji obožujejo vodo.
```
You can also use OPUS-MT models with the transformers pipelines, for example:
```python
from transformers import pipeline
pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-fi-zls")
print(pipe(">>bul<< Ajattelen vain sinua."))
# expected output: Мисля само за теб.
```
## Training
- **Data**: opusTCv20210807 ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge))
- **Pre-processing**: SentencePiece (spm32k,spm32k)
- **Model Type:** transformer-big
- **Original MarianNMT Model**: [opusTCv20210807_transformer-big_2022-07-23.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/fin-zls/opusTCv20210807_transformer-big_2022-07-23.zip)
- **Training Scripts**: [GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train)
## Evaluation
* test set translations: [opusTCv20210807_transformer-big_2022-07-23.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/fin-zls/opusTCv20210807_transformer-big_2022-07-23.test.txt)
* test set scores: [opusTCv20210807_transformer-big_2022-07-23.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/fin-zls/opusTCv20210807_transformer-big_2022-07-23.eval.txt)
* benchmark results: [benchmark_results.txt](benchmark_results.txt)
* benchmark output: [benchmark_translations.zip](benchmark_translations.zip)
| langpair | testset | chr-F | BLEU | #sent | #words |
|----------|---------|-------|-------|-------|--------|
| fin-bul | flores101-devtest | 0.54912 | 26.2 | 1012 | 24700 |
| fin-hrv | flores101-devtest | 0.51468 | 21.3 | 1012 | 22423 |
| fin-slv | flores101-devtest | 0.51226 | 22.3 | 1012 | 23425 |
| fin-srp_Cyrl | flores101-devtest | 0.50774 | 21.8 | 1012 | 23456 |
## Citation Information
* Publications: [OPUS-MT – Building open translation services for the World](https://aclanthology.org/2020.eamt-1.61/) and [The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt-1.139/) (Please, cite if you use this model.)
```
@inproceedings{tiedemann-thottingal-2020-opus,
title = "{OPUS}-{MT} {--} Building open translation services for the World",
author = {Tiedemann, J{\"o}rg and Thottingal, Santhosh},
booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
month = nov,
year = "2020",
address = "Lisboa, Portugal",
publisher = "European Association for Machine Translation",
url = "https://aclanthology.org/2020.eamt-1.61",
pages = "479--480",
}
@inproceedings{tiedemann-2020-tatoeba,
title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}",
author = {Tiedemann, J{\"o}rg},
booktitle = "Proceedings of the Fifth Conference on Machine Translation",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.wmt-1.139",
pages = "1174--1182",
}
```
## Acknowledgements
The work is supported by the [European Language Grid](https://www.european-language-grid.eu/) as [pilot project 2866](https://live.european-language-grid.eu/catalogue/#/resource/projects/2866), by the [FoTran project](https://www.helsinki.fi/en/researchgroups/natural-language-understanding-with-cross-lingual-grounding), funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 771113), and the [MeMAD project](https://memad.eu/), funded by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 780069. We are also grateful for the generous computational resources and IT infrastructure provided by [CSC -- IT Center for Science](https://www.csc.fi/), Finland.
## Model conversion info
* transformers version: 4.16.2
* OPUS-MT git hash: 8b9f0b0
* port time: Sat Aug 13 00:08:29 EEST 2022
* port machine: LM0-400-22516.local
|
Azma-AI/roberta-base-emotion-classifier
|
Azma-AI
| 2023-10-10T11:05:56Z | 108 | 1 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"emotions",
"multi-class-classification",
"multi-label-classification",
"en",
"dataset:go_emotions",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-10-10T11:02:32Z |
---
language: en
tags:
- text-classification
- pytorch
- roberta
- emotions
- multi-class-classification
- multi-label-classification
datasets:
- go_emotions
license: mit
widget:
- text: "I am not having a great day."
---
Model trained from [roberta-base](https://huggingface.co/roberta-base) on the [go_emotions](https://huggingface.co/datasets/go_emotions) dataset for multi-label classification.
[go_emotions](https://huggingface.co/datasets/go_emotions) is based on Reddit data and has 28 labels. It is a multi-label dataset where one or multiple labels may apply for any given input text, hence this model is a multi-label classification model with 28 'probability' float outputs for any given input text. Typically a threshold of 0.5 is applied to the probabilities for the prediction for each label.
The model was trained using `AutoModelForSequenceClassification.from_pretrained` with `problem_type="multi_label_classification"` for 3 epochs with a learning rate of 2e-5 and weight decay of 0.01.
Evaluation (of the 28 dim output via a threshold of 0.5 to binarize each) using the dataset test split gives:
- Micro F1 0.585
- ROC AUC 0.751
- Accuracy 0.474
But the metrics would be more meaningful when measured per label given the multi-label nature.
Additionally some labels (E.g. `gratitude`) when considered independently perform very strongly with F1 around 0.9, whilst others (E.g. `relief`) perform very poorly. This is a challenging dataset. Labels such as `relief` do have much fewer examples in the training data (less than 100 out of the 40k+), but there is also some ambiguity and/or labelling errors visible in the training data of `go_emotions` that is suspected to constrain the performance.
|
Helsinki-NLP/opus-mt-tc-big-itc-tr
|
Helsinki-NLP
| 2023-10-10T11:04:52Z | 113 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"ca",
"es",
"fr",
"gl",
"it",
"oc",
"pt",
"ro",
"tr",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2022-08-12T13:25:33Z |
---
language:
- ca
- es
- fr
- gl
- it
- oc
- pt
- ro
- tr
tags:
- translation
- opus-mt-tc
license: cc-by-4.0
model-index:
- name: opus-mt-tc-big-itc-tr
results:
- task:
name: Translation cat-tur
type: translation
args: cat-tur
dataset:
name: flores101-devtest
type: flores_101
args: cat tur devtest
metrics:
- name: BLEU
type: bleu
value: 21.7
- name: chr-F
type: chrf
value: 0.54892
- task:
name: Translation fra-tur
type: translation
args: fra-tur
dataset:
name: flores101-devtest
type: flores_101
args: fra tur devtest
metrics:
- name: BLEU
type: bleu
value: 21.7
- name: chr-F
type: chrf
value: 0.55342
- task:
name: Translation glg-tur
type: translation
args: glg-tur
dataset:
name: flores101-devtest
type: flores_101
args: glg tur devtest
metrics:
- name: BLEU
type: bleu
value: 20.6
- name: chr-F
type: chrf
value: 0.53936
- task:
name: Translation ita-tur
type: translation
args: ita-tur
dataset:
name: flores101-devtest
type: flores_101
args: ita tur devtest
metrics:
- name: BLEU
type: bleu
value: 18.4
- name: chr-F
type: chrf
value: 0.52842
- task:
name: Translation oci-tur
type: translation
args: oci-tur
dataset:
name: flores101-devtest
type: flores_101
args: oci tur devtest
metrics:
- name: BLEU
type: bleu
value: 17.6
- name: chr-F
type: chrf
value: 0.50618
- task:
name: Translation por-tur
type: translation
args: por-tur
dataset:
name: flores101-devtest
type: flores_101
args: por tur devtest
metrics:
- name: BLEU
type: bleu
value: 23.5
- name: chr-F
type: chrf
value: 0.56396
- task:
name: Translation ron-tur
type: translation
args: ron-tur
dataset:
name: flores101-devtest
type: flores_101
args: ron tur devtest
metrics:
- name: BLEU
type: bleu
value: 21.5
- name: chr-F
type: chrf
value: 0.55409
- task:
name: Translation spa-tur
type: translation
args: spa-tur
dataset:
name: flores101-devtest
type: flores_101
args: spa tur devtest
metrics:
- name: BLEU
type: bleu
value: 16.5
- name: chr-F
type: chrf
value: 0.51066
- task:
name: Translation fra-tur
type: translation
args: fra-tur
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: fra-tur
metrics:
- name: BLEU
type: bleu
value: 34.8
- name: chr-F
type: chrf
value: 0.63006
- task:
name: Translation ita-tur
type: translation
args: ita-tur
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: ita-tur
metrics:
- name: BLEU
type: bleu
value: 34.9
- name: chr-F
type: chrf
value: 0.59991
- task:
name: Translation por-tur
type: translation
args: por-tur
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: por-tur
metrics:
- name: BLEU
type: bleu
value: 40.1
- name: chr-F
type: chrf
value: 0.67836
- task:
name: Translation ron-tur
type: translation
args: ron-tur
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: ron-tur
metrics:
- name: BLEU
type: bleu
value: 35.5
- name: chr-F
type: chrf
value: 0.64031
- task:
name: Translation spa-tur
type: translation
args: spa-tur
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: spa-tur
metrics:
- name: BLEU
type: bleu
value: 45.2
- name: chr-F
type: chrf
value: 0.71524
---
# opus-mt-tc-big-itc-tr
## Table of Contents
- [Model Details](#model-details)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [How to Get Started With the Model](#how-to-get-started-with-the-model)
- [Training](#training)
- [Evaluation](#evaluation)
- [Citation Information](#citation-information)
- [Acknowledgements](#acknowledgements)
## Model Details
Neural machine translation model for translating from Italic languages (itc) to Turkish (tr).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trained using the amazing framework of [Marian NMT](https://marian-nmt.github.io/), an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is taken from [OPUS](https://opus.nlpl.eu/) and training pipelines use the procedures of [OPUS-MT-train](https://github.com/Helsinki-NLP/Opus-MT-train).
**Model Description:**
- **Developed by:** Language Technology Research Group at the University of Helsinki
- **Model Type:** Translation (transformer-big)
- **Release**: 2022-07-28
- **License:** CC-BY-4.0
- **Language(s):**
- Source Language(s): cat fra glg ita lad lad_Latn oci por ron spa
- Target Language(s): tur
- Language Pair(s): cat-tur fra-tur glg-tur ita-tur oci-tur por-tur ron-tur spa-tur
- Valid Target Language Labels:
- **Original Model**: [opusTCv20210807_transformer-big_2022-07-28.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/itc-tur/opusTCv20210807_transformer-big_2022-07-28.zip)
- **Resources for more information:**
- [OPUS-MT-train GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train)
- More information about released models for this language pair: [OPUS-MT itc-tur README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/itc-tur/README.md)
- [More information about MarianNMT models in the transformers library](https://huggingface.co/docs/transformers/model_doc/marian)
- [Tatoeba Translation Challenge](https://github.com/Helsinki-NLP/Tatoeba-Challenge/
## Uses
This model can be used for translation and text-to-text generation.
## Risks, Limitations and Biases
**CONTENT WARNING: Readers should be aware that the model is trained on various public data sets that may contain content that is disturbing, offensive, and can propagate historical and current stereotypes.**
Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)).
## How to Get Started With the Model
A short example code:
```python
from transformers import MarianMTModel, MarianTokenizer
src_text = [
""Di che nazionalità sono le tue dottoresse?" "Malese."",
""Di che nazionalità sono i nostri amici?" "Maltese.""
]
model_name = "pytorch-models/opus-mt-tc-big-itc-tr"
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)
translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True))
for t in translated:
print( tokenizer.decode(t, skip_special_tokens=True) )
# expected output:
# "Doktorların hangi milletten?" "Malezyalı."
# "Arkadaşlarımız hangi milletten?" "Maltalı."
```
You can also use OPUS-MT models with the transformers pipelines, for example:
```python
from transformers import pipeline
pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-itc-tr")
print(pipe(""Di che nazionalità sono le tue dottoresse?" "Malese.""))
# expected output: "Doktorların hangi milletten?" "Malezyalı."
```
## Training
- **Data**: opusTCv20210807 ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge))
- **Pre-processing**: SentencePiece (spm32k,spm32k)
- **Model Type:** transformer-big
- **Original MarianNMT Model**: [opusTCv20210807_transformer-big_2022-07-28.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/itc-tur/opusTCv20210807_transformer-big_2022-07-28.zip)
- **Training Scripts**: [GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train)
## Evaluation
* test set translations: [opusTCv20210807_transformer-big_2022-07-28.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/itc-tur/opusTCv20210807_transformer-big_2022-07-28.test.txt)
* test set scores: [opusTCv20210807_transformer-big_2022-07-28.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/itc-tur/opusTCv20210807_transformer-big_2022-07-28.eval.txt)
* benchmark results: [benchmark_results.txt](benchmark_results.txt)
* benchmark output: [benchmark_translations.zip](benchmark_translations.zip)
| langpair | testset | chr-F | BLEU | #sent | #words |
|----------|---------|-------|-------|-------|--------|
| fra-tur | tatoeba-test-v2021-08-07 | 0.63006 | 34.8 | 2582 | 14307 |
| ita-tur | tatoeba-test-v2021-08-07 | 0.59991 | 34.9 | 10000 | 75807 |
| por-tur | tatoeba-test-v2021-08-07 | 0.67836 | 40.1 | 1794 | 9312 |
| ron-tur | tatoeba-test-v2021-08-07 | 0.64031 | 35.5 | 2460 | 13788 |
| spa-tur | tatoeba-test-v2021-08-07 | 0.71524 | 45.2 | 10615 | 56099 |
| cat-tur | flores101-devtest | 0.54892 | 21.7 | 1012 | 20253 |
| fra-tur | flores101-devtest | 0.55342 | 21.7 | 1012 | 20253 |
| glg-tur | flores101-devtest | 0.53936 | 20.6 | 1012 | 20253 |
| ita-tur | flores101-devtest | 0.52842 | 18.4 | 1012 | 20253 |
| oci-tur | flores101-devtest | 0.50618 | 17.6 | 1012 | 20253 |
| por-tur | flores101-devtest | 0.56396 | 23.5 | 1012 | 20253 |
| ron-tur | flores101-devtest | 0.55409 | 21.5 | 1012 | 20253 |
| spa-tur | flores101-devtest | 0.51066 | 16.5 | 1012 | 20253 |
## Citation Information
* Publications: [OPUS-MT – Building open translation services for the World](https://aclanthology.org/2020.eamt-1.61/) and [The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt-1.139/) (Please, cite if you use this model.)
```
@inproceedings{tiedemann-thottingal-2020-opus,
title = "{OPUS}-{MT} {--} Building open translation services for the World",
author = {Tiedemann, J{\"o}rg and Thottingal, Santhosh},
booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
month = nov,
year = "2020",
address = "Lisboa, Portugal",
publisher = "European Association for Machine Translation",
url = "https://aclanthology.org/2020.eamt-1.61",
pages = "479--480",
}
@inproceedings{tiedemann-2020-tatoeba,
title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}",
author = {Tiedemann, J{\"o}rg},
booktitle = "Proceedings of the Fifth Conference on Machine Translation",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.wmt-1.139",
pages = "1174--1182",
}
```
## Acknowledgements
The work is supported by the [European Language Grid](https://www.european-language-grid.eu/) as [pilot project 2866](https://live.european-language-grid.eu/catalogue/#/resource/projects/2866), by the [FoTran project](https://www.helsinki.fi/en/researchgroups/natural-language-understanding-with-cross-lingual-grounding), funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 771113), and the [MeMAD project](https://memad.eu/), funded by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 780069. We are also grateful for the generous computational resources and IT infrastructure provided by [CSC -- IT Center for Science](https://www.csc.fi/), Finland.
## Model conversion info
* transformers version: 4.16.2
* OPUS-MT git hash: 8b9f0b0
* port time: Sat Aug 13 00:03:26 EEST 2022
* port machine: LM0-400-22516.local
|
Helsinki-NLP/opus-mt-tc-big-zls-de
|
Helsinki-NLP
| 2023-10-10T10:57:17Z | 142 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"bg",
"de",
"hr",
"mk",
"sh",
"sl",
"sr",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2022-08-12T14:51:09Z |
---
language:
- bg
- de
- hr
- mk
- sh
- sl
- sr
language_bcp47:
- sr_Cyrl
- sr_Latn
tags:
- translation
- opus-mt-tc
license: cc-by-4.0
model-index:
- name: opus-mt-tc-big-zls-de
results:
- task:
name: Translation bul-deu
type: translation
args: bul-deu
dataset:
name: flores101-devtest
type: flores_101
args: bul deu devtest
metrics:
- name: BLEU
type: bleu
value: 28.4
- name: chr-F
type: chrf
value: 0.57688
- task:
name: Translation hrv-deu
type: translation
args: hrv-deu
dataset:
name: flores101-devtest
type: flores_101
args: hrv deu devtest
metrics:
- name: BLEU
type: bleu
value: 27.4
- name: chr-F
type: chrf
value: 0.56674
- task:
name: Translation mkd-deu
type: translation
args: mkd-deu
dataset:
name: flores101-devtest
type: flores_101
args: mkd deu devtest
metrics:
- name: BLEU
type: bleu
value: 29.3
- name: chr-F
type: chrf
value: 0.57688
- task:
name: Translation slv-deu
type: translation
args: slv-deu
dataset:
name: flores101-devtest
type: flores_101
args: slv deu devtest
metrics:
- name: BLEU
type: bleu
value: 26.7
- name: chr-F
type: chrf
value: 0.56258
- task:
name: Translation srp_Cyrl-deu
type: translation
args: srp_Cyrl-deu
dataset:
name: flores101-devtest
type: flores_101
args: srp_Cyrl deu devtest
metrics:
- name: BLEU
type: bleu
value: 30.7
- name: chr-F
type: chrf
value: 0.59271
- task:
name: Translation bul-deu
type: translation
args: bul-deu
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: bul-deu
metrics:
- name: BLEU
type: bleu
value: 54.5
- name: chr-F
type: chrf
value: 0.71220
- task:
name: Translation hbs-deu
type: translation
args: hbs-deu
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: hbs-deu
metrics:
- name: BLEU
type: bleu
value: 54.8
- name: chr-F
type: chrf
value: 0.71283
- task:
name: Translation hrv-deu
type: translation
args: hrv-deu
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: hrv-deu
metrics:
- name: BLEU
type: bleu
value: 53.1
- name: chr-F
type: chrf
value: 0.69448
- task:
name: Translation slv-deu
type: translation
args: slv-deu
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: slv-deu
metrics:
- name: BLEU
type: bleu
value: 21.1
- name: chr-F
type: chrf
value: 0.36339
- task:
name: Translation srp_Latn-deu
type: translation
args: srp_Latn-deu
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: srp_Latn-deu
metrics:
- name: BLEU
type: bleu
value: 56.0
- name: chr-F
type: chrf
value: 0.72489
---
# opus-mt-tc-big-zls-de
## Table of Contents
- [Model Details](#model-details)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [How to Get Started With the Model](#how-to-get-started-with-the-model)
- [Training](#training)
- [Evaluation](#evaluation)
- [Citation Information](#citation-information)
- [Acknowledgements](#acknowledgements)
## Model Details
Neural machine translation model for translating from South Slavic languages (zls) to German (de).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trained using the amazing framework of [Marian NMT](https://marian-nmt.github.io/), an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is taken from [OPUS](https://opus.nlpl.eu/) and training pipelines use the procedures of [OPUS-MT-train](https://github.com/Helsinki-NLP/Opus-MT-train).
**Model Description:**
- **Developed by:** Language Technology Research Group at the University of Helsinki
- **Model Type:** Translation (transformer-big)
- **Release**: 2022-07-26
- **License:** CC-BY-4.0
- **Language(s):**
- Source Language(s): bos_Latn bul hbs hrv mkd slv srp_Cyrl srp_Latn
- Target Language(s): deu
- Language Pair(s): bul-deu hbs-deu hrv-deu mkd-deu slv-deu srp_Cyrl-deu srp_Latn-deu
- Valid Target Language Labels:
- **Original Model**: [opusTCv20210807_transformer-big_2022-07-26.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/zls-deu/opusTCv20210807_transformer-big_2022-07-26.zip)
- **Resources for more information:**
- [OPUS-MT-train GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train)
- More information about released models for this language pair: [OPUS-MT zls-deu README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/zls-deu/README.md)
- [More information about MarianNMT models in the transformers library](https://huggingface.co/docs/transformers/model_doc/marian)
- [Tatoeba Translation Challenge](https://github.com/Helsinki-NLP/Tatoeba-Challenge/
## Uses
This model can be used for translation and text-to-text generation.
## Risks, Limitations and Biases
**CONTENT WARNING: Readers should be aware that the model is trained on various public data sets that may contain content that is disturbing, offensive, and can propagate historical and current stereotypes.**
Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)).
## How to Get Started With the Model
A short example code:
```python
from transformers import MarianMTModel, MarianTokenizer
src_text = [
"Jesi li ti student?",
"Dve stvari deca treba da dobiju od svojih roditelja: korene i krila."
]
model_name = "pytorch-models/opus-mt-tc-big-zls-de"
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)
translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True))
for t in translated:
print( tokenizer.decode(t, skip_special_tokens=True) )
# expected output:
# Sind Sie Student?
# Zwei Dinge sollten Kinder von ihren Eltern bekommen: Wurzeln und Flügel.
```
You can also use OPUS-MT models with the transformers pipelines, for example:
```python
from transformers import pipeline
pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-zls-de")
print(pipe("Jesi li ti student?"))
# expected output: Sind Sie Student?
```
## Training
- **Data**: opusTCv20210807 ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge))
- **Pre-processing**: SentencePiece (spm32k,spm32k)
- **Model Type:** transformer-big
- **Original MarianNMT Model**: [opusTCv20210807_transformer-big_2022-07-26.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/zls-deu/opusTCv20210807_transformer-big_2022-07-26.zip)
- **Training Scripts**: [GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train)
## Evaluation
* test set translations: [opusTCv20210807_transformer-big_2022-07-26.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zls-deu/opusTCv20210807_transformer-big_2022-07-26.test.txt)
* test set scores: [opusTCv20210807_transformer-big_2022-07-26.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zls-deu/opusTCv20210807_transformer-big_2022-07-26.eval.txt)
* benchmark results: [benchmark_results.txt](benchmark_results.txt)
* benchmark output: [benchmark_translations.zip](benchmark_translations.zip)
| langpair | testset | chr-F | BLEU | #sent | #words |
|----------|---------|-------|-------|-------|--------|
| bul-deu | tatoeba-test-v2021-08-07 | 0.71220 | 54.5 | 314 | 2224 |
| hbs-deu | tatoeba-test-v2021-08-07 | 0.71283 | 54.8 | 1959 | 15559 |
| hrv-deu | tatoeba-test-v2021-08-07 | 0.69448 | 53.1 | 782 | 5734 |
| slv-deu | tatoeba-test-v2021-08-07 | 0.36339 | 21.1 | 492 | 3003 |
| srp_Latn-deu | tatoeba-test-v2021-08-07 | 0.72489 | 56.0 | 986 | 8500 |
| bul-deu | flores101-devtest | 0.57688 | 28.4 | 1012 | 25094 |
| hrv-deu | flores101-devtest | 0.56674 | 27.4 | 1012 | 25094 |
| mkd-deu | flores101-devtest | 0.57688 | 29.3 | 1012 | 25094 |
| slv-deu | flores101-devtest | 0.56258 | 26.7 | 1012 | 25094 |
| srp_Cyrl-deu | flores101-devtest | 0.59271 | 30.7 | 1012 | 25094 |
## Citation Information
* Publications: [OPUS-MT – Building open translation services for the World](https://aclanthology.org/2020.eamt-1.61/) and [The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt-1.139/) (Please, cite if you use this model.)
```
@inproceedings{tiedemann-thottingal-2020-opus,
title = "{OPUS}-{MT} {--} Building open translation services for the World",
author = {Tiedemann, J{\"o}rg and Thottingal, Santhosh},
booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
month = nov,
year = "2020",
address = "Lisboa, Portugal",
publisher = "European Association for Machine Translation",
url = "https://aclanthology.org/2020.eamt-1.61",
pages = "479--480",
}
@inproceedings{tiedemann-2020-tatoeba,
title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}",
author = {Tiedemann, J{\"o}rg},
booktitle = "Proceedings of the Fifth Conference on Machine Translation",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.wmt-1.139",
pages = "1174--1182",
}
```
## Acknowledgements
The work is supported by the [European Language Grid](https://www.european-language-grid.eu/) as [pilot project 2866](https://live.european-language-grid.eu/catalogue/#/resource/projects/2866), by the [FoTran project](https://www.helsinki.fi/en/researchgroups/natural-language-understanding-with-cross-lingual-grounding), funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 771113), and the [MeMAD project](https://memad.eu/), funded by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 780069. We are also grateful for the generous computational resources and IT infrastructure provided by [CSC -- IT Center for Science](https://www.csc.fi/), Finland.
## Model conversion info
* transformers version: 4.16.2
* OPUS-MT git hash: 8b9f0b0
* port time: Sat Aug 13 00:05:30 EEST 2022
* port machine: LM0-400-22516.local
|
Helsinki-NLP/opus-mt-tc-big-gmq-en
|
Helsinki-NLP
| 2023-10-10T10:53:03Z | 130 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"tc",
"big",
"gmq",
"en",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2022-04-13T16:13:21Z |
---
language:
- da
- en
- fo
- gmq
- is
- nb
- nn
- false
- sv
tags:
- translation
- opus-mt-tc
license: cc-by-4.0
model-index:
- name: opus-mt-tc-big-gmq-en
results:
- task:
name: Translation dan-eng
type: translation
args: dan-eng
dataset:
name: flores101-devtest
type: flores_101
args: dan eng devtest
metrics:
- name: BLEU
type: bleu
value: 49.3
- task:
name: Translation isl-eng
type: translation
args: isl-eng
dataset:
name: flores101-devtest
type: flores_101
args: isl eng devtest
metrics:
- name: BLEU
type: bleu
value: 34.2
- task:
name: Translation nob-eng
type: translation
args: nob-eng
dataset:
name: flores101-devtest
type: flores_101
args: nob eng devtest
metrics:
- name: BLEU
type: bleu
value: 44.2
- task:
name: Translation swe-eng
type: translation
args: swe-eng
dataset:
name: flores101-devtest
type: flores_101
args: swe eng devtest
metrics:
- name: BLEU
type: bleu
value: 49.8
- task:
name: Translation isl-eng
type: translation
args: isl-eng
dataset:
name: newsdev2021.is-en
type: newsdev2021.is-en
args: isl-eng
metrics:
- name: BLEU
type: bleu
value: 30.4
- task:
name: Translation dan-eng
type: translation
args: dan-eng
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: dan-eng
metrics:
- name: BLEU
type: bleu
value: 65.9
- task:
name: Translation fao-eng
type: translation
args: fao-eng
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: fao-eng
metrics:
- name: BLEU
type: bleu
value: 30.1
- task:
name: Translation isl-eng
type: translation
args: isl-eng
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: isl-eng
metrics:
- name: BLEU
type: bleu
value: 53.3
- task:
name: Translation nno-eng
type: translation
args: nno-eng
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: nno-eng
metrics:
- name: BLEU
type: bleu
value: 56.1
- task:
name: Translation nob-eng
type: translation
args: nob-eng
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: nob-eng
metrics:
- name: BLEU
type: bleu
value: 60.2
- task:
name: Translation swe-eng
type: translation
args: swe-eng
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: swe-eng
metrics:
- name: BLEU
type: bleu
value: 66.4
- task:
name: Translation isl-eng
type: translation
args: isl-eng
dataset:
name: newstest2021.is-en
type: wmt-2021-news
args: isl-eng
metrics:
- name: BLEU
type: bleu
value: 34.4
---
# opus-mt-tc-big-gmq-en
Neural machine translation model for translating from North Germanic languages (gmq) to English (en).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trained using the amazing framework of [Marian NMT](https://marian-nmt.github.io/), an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is taken from [OPUS](https://opus.nlpl.eu/) and training pipelines use the procedures of [OPUS-MT-train](https://github.com/Helsinki-NLP/Opus-MT-train).
* Publications: [OPUS-MT – Building open translation services for the World](https://aclanthology.org/2020.eamt-1.61/) and [The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt-1.139/) (Please, cite if you use this model.)
```
@inproceedings{tiedemann-thottingal-2020-opus,
title = "{OPUS}-{MT} {--} Building open translation services for the World",
author = {Tiedemann, J{\"o}rg and Thottingal, Santhosh},
booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
month = nov,
year = "2020",
address = "Lisboa, Portugal",
publisher = "European Association for Machine Translation",
url = "https://aclanthology.org/2020.eamt-1.61",
pages = "479--480",
}
@inproceedings{tiedemann-2020-tatoeba,
title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}",
author = {Tiedemann, J{\"o}rg},
booktitle = "Proceedings of the Fifth Conference on Machine Translation",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.wmt-1.139",
pages = "1174--1182",
}
```
## Model info
* Release: 2022-03-09
* source language(s): dan fao isl nno nob nor swe
* target language(s): eng
* model: transformer-big
* data: opusTCv20210807+bt ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge))
* tokenization: SentencePiece (spm32k,spm32k)
* original model: [opusTCv20210807+bt_transformer-big_2022-03-09.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/gmq-eng/opusTCv20210807+bt_transformer-big_2022-03-09.zip)
* more information released models: [OPUS-MT gmq-eng README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/gmq-eng/README.md)
## Usage
A short example code:
```python
from transformers import MarianMTModel, MarianTokenizer
src_text = [
"Han var synligt nervøs.",
"Inte ens Tom själv var övertygad."
]
model_name = "pytorch-models/opus-mt-tc-big-gmq-en"
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)
translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True))
for t in translated:
print( tokenizer.decode(t, skip_special_tokens=True) )
# expected output:
# He was visibly nervous.
# Even Tom was not convinced.
```
You can also use OPUS-MT models with the transformers pipelines, for example:
```python
from transformers import pipeline
pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-gmq-en")
print(pipe("Han var synligt nervøs."))
# expected output: He was visibly nervous.
```
## Benchmarks
* test set translations: [opusTCv20210807+bt_transformer-big_2022-03-09.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/gmq-eng/opusTCv20210807+bt_transformer-big_2022-03-09.test.txt)
* test set scores: [opusTCv20210807+bt_transformer-big_2022-03-09.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/gmq-eng/opusTCv20210807+bt_transformer-big_2022-03-09.eval.txt)
* benchmark results: [benchmark_results.txt](benchmark_results.txt)
* benchmark output: [benchmark_translations.zip](benchmark_translations.zip)
| langpair | testset | chr-F | BLEU | #sent | #words |
|----------|---------|-------|-------|-------|--------|
| dan-eng | tatoeba-test-v2021-08-07 | 0.78292 | 65.9 | 10795 | 79684 |
| fao-eng | tatoeba-test-v2021-08-07 | 0.47467 | 30.1 | 294 | 1984 |
| isl-eng | tatoeba-test-v2021-08-07 | 0.68346 | 53.3 | 2503 | 19788 |
| nno-eng | tatoeba-test-v2021-08-07 | 0.69788 | 56.1 | 460 | 3524 |
| nob-eng | tatoeba-test-v2021-08-07 | 0.73524 | 60.2 | 4539 | 36823 |
| swe-eng | tatoeba-test-v2021-08-07 | 0.77665 | 66.4 | 10362 | 68513 |
| dan-eng | flores101-devtest | 0.72322 | 49.3 | 1012 | 24721 |
| isl-eng | flores101-devtest | 0.59616 | 34.2 | 1012 | 24721 |
| nob-eng | flores101-devtest | 0.68224 | 44.2 | 1012 | 24721 |
| swe-eng | flores101-devtest | 0.72042 | 49.8 | 1012 | 24721 |
| isl-eng | newsdev2021.is-en | 0.56709 | 30.4 | 2004 | 46383 |
| isl-eng | newstest2021.is-en | 0.57756 | 34.4 | 1000 | 22529 |
## Acknowledgements
The work is supported by the [European Language Grid](https://www.european-language-grid.eu/) as [pilot project 2866](https://live.european-language-grid.eu/catalogue/#/resource/projects/2866), by the [FoTran project](https://www.helsinki.fi/en/researchgroups/natural-language-understanding-with-cross-lingual-grounding), funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 771113), and the [MeMAD project](https://memad.eu/), funded by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 780069. We are also grateful for the generous computational resources and IT infrastructure provided by [CSC -- IT Center for Science](https://www.csc.fi/), Finland.
## Model conversion info
* transformers version: 4.16.2
* OPUS-MT git hash: 3405783
* port time: Wed Apr 13 19:13:11 EEST 2022
* port machine: LM0-400-22516.local
|
Helsinki-NLP/opus-mt-tc-big-en-lv
|
Helsinki-NLP
| 2023-10-10T10:50:52Z | 164 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"en",
"lv",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2022-04-13T14:36:12Z |
---
language:
- en
- lv
tags:
- translation
- opus-mt-tc
license: cc-by-4.0
model-index:
- name: opus-mt-tc-big-en-lv
results:
- task:
name: Translation eng-lav
type: translation
args: eng-lav
dataset:
name: flores101-devtest
type: flores_101
args: eng lav devtest
metrics:
- name: BLEU
type: bleu
value: 30.1
- task:
name: Translation eng-lav
type: translation
args: eng-lav
dataset:
name: newsdev2017
type: newsdev2017
args: eng-lav
metrics:
- name: BLEU
type: bleu
value: 28.9
- task:
name: Translation eng-lav
type: translation
args: eng-lav
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: eng-lav
metrics:
- name: BLEU
type: bleu
value: 44.0
- task:
name: Translation eng-lav
type: translation
args: eng-lav
dataset:
name: newstest2017
type: wmt-2017-news
args: eng-lav
metrics:
- name: BLEU
type: bleu
value: 22.1
---
# opus-mt-tc-big-en-lv
Neural machine translation model for translating from English (en) to Latvian (lv).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trained using the amazing framework of [Marian NMT](https://marian-nmt.github.io/), an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is taken from [OPUS](https://opus.nlpl.eu/) and training pipelines use the procedures of [OPUS-MT-train](https://github.com/Helsinki-NLP/Opus-MT-train).
* Publications: [OPUS-MT – Building open translation services for the World](https://aclanthology.org/2020.eamt-1.61/) and [The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt-1.139/) (Please, cite if you use this model.)
```
@inproceedings{tiedemann-thottingal-2020-opus,
title = "{OPUS}-{MT} {--} Building open translation services for the World",
author = {Tiedemann, J{\"o}rg and Thottingal, Santhosh},
booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
month = nov,
year = "2020",
address = "Lisboa, Portugal",
publisher = "European Association for Machine Translation",
url = "https://aclanthology.org/2020.eamt-1.61",
pages = "479--480",
}
@inproceedings{tiedemann-2020-tatoeba,
title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}",
author = {Tiedemann, J{\"o}rg},
booktitle = "Proceedings of the Fifth Conference on Machine Translation",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.wmt-1.139",
pages = "1174--1182",
}
```
## Model info
* Release: 2022-03-13
* source language(s): eng
* target language(s): lav
* model: transformer-big
* data: opusTCv20210807+bt ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge))
* tokenization: SentencePiece (spm32k,spm32k)
* original model: [opusTCv20210807+bt_transformer-big_2022-03-13.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-lav/opusTCv20210807+bt_transformer-big_2022-03-13.zip)
* more information released models: [OPUS-MT eng-lav README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-lav/README.md)
## Usage
A short example code:
```python
from transformers import MarianMTModel, MarianTokenizer
src_text = [
">>lav<< A day has twenty-four hours.",
">>ltg<< He's a good lawyer."
]
model_name = "pytorch-models/opus-mt-tc-big-en-lv"
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)
translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True))
for t in translated:
print( tokenizer.decode(t, skip_special_tokens=True) )
# expected output:
# Dienā ir divdesmit četras stundas.
# Vyss ir labs advokats.
```
You can also use OPUS-MT models with the transformers pipelines, for example:
```python
from transformers import pipeline
pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-en-lv")
print(pipe(">>lav<< A day has twenty-four hours."))
# expected output: Dienā ir divdesmit četras stundas.
```
## Benchmarks
* test set translations: [opusTCv20210807+bt_transformer-big_2022-03-13.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-lav/opusTCv20210807+bt_transformer-big_2022-03-13.test.txt)
* test set scores: [opusTCv20210807+bt_transformer-big_2022-03-13.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-lav/opusTCv20210807+bt_transformer-big_2022-03-13.eval.txt)
* benchmark results: [benchmark_results.txt](benchmark_results.txt)
* benchmark output: [benchmark_translations.zip](benchmark_translations.zip)
| langpair | testset | chr-F | BLEU | #sent | #words |
|----------|---------|-------|-------|-------|--------|
| eng-lav | tatoeba-test-v2021-08-07 | 0.66411 | 44.0 | 1631 | 9932 |
| eng-lav | flores101-devtest | 0.59397 | 30.1 | 1012 | 22092 |
| eng-lav | newsdev2017 | 0.58082 | 28.9 | 2003 | 41503 |
| eng-lav | newstest2017 | 0.53202 | 22.1 | 2001 | 39392 |
## Acknowledgements
The work is supported by the [European Language Grid](https://www.european-language-grid.eu/) as [pilot project 2866](https://live.european-language-grid.eu/catalogue/#/resource/projects/2866), by the [FoTran project](https://www.helsinki.fi/en/researchgroups/natural-language-understanding-with-cross-lingual-grounding), funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 771113), and the [MeMAD project](https://memad.eu/), funded by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 780069. We are also grateful for the generous computational resources and IT infrastructure provided by [CSC -- IT Center for Science](https://www.csc.fi/), Finland.
## Model conversion info
* transformers version: 4.16.2
* OPUS-MT git hash: 3405783
* port time: Wed Apr 13 17:36:04 EEST 2022
* port machine: LM0-400-22516.local
|
Helsinki-NLP/opus-mt-tc-big-bg-en
|
Helsinki-NLP
| 2023-10-10T10:49:48Z | 170 | 2 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"bg",
"en",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2022-04-13T15:24:05Z |
---
language:
- bg
- en
tags:
- translation
- opus-mt-tc
license: cc-by-4.0
model-index:
- name: opus-mt-tc-big-bg-en
results:
- task:
name: Translation bul-eng
type: translation
args: bul-eng
dataset:
name: flores101-devtest
type: flores_101
args: bul eng devtest
metrics:
- name: BLEU
type: bleu
value: 42.9
- task:
name: Translation bul-eng
type: translation
args: bul-eng
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: bul-eng
metrics:
- name: BLEU
type: bleu
value: 60.5
---
# opus-mt-tc-big-bg-en
Neural machine translation model for translating from Bulgarian (bg) to English (en).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trained using the amazing framework of [Marian NMT](https://marian-nmt.github.io/), an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is taken from [OPUS](https://opus.nlpl.eu/) and training pipelines use the procedures of [OPUS-MT-train](https://github.com/Helsinki-NLP/Opus-MT-train).
* Publications: [OPUS-MT – Building open translation services for the World](https://aclanthology.org/2020.eamt-1.61/) and [The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt-1.139/) (Please, cite if you use this model.)
```
@inproceedings{tiedemann-thottingal-2020-opus,
title = "{OPUS}-{MT} {--} Building open translation services for the World",
author = {Tiedemann, J{\"o}rg and Thottingal, Santhosh},
booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
month = nov,
year = "2020",
address = "Lisboa, Portugal",
publisher = "European Association for Machine Translation",
url = "https://aclanthology.org/2020.eamt-1.61",
pages = "479--480",
}
@inproceedings{tiedemann-2020-tatoeba,
title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}",
author = {Tiedemann, J{\"o}rg},
booktitle = "Proceedings of the Fifth Conference on Machine Translation",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.wmt-1.139",
pages = "1174--1182",
}
```
## Model info
* Release: 2022-03-09
* source language(s): bul
* target language(s): eng
* model: transformer-big
* data: opusTCv20210807+bt ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge))
* tokenization: SentencePiece (spm32k,spm32k)
* original model: [opusTCv20210807+bt_transformer-big_2022-03-09.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/bul-eng/opusTCv20210807+bt_transformer-big_2022-03-09.zip)
* more information released models: [OPUS-MT bul-eng README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/bul-eng/README.md)
## Usage
A short example code:
```python
from transformers import MarianMTModel, MarianTokenizer
src_text = [
"2001 е годината, с която започва 21-ви век.",
"Това е Copacabana!"
]
model_name = "pytorch-models/opus-mt-tc-big-bg-en"
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)
translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True))
for t in translated:
print( tokenizer.decode(t, skip_special_tokens=True) )
# expected output:
# 2001 was the year the 21st century began.
# It's Copacabana!
```
You can also use OPUS-MT models with the transformers pipelines, for example:
```python
from transformers import pipeline
pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-bg-en")
print(pipe("2001 е годината, с която започва 21-ви век."))
# expected output: 2001 was the year the 21st century began.
```
## Benchmarks
* test set translations: [opusTCv20210807+bt_transformer-big_2022-03-09.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/bul-eng/opusTCv20210807+bt_transformer-big_2022-03-09.test.txt)
* test set scores: [opusTCv20210807+bt_transformer-big_2022-03-09.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/bul-eng/opusTCv20210807+bt_transformer-big_2022-03-09.eval.txt)
* benchmark results: [benchmark_results.txt](benchmark_results.txt)
* benchmark output: [benchmark_translations.zip](benchmark_translations.zip)
| langpair | testset | chr-F | BLEU | #sent | #words |
|----------|---------|-------|-------|-------|--------|
| bul-eng | tatoeba-test-v2021-08-07 | 0.73687 | 60.5 | 10000 | 71872 |
| bul-eng | flores101-devtest | 0.67938 | 42.9 | 1012 | 24721 |
## Acknowledgements
The work is supported by the [European Language Grid](https://www.european-language-grid.eu/) as [pilot project 2866](https://live.european-language-grid.eu/catalogue/#/resource/projects/2866), by the [FoTran project](https://www.helsinki.fi/en/researchgroups/natural-language-understanding-with-cross-lingual-grounding), funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 771113), and the [MeMAD project](https://memad.eu/), funded by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 780069. We are also grateful for the generous computational resources and IT infrastructure provided by [CSC -- IT Center for Science](https://www.csc.fi/), Finland.
## Model conversion info
* transformers version: 4.16.2
* OPUS-MT git hash: 3405783
* port time: Wed Apr 13 18:23:56 EEST 2022
* port machine: LM0-400-22516.local
|
Helsinki-NLP/opus-mt-tc-big-en-el
|
Helsinki-NLP
| 2023-10-10T10:48:40Z | 170 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"el",
"en",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2022-04-13T13:53:07Z |
---
language:
- el
- en
tags:
- translation
- opus-mt-tc
license: cc-by-4.0
model-index:
- name: opus-mt-tc-big-en-el
results:
- task:
name: Translation eng-ell
type: translation
args: eng-ell
dataset:
name: flores101-devtest
type: flores_101
args: eng ell devtest
metrics:
- name: BLEU
type: bleu
value: 27.4
- task:
name: Translation eng-ell
type: translation
args: eng-ell
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: eng-ell
metrics:
- name: BLEU
type: bleu
value: 55.4
---
# opus-mt-tc-big-en-el
Neural machine translation model for translating from English (en) to Modern Greek (1453-) (el).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trained using the amazing framework of [Marian NMT](https://marian-nmt.github.io/), an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is taken from [OPUS](https://opus.nlpl.eu/) and training pipelines use the procedures of [OPUS-MT-train](https://github.com/Helsinki-NLP/Opus-MT-train).
* Publications: [OPUS-MT – Building open translation services for the World](https://aclanthology.org/2020.eamt-1.61/) and [The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt-1.139/) (Please, cite if you use this model.)
```
@inproceedings{tiedemann-thottingal-2020-opus,
title = "{OPUS}-{MT} {--} Building open translation services for the World",
author = {Tiedemann, J{\"o}rg and Thottingal, Santhosh},
booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
month = nov,
year = "2020",
address = "Lisboa, Portugal",
publisher = "European Association for Machine Translation",
url = "https://aclanthology.org/2020.eamt-1.61",
pages = "479--480",
}
@inproceedings{tiedemann-2020-tatoeba,
title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}",
author = {Tiedemann, J{\"o}rg},
booktitle = "Proceedings of the Fifth Conference on Machine Translation",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.wmt-1.139",
pages = "1174--1182",
}
```
## Model info
* Release: 2022-03-13
* source language(s): eng
* target language(s): ell
* model: transformer-big
* data: opusTCv20210807+bt ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge))
* tokenization: SentencePiece (spm32k,spm32k)
* original model: [opusTCv20210807+bt_transformer-big_2022-03-13.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-ell/opusTCv20210807+bt_transformer-big_2022-03-13.zip)
* more information released models: [OPUS-MT eng-ell README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-ell/README.md)
## Usage
A short example code:
```python
from transformers import MarianMTModel, MarianTokenizer
src_text = [
"If I weren't broke, I'd buy it.",
"I received your telegram."
]
model_name = "pytorch-models/opus-mt-tc-big-en-el"
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)
translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True))
for t in translated:
print( tokenizer.decode(t, skip_special_tokens=True) )
# expected output:
# Αν δεν ήμουν άφραγκος, θα το αγόραζα.
# Έλαβα το τηλεγράφημα σου.
```
You can also use OPUS-MT models with the transformers pipelines, for example:
```python
from transformers import pipeline
pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-en-el")
print(pipe("If I weren't broke, I'd buy it."))
# expected output: Αν δεν ήμουν άφραγκος, θα το αγόραζα.
```
## Benchmarks
* test set translations: [opusTCv20210807+bt_transformer-big_2022-03-13.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-ell/opusTCv20210807+bt_transformer-big_2022-03-13.test.txt)
* test set scores: [opusTCv20210807+bt_transformer-big_2022-03-13.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-ell/opusTCv20210807+bt_transformer-big_2022-03-13.eval.txt)
* benchmark results: [benchmark_results.txt](benchmark_results.txt)
* benchmark output: [benchmark_translations.zip](benchmark_translations.zip)
| langpair | testset | chr-F | BLEU | #sent | #words |
|----------|---------|-------|-------|-------|--------|
| eng-ell | tatoeba-test-v2021-08-07 | 0.73660 | 55.4 | 10899 | 66884 |
| eng-ell | flores101-devtest | 0.53952 | 27.4 | 1012 | 26615 |
## Acknowledgements
The work is supported by the [European Language Grid](https://www.european-language-grid.eu/) as [pilot project 2866](https://live.european-language-grid.eu/catalogue/#/resource/projects/2866), by the [FoTran project](https://www.helsinki.fi/en/researchgroups/natural-language-understanding-with-cross-lingual-grounding), funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 771113), and the [MeMAD project](https://memad.eu/), funded by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 780069. We are also grateful for the generous computational resources and IT infrastructure provided by [CSC -- IT Center for Science](https://www.csc.fi/), Finland.
## Model conversion info
* transformers version: 4.16.2
* OPUS-MT git hash: 3405783
* port time: Wed Apr 13 16:52:58 EEST 2022
* port machine: LM0-400-22516.local
|
Helsinki-NLP/opus-mt-tc-big-en-ro
|
Helsinki-NLP
| 2023-10-10T10:46:29Z | 256 | 4 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"en",
"ro",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2022-04-13T14:55:54Z |
---
language:
- en
- ro
tags:
- translation
- opus-mt-tc
license: cc-by-4.0
model-index:
- name: opus-mt-tc-big-en-ro
results:
- task:
name: Translation eng-ron
type: translation
args: eng-ron
dataset:
name: flores101-devtest
type: flores_101
args: eng ron devtest
metrics:
- name: BLEU
type: bleu
value: 40.4
- task:
name: Translation eng-ron
type: translation
args: eng-ron
dataset:
name: newsdev2016
type: newsdev2016
args: eng-ron
metrics:
- name: BLEU
type: bleu
value: 36.4
- task:
name: Translation eng-ron
type: translation
args: eng-ron
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: eng-ron
metrics:
- name: BLEU
type: bleu
value: 48.6
- task:
name: Translation eng-ron
type: translation
args: eng-ron
dataset:
name: newstest2016
type: wmt-2016-news
args: eng-ron
metrics:
- name: BLEU
type: bleu
value: 34.0
---
# opus-mt-tc-big-en-ro
Neural machine translation model for translating from English (en) to Romanian (ro).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trained using the amazing framework of [Marian NMT](https://marian-nmt.github.io/), an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is taken from [OPUS](https://opus.nlpl.eu/) and training pipelines use the procedures of [OPUS-MT-train](https://github.com/Helsinki-NLP/Opus-MT-train).
* Publications: [OPUS-MT – Building open translation services for the World](https://aclanthology.org/2020.eamt-1.61/) and [The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt-1.139/) (Please, cite if you use this model.)
```
@inproceedings{tiedemann-thottingal-2020-opus,
title = "{OPUS}-{MT} {--} Building open translation services for the World",
author = {Tiedemann, J{\"o}rg and Thottingal, Santhosh},
booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
month = nov,
year = "2020",
address = "Lisboa, Portugal",
publisher = "European Association for Machine Translation",
url = "https://aclanthology.org/2020.eamt-1.61",
pages = "479--480",
}
@inproceedings{tiedemann-2020-tatoeba,
title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}",
author = {Tiedemann, J{\"o}rg},
booktitle = "Proceedings of the Fifth Conference on Machine Translation",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.wmt-1.139",
pages = "1174--1182",
}
```
## Model info
* Release: 2022-02-25
* source language(s): eng
* target language(s): ron
* model: transformer-big
* data: opusTCv20210807+bt ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge))
* tokenization: SentencePiece (spm32k,spm32k)
* original model: [opusTCv20210807+bt_transformer-big_2022-02-25.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-ron/opusTCv20210807+bt_transformer-big_2022-02-25.zip)
* more information released models: [OPUS-MT eng-ron README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-ron/README.md)
## Usage
A short example code:
```python
from transformers import MarianMTModel, MarianTokenizer
src_text = [
">>ron<< A bad writer's prose is full of hackneyed phrases.",
">>ron<< Zero is a special number."
]
model_name = "pytorch-models/opus-mt-tc-big-en-ro"
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)
translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True))
for t in translated:
print( tokenizer.decode(t, skip_special_tokens=True) )
# expected output:
# Proza unui scriitor prost este plină de fraze tocite.
# Zero este un număr special.
```
You can also use OPUS-MT models with the transformers pipelines, for example:
```python
from transformers import pipeline
pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-en-ro")
print(pipe(">>ron<< A bad writer's prose is full of hackneyed phrases."))
# expected output: Proza unui scriitor prost este plină de fraze tocite.
```
## Benchmarks
* test set translations: [opusTCv20210807+bt_transformer-big_2022-02-25.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-ron/opusTCv20210807+bt_transformer-big_2022-02-25.test.txt)
* test set scores: [opusTCv20210807+bt_transformer-big_2022-02-25.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-ron/opusTCv20210807+bt_transformer-big_2022-02-25.eval.txt)
* benchmark results: [benchmark_results.txt](benchmark_results.txt)
* benchmark output: [benchmark_translations.zip](benchmark_translations.zip)
| langpair | testset | chr-F | BLEU | #sent | #words |
|----------|---------|-------|-------|-------|--------|
| eng-ron | tatoeba-test-v2021-08-07 | 0.68606 | 48.6 | 5508 | 40367 |
| eng-ron | flores101-devtest | 0.64876 | 40.4 | 1012 | 26799 |
| eng-ron | newsdev2016 | 0.62682 | 36.4 | 1999 | 51300 |
| eng-ron | newstest2016 | 0.60702 | 34.0 | 1999 | 48945 |
## Acknowledgements
The work is supported by the [European Language Grid](https://www.european-language-grid.eu/) as [pilot project 2866](https://live.european-language-grid.eu/catalogue/#/resource/projects/2866), by the [FoTran project](https://www.helsinki.fi/en/researchgroups/natural-language-understanding-with-cross-lingual-grounding), funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 771113), and the [MeMAD project](https://memad.eu/), funded by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 780069. We are also grateful for the generous computational resources and IT infrastructure provided by [CSC -- IT Center for Science](https://www.csc.fi/), Finland.
## Model conversion info
* transformers version: 4.16.2
* OPUS-MT git hash: 3405783
* port time: Wed Apr 13 17:55:46 EEST 2022
* port machine: LM0-400-22516.local
|
Helsinki-NLP/opus-mt-tc-big-hu-en
|
Helsinki-NLP
| 2023-10-10T10:45:29Z | 1,036 | 3 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"en",
"hu",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2022-04-13T16:33:48Z |
---
language:
- en
- hu
tags:
- translation
- opus-mt-tc
license: cc-by-4.0
model-index:
- name: opus-mt-tc-big-hu-en
results:
- task:
name: Translation hun-eng
type: translation
args: hun-eng
dataset:
name: flores101-devtest
type: flores_101
args: hun eng devtest
metrics:
- name: BLEU
type: bleu
value: 34.6
- task:
name: Translation hun-eng
type: translation
args: hun-eng
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: hun-eng
metrics:
- name: BLEU
type: bleu
value: 50.4
- task:
name: Translation hun-eng
type: translation
args: hun-eng
dataset:
name: newstest2009
type: wmt-2009-news
args: hun-eng
metrics:
- name: BLEU
type: bleu
value: 23.4
---
# opus-mt-tc-big-hu-en
Neural machine translation model for translating from Hungarian (hu) to English (en).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trained using the amazing framework of [Marian NMT](https://marian-nmt.github.io/), an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is taken from [OPUS](https://opus.nlpl.eu/) and training pipelines use the procedures of [OPUS-MT-train](https://github.com/Helsinki-NLP/Opus-MT-train).
* Publications: [OPUS-MT – Building open translation services for the World](https://aclanthology.org/2020.eamt-1.61/) and [The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt-1.139/) (Please, cite if you use this model.)
```
@inproceedings{tiedemann-thottingal-2020-opus,
title = "{OPUS}-{MT} {--} Building open translation services for the World",
author = {Tiedemann, J{\"o}rg and Thottingal, Santhosh},
booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
month = nov,
year = "2020",
address = "Lisboa, Portugal",
publisher = "European Association for Machine Translation",
url = "https://aclanthology.org/2020.eamt-1.61",
pages = "479--480",
}
@inproceedings{tiedemann-2020-tatoeba,
title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}",
author = {Tiedemann, J{\"o}rg},
booktitle = "Proceedings of the Fifth Conference on Machine Translation",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.wmt-1.139",
pages = "1174--1182",
}
```
## Model info
* Release: 2022-03-09
* source language(s): hun
* target language(s): eng
* model: transformer-big
* data: opusTCv20210807+bt ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge))
* tokenization: SentencePiece (spm32k,spm32k)
* original model: [opusTCv20210807+bt_transformer-big_2022-03-09.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/hun-eng/opusTCv20210807+bt_transformer-big_2022-03-09.zip)
* more information released models: [OPUS-MT hun-eng README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/hun-eng/README.md)
## Usage
A short example code:
```python
from transformers import MarianMTModel, MarianTokenizer
src_text = [
"Bárcsak ne láttam volna ilyen borzalmas filmet!",
"Iskolában van."
]
model_name = "pytorch-models/opus-mt-tc-big-hu-en"
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)
translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True))
for t in translated:
print( tokenizer.decode(t, skip_special_tokens=True) )
# expected output:
# I wish I hadn't seen such a terrible movie.
# She's at school.
```
You can also use OPUS-MT models with the transformers pipelines, for example:
```python
from transformers import pipeline
pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-hu-en")
print(pipe("Bárcsak ne láttam volna ilyen borzalmas filmet!"))
# expected output: I wish I hadn't seen such a terrible movie.
```
## Benchmarks
* test set translations: [opusTCv20210807+bt_transformer-big_2022-03-09.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/hun-eng/opusTCv20210807+bt_transformer-big_2022-03-09.test.txt)
* test set scores: [opusTCv20210807+bt_transformer-big_2022-03-09.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/hun-eng/opusTCv20210807+bt_transformer-big_2022-03-09.eval.txt)
* benchmark results: [benchmark_results.txt](benchmark_results.txt)
* benchmark output: [benchmark_translations.zip](benchmark_translations.zip)
| langpair | testset | chr-F | BLEU | #sent | #words |
|----------|---------|-------|-------|-------|--------|
| hun-eng | tatoeba-test-v2021-08-07 | 0.66644 | 50.4 | 13037 | 94699 |
| hun-eng | flores101-devtest | 0.61974 | 34.6 | 1012 | 24721 |
| hun-eng | newssyscomb2009 | 0.52563 | 24.7 | 502 | 11818 |
| hun-eng | newstest2009 | 0.51698 | 23.4 | 2525 | 65399 |
## Acknowledgements
The work is supported by the [European Language Grid](https://www.european-language-grid.eu/) as [pilot project 2866](https://live.european-language-grid.eu/catalogue/#/resource/projects/2866), by the [FoTran project](https://www.helsinki.fi/en/researchgroups/natural-language-understanding-with-cross-lingual-grounding), funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 771113), and the [MeMAD project](https://memad.eu/), funded by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 780069. We are also grateful for the generous computational resources and IT infrastructure provided by [CSC -- IT Center for Science](https://www.csc.fi/), Finland.
## Model conversion info
* transformers version: 4.16.2
* OPUS-MT git hash: 3405783
* port time: Wed Apr 13 19:33:38 EEST 2022
* port machine: LM0-400-22516.local
|
Helsinki-NLP/opus-mt-tc-big-en-lt
|
Helsinki-NLP
| 2023-10-10T10:42:32Z | 270 | 2 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"en",
"lt",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2022-04-13T14:42:47Z |
---
language:
- en
- lt
tags:
- translation
- opus-mt-tc
license: cc-by-4.0
model-index:
- name: opus-mt-tc-big-en-lt
results:
- task:
name: Translation eng-lit
type: translation
args: eng-lit
dataset:
name: flores101-devtest
type: flores_101
args: eng lit devtest
metrics:
- name: BLEU
type: bleu
value: 28.0
- task:
name: Translation eng-lit
type: translation
args: eng-lit
dataset:
name: newsdev2019
type: newsdev2019
args: eng-lit
metrics:
- name: BLEU
type: bleu
value: 26.6
- task:
name: Translation eng-lit
type: translation
args: eng-lit
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: eng-lit
metrics:
- name: BLEU
type: bleu
value: 39.5
- task:
name: Translation eng-lit
type: translation
args: eng-lit
dataset:
name: newstest2019
type: wmt-2019-news
args: eng-lit
metrics:
- name: BLEU
type: bleu
value: 17.5
---
# opus-mt-tc-big-en-lt
Neural machine translation model for translating from English (en) to Lithuanian (lt).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trained using the amazing framework of [Marian NMT](https://marian-nmt.github.io/), an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is taken from [OPUS](https://opus.nlpl.eu/) and training pipelines use the procedures of [OPUS-MT-train](https://github.com/Helsinki-NLP/Opus-MT-train).
* Publications: [OPUS-MT – Building open translation services for the World](https://aclanthology.org/2020.eamt-1.61/) and [The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt-1.139/) (Please, cite if you use this model.)
```
@inproceedings{tiedemann-thottingal-2020-opus,
title = "{OPUS}-{MT} {--} Building open translation services for the World",
author = {Tiedemann, J{\"o}rg and Thottingal, Santhosh},
booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
month = nov,
year = "2020",
address = "Lisboa, Portugal",
publisher = "European Association for Machine Translation",
url = "https://aclanthology.org/2020.eamt-1.61",
pages = "479--480",
}
@inproceedings{tiedemann-2020-tatoeba,
title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}",
author = {Tiedemann, J{\"o}rg},
booktitle = "Proceedings of the Fifth Conference on Machine Translation",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.wmt-1.139",
pages = "1174--1182",
}
```
## Model info
* Release: 2022-02-25
* source language(s): eng
* target language(s): lit
* model: transformer-big
* data: opusTCv20210807+bt ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge))
* tokenization: SentencePiece (spm32k,spm32k)
* original model: [opusTCv20210807+bt_transformer-big_2022-02-25.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-lit/opusTCv20210807+bt_transformer-big_2022-02-25.zip)
* more information released models: [OPUS-MT eng-lit README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-lit/README.md)
## Usage
A short example code:
```python
from transformers import MarianMTModel, MarianTokenizer
src_text = [
"A cat was sitting on the chair.",
"Yukiko likes potatoes."
]
model_name = "pytorch-models/opus-mt-tc-big-en-lt"
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)
translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True))
for t in translated:
print( tokenizer.decode(t, skip_special_tokens=True) )
# expected output:
# Katė sėdėjo ant kėdės.
# Jukiko mėgsta bulves.
```
You can also use OPUS-MT models with the transformers pipelines, for example:
```python
from transformers import pipeline
pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-en-lt")
print(pipe("A cat was sitting on the chair."))
# expected output: Katė sėdėjo ant kėdės.
```
## Benchmarks
* test set translations: [opusTCv20210807+bt_transformer-big_2022-02-25.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-lit/opusTCv20210807+bt_transformer-big_2022-02-25.test.txt)
* test set scores: [opusTCv20210807+bt_transformer-big_2022-02-25.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-lit/opusTCv20210807+bt_transformer-big_2022-02-25.eval.txt)
* benchmark results: [benchmark_results.txt](benchmark_results.txt)
* benchmark output: [benchmark_translations.zip](benchmark_translations.zip)
| langpair | testset | chr-F | BLEU | #sent | #words |
|----------|---------|-------|-------|-------|--------|
| eng-lit | tatoeba-test-v2021-08-07 | 0.67434 | 39.5 | 2528 | 14942 |
| eng-lit | flores101-devtest | 0.59593 | 28.0 | 1012 | 20695 |
| eng-lit | newsdev2019 | 0.58444 | 26.6 | 2000 | 39627 |
| eng-lit | newstest2019 | 0.51559 | 17.5 | 998 | 19711 |
## Acknowledgements
The work is supported by the [European Language Grid](https://www.european-language-grid.eu/) as [pilot project 2866](https://live.european-language-grid.eu/catalogue/#/resource/projects/2866), by the [FoTran project](https://www.helsinki.fi/en/researchgroups/natural-language-understanding-with-cross-lingual-grounding), funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 771113), and the [MeMAD project](https://memad.eu/), funded by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 780069. We are also grateful for the generous computational resources and IT infrastructure provided by [CSC -- IT Center for Science](https://www.csc.fi/), Finland.
## Model conversion info
* transformers version: 4.16.2
* OPUS-MT git hash: 3405783
* port time: Wed Apr 13 17:42:39 EEST 2022
* port machine: LM0-400-22516.local
|
Helsinki-NLP/opus-mt-tc-big-zle-de
|
Helsinki-NLP
| 2023-10-10T10:41:28Z | 323 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"be",
"de",
"ru",
"uk",
"zle",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2022-03-24T08:57:20Z |
---
language:
- be
- de
- ru
- uk
- zle
tags:
- translation
- opus-mt-tc
license: cc-by-4.0
model-index:
- name: opus-mt-tc-big-zle-de
results:
- task:
name: Translation rus-deu
type: translation
args: rus-deu
dataset:
name: flores101-devtest
type: flores_101
args: rus deu devtest
metrics:
- name: BLEU
type: bleu
value: 26.1
- task:
name: Translation ukr-deu
type: translation
args: ukr-deu
dataset:
name: flores101-devtest
type: flores_101
args: ukr deu devtest
metrics:
- name: BLEU
type: bleu
value: 28.1
- task:
name: Translation bel-deu
type: translation
args: bel-deu
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: bel-deu
metrics:
- name: BLEU
type: bleu
value: 44.8
- task:
name: Translation rus-deu
type: translation
args: rus-deu
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: rus-deu
metrics:
- name: BLEU
type: bleu
value: 51.8
- task:
name: Translation ukr-deu
type: translation
args: ukr-deu
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: ukr-deu
metrics:
- name: BLEU
type: bleu
value: 54.7
- task:
name: Translation rus-deu
type: translation
args: rus-deu
dataset:
name: newstest2013
type: wmt-2013-news
args: rus-deu
metrics:
- name: BLEU
type: bleu
value: 25.2
---
# opus-mt-tc-big-zle-de
Neural machine translation model for translating from East Slavic languages (zle) to German (de).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trained using the amazing framework of [Marian NMT](https://marian-nmt.github.io/), an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is taken from [OPUS](https://opus.nlpl.eu/) and training pipelines use the procedures of [OPUS-MT-train](https://github.com/Helsinki-NLP/Opus-MT-train).
* Publications: [OPUS-MT – Building open translation services for the World](https://aclanthology.org/2020.eamt-1.61/) and [The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt-1.139/) (Please, cite if you use this model.)
```
@inproceedings{tiedemann-thottingal-2020-opus,
title = "{OPUS}-{MT} {--} Building open translation services for the World",
author = {Tiedemann, J{\"o}rg and Thottingal, Santhosh},
booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
month = nov,
year = "2020",
address = "Lisboa, Portugal",
publisher = "European Association for Machine Translation",
url = "https://aclanthology.org/2020.eamt-1.61",
pages = "479--480",
}
@inproceedings{tiedemann-2020-tatoeba,
title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}",
author = {Tiedemann, J{\"o}rg},
booktitle = "Proceedings of the Fifth Conference on Machine Translation",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.wmt-1.139",
pages = "1174--1182",
}
```
## Model info
* Release: 2022-03-19
* source language(s): bel rus ukr
* target language(s): deu
* model: transformer-big
* data: opusTCv20210807 ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge))
* tokenization: SentencePiece (spm32k,spm32k)
* original model: [opusTCv20210807_transformer-big_2022-03-19.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/zle-deu/opusTCv20210807_transformer-big_2022-03-19.zip)
* more information released models: [OPUS-MT zle-deu README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/zle-deu/README.md)
## Usage
A short example code:
```python
from transformers import MarianMTModel, MarianTokenizer
src_text = [
"Это был по-настоящему прекрасный день.",
"Дождь кончился?"
]
model_name = "pytorch-models/opus-mt-tc-big-zle-de"
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)
translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True))
for t in translated:
print( tokenizer.decode(t, skip_special_tokens=True) )
# expected output:
# Es war ein wirklich schöner Tag.
# Ist der Regen vorbei?
```
You can also use OPUS-MT models with the transformers pipelines, for example:
```python
from transformers import pipeline
pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-zle-de")
print(pipe("Это был по-настоящему прекрасный день."))
# expected output: Es war ein wirklich schöner Tag.
```
## Benchmarks
* test set translations: [opusTCv20210807_transformer-big_2022-03-19.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zle-deu/opusTCv20210807_transformer-big_2022-03-19.test.txt)
* test set scores: [opusTCv20210807_transformer-big_2022-03-19.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zle-deu/opusTCv20210807_transformer-big_2022-03-19.eval.txt)
* benchmark results: [benchmark_results.txt](benchmark_results.txt)
* benchmark output: [benchmark_translations.zip](benchmark_translations.zip)
| langpair | testset | chr-F | BLEU | #sent | #words |
|----------|---------|-------|-------|-------|--------|
| bel-deu | tatoeba-test-v2021-08-07 | 0.63720 | 44.8 | 551 | 4182 |
| rus-deu | tatoeba-test-v2021-08-07 | 0.69768 | 51.8 | 12800 | 98842 |
| ukr-deu | tatoeba-test-v2021-08-07 | 0.70860 | 54.7 | 10319 | 64646 |
| bel-deu | flores101-devtest | 0.47052 | 12.9 | 1012 | 25094 |
| rus-deu | flores101-devtest | 0.56159 | 26.1 | 1012 | 25094 |
| ukr-deu | flores101-devtest | 0.57251 | 28.1 | 1012 | 25094 |
| rus-deu | newstest2012 | 0.49257 | 19.8 | 3003 | 72886 |
| rus-deu | newstest2013 | 0.54015 | 25.2 | 3000 | 63737 |
## Acknowledgements
The work is supported by the [European Language Grid](https://www.european-language-grid.eu/) as [pilot project 2866](https://live.european-language-grid.eu/catalogue/#/resource/projects/2866), by the [FoTran project](https://www.helsinki.fi/en/researchgroups/natural-language-understanding-with-cross-lingual-grounding), funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 771113), and the [MeMAD project](https://memad.eu/), funded by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 780069. We are also grateful for the generous computational resources and IT infrastructure provided by [CSC -- IT Center for Science](https://www.csc.fi/), Finland.
## Model conversion info
* transformers version: 4.16.2
* OPUS-MT git hash: 1bdabf7
* port time: Wed Mar 23 22:16:45 EET 2022
* port machine: LM0-400-22516.local
|
Helsinki-NLP/opus-mt-tc-big-en-hu
|
Helsinki-NLP
| 2023-10-10T10:40:26Z | 1,246 | 2 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"en",
"hu",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2022-04-13T14:21:29Z |
---
language:
- en
- hu
tags:
- translation
- opus-mt-tc
license: cc-by-4.0
model-index:
- name: opus-mt-tc-big-en-hu
results:
- task:
name: Translation eng-hun
type: translation
args: eng-hun
dataset:
name: flores101-devtest
type: flores_101
args: eng hun devtest
metrics:
- name: BLEU
type: bleu
value: 29.6
- task:
name: Translation eng-hun
type: translation
args: eng-hun
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: eng-hun
metrics:
- name: BLEU
type: bleu
value: 38.7
- task:
name: Translation eng-hun
type: translation
args: eng-hun
dataset:
name: newstest2009
type: wmt-2009-news
args: eng-hun
metrics:
- name: BLEU
type: bleu
value: 20.3
---
# opus-mt-tc-big-en-hu
Neural machine translation model for translating from English (en) to Hungarian (hu).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trained using the amazing framework of [Marian NMT](https://marian-nmt.github.io/), an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is taken from [OPUS](https://opus.nlpl.eu/) and training pipelines use the procedures of [OPUS-MT-train](https://github.com/Helsinki-NLP/Opus-MT-train).
* Publications: [OPUS-MT – Building open translation services for the World](https://aclanthology.org/2020.eamt-1.61/) and [The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt-1.139/) (Please, cite if you use this model.)
```
@inproceedings{tiedemann-thottingal-2020-opus,
title = "{OPUS}-{MT} {--} Building open translation services for the World",
author = {Tiedemann, J{\"o}rg and Thottingal, Santhosh},
booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
month = nov,
year = "2020",
address = "Lisboa, Portugal",
publisher = "European Association for Machine Translation",
url = "https://aclanthology.org/2020.eamt-1.61",
pages = "479--480",
}
@inproceedings{tiedemann-2020-tatoeba,
title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}",
author = {Tiedemann, J{\"o}rg},
booktitle = "Proceedings of the Fifth Conference on Machine Translation",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.wmt-1.139",
pages = "1174--1182",
}
```
## Model info
* Release: 2022-02-25
* source language(s): eng
* target language(s): hun
* model: transformer-big
* data: opusTCv20210807+bt ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge))
* tokenization: SentencePiece (spm32k,spm32k)
* original model: [opusTCv20210807+bt_transformer-big_2022-02-25.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-hun/opusTCv20210807+bt_transformer-big_2022-02-25.zip)
* more information released models: [OPUS-MT eng-hun README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-hun/README.md)
## Usage
A short example code:
```python
from transformers import MarianMTModel, MarianTokenizer
src_text = [
"I wish I hadn't seen such a horrible film.",
"She's at school."
]
model_name = "pytorch-models/opus-mt-tc-big-en-hu"
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)
translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True))
for t in translated:
print( tokenizer.decode(t, skip_special_tokens=True) )
# expected output:
# Bárcsak ne láttam volna ilyen szörnyű filmet.
# Iskolában van.
```
You can also use OPUS-MT models with the transformers pipelines, for example:
```python
from transformers import pipeline
pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-en-hu")
print(pipe("I wish I hadn't seen such a horrible film."))
# expected output: Bárcsak ne láttam volna ilyen szörnyű filmet.
```
## Benchmarks
* test set translations: [opusTCv20210807+bt_transformer-big_2022-02-25.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-hun/opusTCv20210807+bt_transformer-big_2022-02-25.test.txt)
* test set scores: [opusTCv20210807+bt_transformer-big_2022-02-25.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-hun/opusTCv20210807+bt_transformer-big_2022-02-25.eval.txt)
* benchmark results: [benchmark_results.txt](benchmark_results.txt)
* benchmark output: [benchmark_translations.zip](benchmark_translations.zip)
| langpair | testset | chr-F | BLEU | #sent | #words |
|----------|---------|-------|-------|-------|--------|
| eng-hun | tatoeba-test-v2021-08-07 | 0.62096 | 38.7 | 13037 | 79562 |
| eng-hun | flores101-devtest | 0.60159 | 29.6 | 1012 | 22183 |
| eng-hun | newssyscomb2009 | 0.51918 | 20.6 | 502 | 9733 |
| eng-hun | newstest2009 | 0.50973 | 20.3 | 2525 | 54965 |
## Acknowledgements
The work is supported by the [European Language Grid](https://www.european-language-grid.eu/) as [pilot project 2866](https://live.european-language-grid.eu/catalogue/#/resource/projects/2866), by the [FoTran project](https://www.helsinki.fi/en/researchgroups/natural-language-understanding-with-cross-lingual-grounding), funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 771113), and the [MeMAD project](https://memad.eu/), funded by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 780069. We are also grateful for the generous computational resources and IT infrastructure provided by [CSC -- IT Center for Science](https://www.csc.fi/), Finland.
## Model conversion info
* transformers version: 4.16.2
* OPUS-MT git hash: 3405783
* port time: Wed Apr 13 17:21:20 EEST 2022
* port machine: LM0-400-22516.local
|
Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa
|
Helsinki-NLP
| 2023-10-10T10:38:12Z | 130 | 3 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"ca",
"en",
"es",
"oc",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2022-04-13T13:40:56Z |
---
language:
- ca
- en
- es
- oc
tags:
- translation
- opus-mt-tc
license: cc-by-4.0
model-index:
- name: opus-mt-tc-big-en-cat_oci_spa
results:
- task:
name: Translation eng-cat
type: translation
args: eng-cat
dataset:
name: flores101-devtest
type: flores_101
args: eng cat devtest
metrics:
- name: BLEU
type: bleu
value: 41.5
- task:
name: Translation eng-oci
type: translation
args: eng-oci
dataset:
name: flores101-devtest
type: flores_101
args: eng oci devtest
metrics:
- name: BLEU
type: bleu
value: 25.4
- task:
name: Translation eng-spa
type: translation
args: eng-spa
dataset:
name: flores101-devtest
type: flores_101
args: eng spa devtest
metrics:
- name: BLEU
type: bleu
value: 28.1
- task:
name: Translation eng-spa
type: translation
args: eng-spa
dataset:
name: news-test2008
type: news-test2008
args: eng-spa
metrics:
- name: BLEU
type: bleu
value: 30.0
- task:
name: Translation eng-cat
type: translation
args: eng-cat
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: eng-cat
metrics:
- name: BLEU
type: bleu
value: 47.8
- task:
name: Translation eng-spa
type: translation
args: eng-spa
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: eng-spa
metrics:
- name: BLEU
type: bleu
value: 57.0
- task:
name: Translation eng-spa
type: translation
args: eng-spa
dataset:
name: tico19-test
type: tico19-test
args: eng-spa
metrics:
- name: BLEU
type: bleu
value: 52.5
- task:
name: Translation eng-spa
type: translation
args: eng-spa
dataset:
name: newstest2009
type: wmt-2009-news
args: eng-spa
metrics:
- name: BLEU
type: bleu
value: 30.5
- task:
name: Translation eng-spa
type: translation
args: eng-spa
dataset:
name: newstest2010
type: wmt-2010-news
args: eng-spa
metrics:
- name: BLEU
type: bleu
value: 37.4
- task:
name: Translation eng-spa
type: translation
args: eng-spa
dataset:
name: newstest2011
type: wmt-2011-news
args: eng-spa
metrics:
- name: BLEU
type: bleu
value: 39.1
- task:
name: Translation eng-spa
type: translation
args: eng-spa
dataset:
name: newstest2012
type: wmt-2012-news
args: eng-spa
metrics:
- name: BLEU
type: bleu
value: 39.6
- task:
name: Translation eng-spa
type: translation
args: eng-spa
dataset:
name: newstest2013
type: wmt-2013-news
args: eng-spa
metrics:
- name: BLEU
type: bleu
value: 35.8
---
# opus-mt-tc-big-en-cat_oci_spa
Neural machine translation model for translating from English (en) to Catalan, Occitan and Spanish (cat+oci+spa).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trained using the amazing framework of [Marian NMT](https://marian-nmt.github.io/), an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is taken from [OPUS](https://opus.nlpl.eu/) and training pipelines use the procedures of [OPUS-MT-train](https://github.com/Helsinki-NLP/Opus-MT-train).
* Publications: [OPUS-MT – Building open translation services for the World](https://aclanthology.org/2020.eamt-1.61/) and [The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt-1.139/) (Please, cite if you use this model.)
```
@inproceedings{tiedemann-thottingal-2020-opus,
title = "{OPUS}-{MT} {--} Building open translation services for the World",
author = {Tiedemann, J{\"o}rg and Thottingal, Santhosh},
booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
month = nov,
year = "2020",
address = "Lisboa, Portugal",
publisher = "European Association for Machine Translation",
url = "https://aclanthology.org/2020.eamt-1.61",
pages = "479--480",
}
@inproceedings{tiedemann-2020-tatoeba,
title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}",
author = {Tiedemann, J{\"o}rg},
booktitle = "Proceedings of the Fifth Conference on Machine Translation",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.wmt-1.139",
pages = "1174--1182",
}
```
## Model info
* Release: 2022-03-13
* source language(s): eng
* target language(s): cat spa
* valid target language labels: >>cat<< >>spa<<
* model: transformer-big
* data: opusTCv20210807+bt ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge))
* tokenization: SentencePiece (spm32k,spm32k)
* original model: [opusTCv20210807+bt_transformer-big_2022-03-13.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-cat+oci+spa/opusTCv20210807+bt_transformer-big_2022-03-13.zip)
* more information released models: [OPUS-MT eng-cat+oci+spa README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-cat+oci+spa/README.md)
* more information about the model: [MarianMT](https://huggingface.co/docs/transformers/model_doc/marian)
This is a multilingual translation model with multiple target languages. A sentence initial language token is required in the form of `>>id<<` (id = valid target language ID), e.g. `>>cat<<`
## Usage
A short example code:
```python
from transformers import MarianMTModel, MarianTokenizer
src_text = [
">>spa<< Why do you want Tom to go there with me?",
">>spa<< She forced him to eat spinach."
]
model_name = "pytorch-models/opus-mt-tc-big-en-cat_oci_spa"
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)
translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True))
for t in translated:
print( tokenizer.decode(t, skip_special_tokens=True) )
# expected output:
# ¿Por qué quieres que Tom vaya conmigo?
# Ella lo obligó a comer espinacas.
```
You can also use OPUS-MT models with the transformers pipelines, for example:
```python
from transformers import pipeline
pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-en-cat_oci_spa")
print(pipe(">>spa<< Why do you want Tom to go there with me?"))
# expected output: ¿Por qué quieres que Tom vaya conmigo?
```
## Benchmarks
* test set translations: [opusTCv20210807+bt_transformer-big_2022-03-13.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-cat+oci+spa/opusTCv20210807+bt_transformer-big_2022-03-13.test.txt)
* test set scores: [opusTCv20210807+bt_transformer-big_2022-03-13.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-cat+oci+spa/opusTCv20210807+bt_transformer-big_2022-03-13.eval.txt)
* benchmark results: [benchmark_results.txt](benchmark_results.txt)
* benchmark output: [benchmark_translations.zip](benchmark_translations.zip)
| langpair | testset | chr-F | BLEU | #sent | #words |
|----------|---------|-------|-------|-------|--------|
| eng-cat | tatoeba-test-v2021-08-07 | 0.66414 | 47.8 | 1631 | 12344 |
| eng-spa | tatoeba-test-v2021-08-07 | 0.73725 | 57.0 | 16583 | 134710 |
| eng-cat | flores101-devtest | 0.66071 | 41.5 | 1012 | 27304 |
| eng-oci | flores101-devtest | 0.56192 | 25.4 | 1012 | 27305 |
| eng-spa | flores101-devtest | 0.56288 | 28.1 | 1012 | 29199 |
| eng-spa | newssyscomb2009 | 0.58431 | 31.4 | 502 | 12503 |
| eng-spa | news-test2008 | 0.56622 | 30.0 | 2051 | 52586 |
| eng-spa | newstest2009 | 0.57988 | 30.5 | 2525 | 68111 |
| eng-spa | newstest2010 | 0.62343 | 37.4 | 2489 | 65480 |
| eng-spa | newstest2011 | 0.62424 | 39.1 | 3003 | 79476 |
| eng-spa | newstest2012 | 0.63006 | 39.6 | 3003 | 79006 |
| eng-spa | newstest2013 | 0.60291 | 35.8 | 3000 | 70528 |
| eng-spa | tico19-test | 0.73224 | 52.5 | 2100 | 66563 |
## Acknowledgements
The work is supported by the [European Language Grid](https://www.european-language-grid.eu/) as [pilot project 2866](https://live.european-language-grid.eu/catalogue/#/resource/projects/2866), by the [FoTran project](https://www.helsinki.fi/en/researchgroups/natural-language-understanding-with-cross-lingual-grounding), funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 771113), and the [MeMAD project](https://memad.eu/), funded by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 780069. We are also grateful for the generous computational resources and IT infrastructure provided by [CSC -- IT Center for Science](https://www.csc.fi/), Finland.
## Model conversion info
* transformers version: 4.16.2
* OPUS-MT git hash: 3405783
* port time: Wed Apr 13 16:40:45 EEST 2022
* port machine: LM0-400-22516.local
|
Helsinki-NLP/opus-mt-tc-big-en-gmq
|
Helsinki-NLP
| 2023-10-10T10:34:07Z | 3,092 | 3 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"tc",
"big",
"en",
"gmq",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2022-04-13T14:14:55Z |
---
language:
- da
- en
- fo
- gmq
- is
- nb
- nn
- false
- sv
tags:
- translation
- opus-mt-tc
license: cc-by-4.0
model-index:
- name: opus-mt-tc-big-en-gmq
results:
- task:
name: Translation eng-dan
type: translation
args: eng-dan
dataset:
name: flores101-devtest
type: flores_101
args: eng dan devtest
metrics:
- name: BLEU
type: bleu
value: 47.7
- task:
name: Translation eng-isl
type: translation
args: eng-isl
dataset:
name: flores101-devtest
type: flores_101
args: eng isl devtest
metrics:
- name: BLEU
type: bleu
value: 24.1
- task:
name: Translation eng-nob
type: translation
args: eng-nob
dataset:
name: flores101-devtest
type: flores_101
args: eng nob devtest
metrics:
- name: BLEU
type: bleu
value: 34.5
- task:
name: Translation eng-swe
type: translation
args: eng-swe
dataset:
name: flores101-devtest
type: flores_101
args: eng swe devtest
metrics:
- name: BLEU
type: bleu
value: 46.9
- task:
name: Translation eng-isl
type: translation
args: eng-isl
dataset:
name: newsdev2021.en-is
type: newsdev2021.en-is
args: eng-isl
metrics:
- name: BLEU
type: bleu
value: 22.6
- task:
name: Translation eng-dan
type: translation
args: eng-dan
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: eng-dan
metrics:
- name: BLEU
type: bleu
value: 61.6
- task:
name: Translation eng-isl
type: translation
args: eng-isl
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: eng-isl
metrics:
- name: BLEU
type: bleu
value: 39.9
- task:
name: Translation eng-nno
type: translation
args: eng-nno
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: eng-nno
metrics:
- name: BLEU
type: bleu
value: 40.1
- task:
name: Translation eng-nob
type: translation
args: eng-nob
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: eng-nob
metrics:
- name: BLEU
type: bleu
value: 57.3
- task:
name: Translation eng-swe
type: translation
args: eng-swe
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: eng-swe
metrics:
- name: BLEU
type: bleu
value: 60.9
- task:
name: Translation eng-isl
type: translation
args: eng-isl
dataset:
name: newstest2021.en-is
type: wmt-2021-news
args: eng-isl
metrics:
- name: BLEU
type: bleu
value: 21.5
---
# opus-mt-tc-big-en-gmq
Neural machine translation model for translating from English (en) to North Germanic languages (gmq).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trained using the amazing framework of [Marian NMT](https://marian-nmt.github.io/), an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is taken from [OPUS](https://opus.nlpl.eu/) and training pipelines use the procedures of [OPUS-MT-train](https://github.com/Helsinki-NLP/Opus-MT-train).
* Publications: [OPUS-MT – Building open translation services for the World](https://aclanthology.org/2020.eamt-1.61/) and [The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt-1.139/) (Please, cite if you use this model.)
```
@inproceedings{tiedemann-thottingal-2020-opus,
title = "{OPUS}-{MT} {--} Building open translation services for the World",
author = {Tiedemann, J{\"o}rg and Thottingal, Santhosh},
booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
month = nov,
year = "2020",
address = "Lisboa, Portugal",
publisher = "European Association for Machine Translation",
url = "https://aclanthology.org/2020.eamt-1.61",
pages = "479--480",
}
@inproceedings{tiedemann-2020-tatoeba,
title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}",
author = {Tiedemann, J{\"o}rg},
booktitle = "Proceedings of the Fifth Conference on Machine Translation",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.wmt-1.139",
pages = "1174--1182",
}
```
## Model info
* Release: 2022-03-17
* source language(s): eng
* target language(s): dan fao isl nno nob nor swe
* valid target language labels: >>dan<< >>fao<< >>isl<< >>nno<< >>nob<< >>nor<< >>swe<<
* model: transformer-big
* data: opusTCv20210807+bt ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge))
* tokenization: SentencePiece (spm32k,spm32k)
* original model: [opusTCv20210807+bt_transformer-big_2022-03-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-gmq/opusTCv20210807+bt_transformer-big_2022-03-17.zip)
* more information released models: [OPUS-MT eng-gmq README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-gmq/README.md)
* more information about the model: [MarianMT](https://huggingface.co/docs/transformers/model_doc/marian)
This is a multilingual translation model with multiple target languages. A sentence initial language token is required in the form of `>>id<<` (id = valid target language ID), e.g. `>>dan<<`
## Usage
A short example code:
```python
from transformers import MarianMTModel, MarianTokenizer
src_text = [
">>nno<< The United States borders Canada.",
">>nob<< This is the biggest hotel in this city."
]
model_name = "pytorch-models/opus-mt-tc-big-en-gmq"
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)
translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True))
for t in translated:
print( tokenizer.decode(t, skip_special_tokens=True) )
# expected output:
# USA grensar til Canada.
# Dette er det største hotellet i denne byen.
```
You can also use OPUS-MT models with the transformers pipelines, for example:
```python
from transformers import pipeline
pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-en-gmq")
print(pipe(">>nno<< The United States borders Canada."))
# expected output: USA grensar til Canada.
```
## Benchmarks
* test set translations: [opusTCv20210807+bt_transformer-big_2022-03-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-gmq/opusTCv20210807+bt_transformer-big_2022-03-17.test.txt)
* test set scores: [opusTCv20210807+bt_transformer-big_2022-03-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-gmq/opusTCv20210807+bt_transformer-big_2022-03-17.eval.txt)
* benchmark results: [benchmark_results.txt](benchmark_results.txt)
* benchmark output: [benchmark_translations.zip](benchmark_translations.zip)
| langpair | testset | chr-F | BLEU | #sent | #words |
|----------|---------|-------|-------|-------|--------|
| eng-dan | tatoeba-test-v2021-08-07 | 0.75165 | 61.6 | 10795 | 79385 |
| eng-fao | tatoeba-test-v2021-08-07 | 0.40395 | 18.3 | 294 | 1933 |
| eng-isl | tatoeba-test-v2021-08-07 | 0.59731 | 39.9 | 2503 | 19023 |
| eng-nno | tatoeba-test-v2021-08-07 | 0.61271 | 40.1 | 460 | 3428 |
| eng-nob | tatoeba-test-v2021-08-07 | 0.72380 | 57.3 | 4539 | 36119 |
| eng-swe | tatoeba-test-v2021-08-07 | 0.74197 | 60.9 | 10362 | 68067 |
| eng-dan | flores101-devtest | 0.70810 | 47.7 | 1012 | 24638 |
| eng-isl | flores101-devtest | 0.52076 | 24.1 | 1012 | 22834 |
| eng-nob | flores101-devtest | 0.62760 | 34.5 | 1012 | 23873 |
| eng-swe | flores101-devtest | 0.70129 | 46.9 | 1012 | 23121 |
| eng-isl | newsdev2021.en-is | 0.50376 | 22.6 | 2004 | 43721 |
| eng-isl | newstest2021.en-is | 0.50516 | 21.5 | 1000 | 25233 |
## Acknowledgements
The work is supported by the [European Language Grid](https://www.european-language-grid.eu/) as [pilot project 2866](https://live.european-language-grid.eu/catalogue/#/resource/projects/2866), by the [FoTran project](https://www.helsinki.fi/en/researchgroups/natural-language-understanding-with-cross-lingual-grounding), funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 771113), and the [MeMAD project](https://memad.eu/), funded by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 780069. We are also grateful for the generous computational resources and IT infrastructure provided by [CSC -- IT Center for Science](https://www.csc.fi/), Finland.
## Model conversion info
* transformers version: 4.16.2
* OPUS-MT git hash: 3405783
* port time: Wed Apr 13 17:14:46 EEST 2022
* port machine: LM0-400-22516.local
|
EscvNcl/MobileNet-V2-Retinopathy
|
EscvNcl
| 2023-10-10T10:33:31Z | 198 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mobilenet_v2",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:google/mobilenet_v2_1.4_224",
"base_model:finetune:google/mobilenet_v2_1.4_224",
"license:other",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-10-10T09:46:40Z |
---
license: other
base_model: google/mobilenet_v2_1.4_224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: MobileNet-V2-Retinopathy
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9306930693069307
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# MobileNet-V2-Retinopathy
This model is a fine-tuned version of [google/mobilenet_v2_1.4_224](https://huggingface.co/google/mobilenet_v2_1.4_224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2044
- Accuracy: 0.9307
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4403 | 1.0 | 113 | 0.5330 | 0.7079 |
| 0.5538 | 2.0 | 227 | 0.4312 | 0.7723 |
| 0.542 | 3.0 | 340 | 0.5137 | 0.7426 |
| 0.4776 | 4.0 | 454 | 0.4656 | 0.7723 |
| 0.4244 | 5.0 | 567 | 1.0400 | 0.5990 |
| 0.4694 | 6.0 | 681 | 0.5936 | 0.7228 |
| 0.4494 | 7.0 | 794 | 0.4667 | 0.7822 |
| 0.4647 | 8.0 | 908 | 0.2629 | 0.8960 |
| 0.3646 | 9.0 | 1021 | 0.2287 | 0.8861 |
| 0.4827 | 10.0 | 1135 | 1.7967 | 0.5149 |
| 0.3679 | 11.0 | 1248 | 0.4184 | 0.8267 |
| 0.3454 | 12.0 | 1362 | 0.1885 | 0.9406 |
| 0.3562 | 13.0 | 1475 | 0.2798 | 0.9059 |
| 0.3397 | 14.0 | 1589 | 1.6444 | 0.5891 |
| 0.4047 | 14.93 | 1695 | 0.2044 | 0.9307 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
|
Helsinki-NLP/opus-mt-tc-big-en-it
|
Helsinki-NLP
| 2023-10-10T10:33:03Z | 297 | 5 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"en",
"it",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2022-04-13T14:27:31Z |
---
language:
- en
- it
tags:
- translation
- opus-mt-tc
license: cc-by-4.0
model-index:
- name: opus-mt-tc-big-en-it
results:
- task:
name: Translation eng-ita
type: translation
args: eng-ita
dataset:
name: flores101-devtest
type: flores_101
args: eng ita devtest
metrics:
- name: BLEU
type: bleu
value: 29.6
- task:
name: Translation eng-ita
type: translation
args: eng-ita
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: eng-ita
metrics:
- name: BLEU
type: bleu
value: 53.9
- task:
name: Translation eng-ita
type: translation
args: eng-ita
dataset:
name: newstest2009
type: wmt-2009-news
args: eng-ita
metrics:
- name: BLEU
type: bleu
value: 31.6
---
# opus-mt-tc-big-en-it
Neural machine translation model for translating from English (en) to Italian (it).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trained using the amazing framework of [Marian NMT](https://marian-nmt.github.io/), an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is taken from [OPUS](https://opus.nlpl.eu/) and training pipelines use the procedures of [OPUS-MT-train](https://github.com/Helsinki-NLP/Opus-MT-train).
* Publications: [OPUS-MT – Building open translation services for the World](https://aclanthology.org/2020.eamt-1.61/) and [The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt-1.139/) (Please, cite if you use this model.)
```
@inproceedings{tiedemann-thottingal-2020-opus,
title = "{OPUS}-{MT} {--} Building open translation services for the World",
author = {Tiedemann, J{\"o}rg and Thottingal, Santhosh},
booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
month = nov,
year = "2020",
address = "Lisboa, Portugal",
publisher = "European Association for Machine Translation",
url = "https://aclanthology.org/2020.eamt-1.61",
pages = "479--480",
}
@inproceedings{tiedemann-2020-tatoeba,
title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}",
author = {Tiedemann, J{\"o}rg},
booktitle = "Proceedings of the Fifth Conference on Machine Translation",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.wmt-1.139",
pages = "1174--1182",
}
```
## Model info
* Release: 2022-03-13
* source language(s): eng
* target language(s): ita
* model: transformer-big
* data: opusTCv20210807+bt ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge))
* tokenization: SentencePiece (spm32k,spm32k)
* original model: [opusTCv20210807+bt_transformer-big_2022-03-13.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-ita/opusTCv20210807+bt_transformer-big_2022-03-13.zip)
* more information released models: [OPUS-MT eng-ita README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-ita/README.md)
## Usage
A short example code:
```python
from transformers import MarianMTModel, MarianTokenizer
src_text = [
"He was always very respectful.",
"This cat is black. Is the dog, too?"
]
model_name = "pytorch-models/opus-mt-tc-big-en-it"
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)
translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True))
for t in translated:
print( tokenizer.decode(t, skip_special_tokens=True) )
# expected output:
# Era sempre molto rispettoso.
# Questo gatto e' nero, e' anche il cane?
```
You can also use OPUS-MT models with the transformers pipelines, for example:
```python
from transformers import pipeline
pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-en-it")
print(pipe("He was always very respectful."))
# expected output: Era sempre molto rispettoso.
```
## Benchmarks
* test set translations: [opusTCv20210807+bt_transformer-big_2022-03-13.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-ita/opusTCv20210807+bt_transformer-big_2022-03-13.test.txt)
* test set scores: [opusTCv20210807+bt_transformer-big_2022-03-13.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-ita/opusTCv20210807+bt_transformer-big_2022-03-13.eval.txt)
* benchmark results: [benchmark_results.txt](benchmark_results.txt)
* benchmark output: [benchmark_translations.zip](benchmark_translations.zip)
| langpair | testset | chr-F | BLEU | #sent | #words |
|----------|---------|-------|-------|-------|--------|
| eng-ita | tatoeba-test-v2021-08-07 | 0.72539 | 53.9 | 17320 | 116336 |
| eng-ita | flores101-devtest | 0.59002 | 29.6 | 1012 | 27306 |
| eng-ita | newssyscomb2009 | 0.60759 | 31.2 | 502 | 11551 |
| eng-ita | newstest2009 | 0.60441 | 31.6 | 2525 | 63466 |
## Acknowledgements
The work is supported by the [European Language Grid](https://www.european-language-grid.eu/) as [pilot project 2866](https://live.european-language-grid.eu/catalogue/#/resource/projects/2866), by the [FoTran project](https://www.helsinki.fi/en/researchgroups/natural-language-understanding-with-cross-lingual-grounding), funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 771113), and the [MeMAD project](https://memad.eu/), funded by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 780069. We are also grateful for the generous computational resources and IT infrastructure provided by [CSC -- IT Center for Science](https://www.csc.fi/), Finland.
## Model conversion info
* transformers version: 4.16.2
* OPUS-MT git hash: 3405783
* port time: Wed Apr 13 17:27:22 EEST 2022
* port machine: LM0-400-22516.local
|
YuZhong-Chen/q-FrozenLake-v1-4x4-noSlippery
|
YuZhong-Chen
| 2023-10-10T10:32:14Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-10-10T10:32:11Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="YuZhong-Chen/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
Helsinki-NLP/opus-mt-tc-big-sh-en
|
Helsinki-NLP
| 2023-10-10T10:32:07Z | 42,437 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"tc",
"big",
"sh",
"en",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2022-04-13T16:21:20Z |
---
language:
- bs_Latn
- en
- hr
- sh
- sr_Cyrl
- sr_Latn
tags:
- translation
- opus-mt-tc
license: cc-by-4.0
model-index:
- name: opus-mt-tc-big-sh-en
results:
- task:
name: Translation hrv-eng
type: translation
args: hrv-eng
dataset:
name: flores101-devtest
type: flores_101
args: hrv eng devtest
metrics:
- name: BLEU
type: bleu
value: 37.1
- task:
name: Translation bos_Latn-eng
type: translation
args: bos_Latn-eng
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: bos_Latn-eng
metrics:
- name: BLEU
type: bleu
value: 66.5
- task:
name: Translation hbs-eng
type: translation
args: hbs-eng
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: hbs-eng
metrics:
- name: BLEU
type: bleu
value: 56.4
- task:
name: Translation hrv-eng
type: translation
args: hrv-eng
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: hrv-eng
metrics:
- name: BLEU
type: bleu
value: 58.8
- task:
name: Translation srp_Cyrl-eng
type: translation
args: srp_Cyrl-eng
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: srp_Cyrl-eng
metrics:
- name: BLEU
type: bleu
value: 44.7
- task:
name: Translation srp_Latn-eng
type: translation
args: srp_Latn-eng
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: srp_Latn-eng
metrics:
- name: BLEU
type: bleu
value: 58.4
---
# opus-mt-tc-big-sh-en
Neural machine translation model for translating from Serbo-Croatian (sh) to English (en).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trained using the amazing framework of [Marian NMT](https://marian-nmt.github.io/), an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is taken from [OPUS](https://opus.nlpl.eu/) and training pipelines use the procedures of [OPUS-MT-train](https://github.com/Helsinki-NLP/Opus-MT-train).
* Publications: [OPUS-MT – Building open translation services for the World](https://aclanthology.org/2020.eamt-1.61/) and [The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt-1.139/) (Please, cite if you use this model.)
```
@inproceedings{tiedemann-thottingal-2020-opus,
title = "{OPUS}-{MT} {--} Building open translation services for the World",
author = {Tiedemann, J{\"o}rg and Thottingal, Santhosh},
booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
month = nov,
year = "2020",
address = "Lisboa, Portugal",
publisher = "European Association for Machine Translation",
url = "https://aclanthology.org/2020.eamt-1.61",
pages = "479--480",
}
@inproceedings{tiedemann-2020-tatoeba,
title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}",
author = {Tiedemann, J{\"o}rg},
booktitle = "Proceedings of the Fifth Conference on Machine Translation",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.wmt-1.139",
pages = "1174--1182",
}
```
## Model info
* Release: 2022-02-25
* source language(s): bos_Latn hrv srp_Cyrl srp_Latn
* target language(s): eng
* model: transformer-big
* data: opusTCv20210807+bt ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge))
* tokenization: SentencePiece (spm32k,spm32k)
* original model: [opusTCv20210807+bt_transformer-big_2022-02-25.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/hbs-eng/opusTCv20210807+bt_transformer-big_2022-02-25.zip)
* more information released models: [OPUS-MT hbs-eng README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/hbs-eng/README.md)
## Usage
A short example code:
```python
from transformers import MarianMTModel, MarianTokenizer
src_text = [
"Ispostavilo se da je istina.",
"Ovaj vikend imamo besplatne pozive."
]
model_name = "pytorch-models/opus-mt-tc-big-sh-en"
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)
translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True))
for t in translated:
print( tokenizer.decode(t, skip_special_tokens=True) )
# expected output:
# Turns out it's true.
# We got free calls this weekend.
```
You can also use OPUS-MT models with the transformers pipelines, for example:
```python
from transformers import pipeline
pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-sh-en")
print(pipe("Ispostavilo se da je istina."))
# expected output: Turns out it's true.
```
## Benchmarks
* test set translations: [opusTCv20210807+bt_transformer-big_2022-02-25.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/hbs-eng/opusTCv20210807+bt_transformer-big_2022-02-25.test.txt)
* test set scores: [opusTCv20210807+bt_transformer-big_2022-02-25.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/hbs-eng/opusTCv20210807+bt_transformer-big_2022-02-25.eval.txt)
* benchmark results: [benchmark_results.txt](benchmark_results.txt)
* benchmark output: [benchmark_translations.zip](benchmark_translations.zip)
| langpair | testset | chr-F | BLEU | #sent | #words |
|----------|---------|-------|-------|-------|--------|
| bos_Latn-eng | tatoeba-test-v2021-08-07 | 0.80010 | 66.5 | 301 | 1826 |
| hbs-eng | tatoeba-test-v2021-08-07 | 0.71744 | 56.4 | 10017 | 68934 |
| hrv-eng | tatoeba-test-v2021-08-07 | 0.73563 | 58.8 | 1480 | 10620 |
| srp_Cyrl-eng | tatoeba-test-v2021-08-07 | 0.68248 | 44.7 | 1580 | 10181 |
| srp_Latn-eng | tatoeba-test-v2021-08-07 | 0.71781 | 58.4 | 6656 | 46307 |
| hrv-eng | flores101-devtest | 0.63948 | 37.1 | 1012 | 24721 |
## Acknowledgements
The work is supported by the [European Language Grid](https://www.european-language-grid.eu/) as [pilot project 2866](https://live.european-language-grid.eu/catalogue/#/resource/projects/2866), by the [FoTran project](https://www.helsinki.fi/en/researchgroups/natural-language-understanding-with-cross-lingual-grounding), funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 771113), and the [MeMAD project](https://memad.eu/), funded by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 780069. We are also grateful for the generous computational resources and IT infrastructure provided by [CSC -- IT Center for Science](https://www.csc.fi/), Finland.
## Model conversion info
* transformers version: 4.16.2
* OPUS-MT git hash: 3405783
* port time: Wed Apr 13 19:21:10 EEST 2022
* port machine: LM0-400-22516.local
|
Helsinki-NLP/opus-mt-tc-big-zls-en
|
Helsinki-NLP
| 2023-10-10T10:31:05Z | 5,752 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"bg",
"bs",
"en",
"hr",
"mk",
"sh",
"sl",
"sr",
"zls",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2022-04-13T17:12:36Z |
---
language:
- bg
- bs
- en
- hr
- mk
- sh
- sl
- sr
- zls
tags:
- translation
- opus-mt-tc
license: cc-by-4.0
model-index:
- name: opus-mt-tc-big-zls-en
results:
- task:
name: Translation bul-eng
type: translation
args: bul-eng
dataset:
name: flores101-devtest
type: flores_101
args: bul eng devtest
metrics:
- name: BLEU
type: bleu
value: 42.0
- task:
name: Translation hrv-eng
type: translation
args: hrv-eng
dataset:
name: flores101-devtest
type: flores_101
args: hrv eng devtest
metrics:
- name: BLEU
type: bleu
value: 37.1
- task:
name: Translation mkd-eng
type: translation
args: mkd-eng
dataset:
name: flores101-devtest
type: flores_101
args: mkd eng devtest
metrics:
- name: BLEU
type: bleu
value: 43.2
- task:
name: Translation slv-eng
type: translation
args: slv-eng
dataset:
name: flores101-devtest
type: flores_101
args: slv eng devtest
metrics:
- name: BLEU
type: bleu
value: 35.2
- task:
name: Translation srp_Cyrl-eng
type: translation
args: srp_Cyrl-eng
dataset:
name: flores101-devtest
type: flores_101
args: srp_Cyrl eng devtest
metrics:
- name: BLEU
type: bleu
value: 36.8
- task:
name: Translation bos_Latn-eng
type: translation
args: bos_Latn-eng
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: bos_Latn-eng
metrics:
- name: BLEU
type: bleu
value: 66.5
- task:
name: Translation bul-eng
type: translation
args: bul-eng
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: bul-eng
metrics:
- name: BLEU
type: bleu
value: 59.3
- task:
name: Translation hbs-eng
type: translation
args: hbs-eng
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: hbs-eng
metrics:
- name: BLEU
type: bleu
value: 57.3
- task:
name: Translation hrv-eng
type: translation
args: hrv-eng
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: hrv-eng
metrics:
- name: BLEU
type: bleu
value: 59.2
- task:
name: Translation mkd-eng
type: translation
args: mkd-eng
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: mkd-eng
metrics:
- name: BLEU
type: bleu
value: 57.4
- task:
name: Translation slv-eng
type: translation
args: slv-eng
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: slv-eng
metrics:
- name: BLEU
type: bleu
value: 23.5
- task:
name: Translation srp_Cyrl-eng
type: translation
args: srp_Cyrl-eng
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: srp_Cyrl-eng
metrics:
- name: BLEU
type: bleu
value: 47.0
- task:
name: Translation srp_Latn-eng
type: translation
args: srp_Latn-eng
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: srp_Latn-eng
metrics:
- name: BLEU
type: bleu
value: 58.5
---
# opus-mt-tc-big-zls-en
Neural machine translation model for translating from South Slavic languages (zls) to English (en).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trained using the amazing framework of [Marian NMT](https://marian-nmt.github.io/), an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is taken from [OPUS](https://opus.nlpl.eu/) and training pipelines use the procedures of [OPUS-MT-train](https://github.com/Helsinki-NLP/Opus-MT-train).
* Publications: [OPUS-MT – Building open translation services for the World](https://aclanthology.org/2020.eamt-1.61/) and [The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt-1.139/) (Please, cite if you use this model.)
```
@inproceedings{tiedemann-thottingal-2020-opus,
title = "{OPUS}-{MT} {--} Building open translation services for the World",
author = {Tiedemann, J{\"o}rg and Thottingal, Santhosh},
booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
month = nov,
year = "2020",
address = "Lisboa, Portugal",
publisher = "European Association for Machine Translation",
url = "https://aclanthology.org/2020.eamt-1.61",
pages = "479--480",
}
@inproceedings{tiedemann-2020-tatoeba,
title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}",
author = {Tiedemann, J{\"o}rg},
booktitle = "Proceedings of the Fifth Conference on Machine Translation",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.wmt-1.139",
pages = "1174--1182",
}
```
## Model info
* Release: 2022-03-17
* source language(s): bos_Latn bul hbs hrv mkd slv srp_Cyrl srp_Latn
* target language(s): eng
* model: transformer-big
* data: opusTCv20210807+bt ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge))
* tokenization: SentencePiece (spm32k,spm32k)
* original model: [opusTCv20210807+bt_transformer-big_2022-03-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/zls-eng/opusTCv20210807+bt_transformer-big_2022-03-17.zip)
* more information released models: [OPUS-MT zls-eng README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/zls-eng/README.md)
## Usage
A short example code:
```python
from transformers import MarianMTModel, MarianTokenizer
src_text = [
"Да не би случайно Том да остави Мери да кара колата?",
"Какво е времето днес?"
]
model_name = "pytorch-models/opus-mt-tc-big-zls-en"
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)
translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True))
for t in translated:
print( tokenizer.decode(t, skip_special_tokens=True) )
# expected output:
# Did Tom just let Mary drive the car?
# What's the weather like today?
```
You can also use OPUS-MT models with the transformers pipelines, for example:
```python
from transformers import pipeline
pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-zls-en")
print(pipe("Да не би случайно Том да остави Мери да кара колата?"))
# expected output: Did Tom just let Mary drive the car?
```
## Benchmarks
* test set translations: [opusTCv20210807+bt_transformer-big_2022-03-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zls-eng/opusTCv20210807+bt_transformer-big_2022-03-17.test.txt)
* test set scores: [opusTCv20210807+bt_transformer-big_2022-03-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zls-eng/opusTCv20210807+bt_transformer-big_2022-03-17.eval.txt)
* benchmark results: [benchmark_results.txt](benchmark_results.txt)
* benchmark output: [benchmark_translations.zip](benchmark_translations.zip)
| langpair | testset | chr-F | BLEU | #sent | #words |
|----------|---------|-------|-------|-------|--------|
| bos_Latn-eng | tatoeba-test-v2021-08-07 | 0.79339 | 66.5 | 301 | 1826 |
| bul-eng | tatoeba-test-v2021-08-07 | 0.72656 | 59.3 | 10000 | 71872 |
| hbs-eng | tatoeba-test-v2021-08-07 | 0.71783 | 57.3 | 10017 | 68934 |
| hrv-eng | tatoeba-test-v2021-08-07 | 0.74066 | 59.2 | 1480 | 10620 |
| mkd-eng | tatoeba-test-v2021-08-07 | 0.70043 | 57.4 | 10010 | 65667 |
| slv-eng | tatoeba-test-v2021-08-07 | 0.39534 | 23.5 | 2495 | 16940 |
| srp_Cyrl-eng | tatoeba-test-v2021-08-07 | 0.67628 | 47.0 | 1580 | 10181 |
| srp_Latn-eng | tatoeba-test-v2021-08-07 | 0.71878 | 58.5 | 6656 | 46307 |
| bul-eng | flores101-devtest | 0.67375 | 42.0 | 1012 | 24721 |
| hrv-eng | flores101-devtest | 0.63914 | 37.1 | 1012 | 24721 |
| mkd-eng | flores101-devtest | 0.67444 | 43.2 | 1012 | 24721 |
| slv-eng | flores101-devtest | 0.62087 | 35.2 | 1012 | 24721 |
| srp_Cyrl-eng | flores101-devtest | 0.67810 | 36.8 | 1012 | 24721 |
## Acknowledgements
The work is supported by the [European Language Grid](https://www.european-language-grid.eu/) as [pilot project 2866](https://live.european-language-grid.eu/catalogue/#/resource/projects/2866), by the [FoTran project](https://www.helsinki.fi/en/researchgroups/natural-language-understanding-with-cross-lingual-grounding), funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 771113), and the [MeMAD project](https://memad.eu/), funded by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 780069. We are also grateful for the generous computational resources and IT infrastructure provided by [CSC -- IT Center for Science](https://www.csc.fi/), Finland.
## Model conversion info
* transformers version: 4.16.2
* OPUS-MT git hash: 3405783
* port time: Wed Apr 13 20:12:26 EEST 2022
* port machine: LM0-400-22516.local
|
Helsinki-NLP/opus-mt-tc-big-en-ko
|
Helsinki-NLP
| 2023-10-10T10:29:58Z | 1,276 | 14 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"en",
"ko",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2022-08-12T08:02:12Z |
---
language:
- en
- ko
tags:
- translation
- opus-mt-tc
license: cc-by-4.0
model-index:
- name: opus-mt-tc-big-en-ko
results:
- task:
name: Translation eng-kor
type: translation
args: eng-kor
dataset:
name: flores101-devtest
type: flores_101
args: eng kor devtest
metrics:
- name: BLEU
type: bleu
value: 13.7
- name: chr-F
type: chrf
value: 0.36399
---
# opus-mt-tc-big-en-ko
## Table of Contents
- [Model Details](#model-details)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [How to Get Started With the Model](#how-to-get-started-with-the-model)
- [Training](#training)
- [Evaluation](#evaluation)
- [Citation Information](#citation-information)
- [Acknowledgements](#acknowledgements)
## Model Details
Neural machine translation model for translating from English (en) to Korean (ko).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trained using the amazing framework of [Marian NMT](https://marian-nmt.github.io/), an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is taken from [OPUS](https://opus.nlpl.eu/) and training pipelines use the procedures of [OPUS-MT-train](https://github.com/Helsinki-NLP/Opus-MT-train).
**Model Description:**
- **Developed by:** Language Technology Research Group at the University of Helsinki
- **Model Type:** Translation (transformer-big)
- **Release**: 2022-07-28
- **License:** CC-BY-4.0
- **Language(s):**
- Source Language(s):
- Target Language(s):
- Valid Target Language Labels:
- **Original Model**: [opusTCv20210807-sepvoc_transformer-big_2022-07-28.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-kor/opusTCv20210807-sepvoc_transformer-big_2022-07-28.zip)
- **Resources for more information:**
- [OPUS-MT-train GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train)
- More information about released models for this language pair: [OPUS-MT eng-kor README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-kor/README.md)
- [More information about MarianNMT models in the transformers library](https://huggingface.co/docs/transformers/model_doc/marian)
- [Tatoeba Translation Challenge](https://github.com/Helsinki-NLP/Tatoeba-Challenge/
This is a multilingual translation model with multiple target languages. A sentence initial language token is required in the form of `>>id<<` (id = valid target language ID), e.g. `>><<`
## Uses
This model can be used for translation and text-to-text generation.
## Risks, Limitations and Biases
**CONTENT WARNING: Readers should be aware that the model is trained on various public data sets that may contain content that is disturbing, offensive, and can propagate historical and current stereotypes.**
Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)).
## How to Get Started With the Model
A short example code:
```python
from transformers import MarianMTModel, MarianTokenizer
src_text = [
"2, 4, 6 etc. are even numbers.",
"Yes."
]
model_name = "pytorch-models/opus-mt-tc-big-en-ko"
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)
translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True))
for t in translated:
print( tokenizer.decode(t, skip_special_tokens=True) )
# expected output:
# 2, 4, 6 등은 짝수입니다.
# 그래
```
You can also use OPUS-MT models with the transformers pipelines, for example:
```python
from transformers import pipeline
pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-en-ko")
print(pipe("2, 4, 6 etc. are even numbers."))
# expected output: 2, 4, 6 등은 짝수입니다.
```
## Training
- **Data**: opusTCv20210807 ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge))
- **Pre-processing**: SentencePiece (spm32k,spm32k)
- **Model Type:** transformer-big
- **Original MarianNMT Model**: [opusTCv20210807-sepvoc_transformer-big_2022-07-28.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-kor/opusTCv20210807-sepvoc_transformer-big_2022-07-28.zip)
- **Training Scripts**: [GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train)
## Evaluation
* test set translations: [opusTCv20210807-sepvoc_transformer-big_2022-07-28.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-kor/opusTCv20210807-sepvoc_transformer-big_2022-07-28.test.txt)
* test set scores: [opusTCv20210807-sepvoc_transformer-big_2022-07-28.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-kor/opusTCv20210807-sepvoc_transformer-big_2022-07-28.eval.txt)
* benchmark results: [benchmark_results.txt](benchmark_results.txt)
* benchmark output: [benchmark_translations.zip](benchmark_translations.zip)
| langpair | testset | chr-F | BLEU | #sent | #words |
|----------|---------|-------|-------|-------|--------|
## Citation Information
* Publications: [OPUS-MT – Building open translation services for the World](https://aclanthology.org/2020.eamt-1.61/) and [The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt-1.139/) (Please, cite if you use this model.)
```
@inproceedings{tiedemann-thottingal-2020-opus,
title = "{OPUS}-{MT} {--} Building open translation services for the World",
author = {Tiedemann, J{\"o}rg and Thottingal, Santhosh},
booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
month = nov,
year = "2020",
address = "Lisboa, Portugal",
publisher = "European Association for Machine Translation",
url = "https://aclanthology.org/2020.eamt-1.61",
pages = "479--480",
}
@inproceedings{tiedemann-2020-tatoeba,
title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}",
author = {Tiedemann, J{\"o}rg},
booktitle = "Proceedings of the Fifth Conference on Machine Translation",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.wmt-1.139",
pages = "1174--1182",
}
```
## Acknowledgements
The work is supported by the [European Language Grid](https://www.european-language-grid.eu/) as [pilot project 2866](https://live.european-language-grid.eu/catalogue/#/resource/projects/2866), by the [FoTran project](https://www.helsinki.fi/en/researchgroups/natural-language-understanding-with-cross-lingual-grounding), funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 771113), and the [MeMAD project](https://memad.eu/), funded by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 780069. We are also grateful for the generous computational resources and IT infrastructure provided by [CSC -- IT Center for Science](https://www.csc.fi/), Finland.
## Model conversion info
* transformers version: 4.16.2
* OPUS-MT git hash: 8b9f0b0
* port time: Fri Aug 12 11:02:03 EEST 2022
* port machine: LM0-400-22516.local
|
Nga3110/nha97
|
Nga3110
| 2023-10-10T10:27:55Z | 1 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"region:us"
] |
text-to-image
| 2023-10-10T09:59:36Z |
---
library_name: diffusers
pipeline_tag: text-to-image
---
|
srushtibhavsar/sqaud-bloom-3b
|
srushtibhavsar
| 2023-10-10T10:25:44Z | 0 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:bigscience/bloom-1b7",
"base_model:adapter:bigscience/bloom-1b7",
"region:us"
] | null | 2023-10-10T10:25:43Z |
---
library_name: peft
base_model: bigscience/bloom-1b7
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
### Framework versions
- PEFT 0.6.0.dev0
|
cys/Reinforce-v1
|
cys
| 2023-10-10T10:23:24Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-10-10T10:23:14Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
jcinque/ppo-LunarLander-v2
|
jcinque
| 2023-10-10T10:21:50Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-10-10T10:21:25Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 268.91 +/- 22.15
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
mys/ggml_llava-v1.5-13b
|
mys
| 2023-10-10T10:20:06Z | 1,078 | 53 | null |
[
"gguf",
"llava",
"lmm",
"ggml",
"llama.cpp",
"endpoints_compatible",
"region:us"
] | null | 2023-10-10T10:04:00Z |
---
tags:
- llava
- lmm
- ggml
- llama.cpp
---
# ggml_llava-v1.5-13b
This repo contains GGUF files to inference [llava-v1.5-13b](https://huggingface.co/liuhaotian/llava-v1.5-13b) with [llama.cpp](https://github.com/ggerganov/llama.cpp) end-to-end without any extra dependency.
**Note**: The `mmproj-model-f16.gguf` file structure is experimental and may change. Always use the latest code in llama.cpp.
|
srjn/q-Taxi-v3
|
srjn
| 2023-10-10T10:16:01Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-10-10T10:15:59Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="srjn/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
Dhineshk/TestDocumentQuestionAnswering
|
Dhineshk
| 2023-10-10T10:15:36Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"layoutlmv2",
"document-question-answering",
"generated_from_trainer",
"base_model:microsoft/layoutlmv2-base-uncased",
"base_model:finetune:microsoft/layoutlmv2-base-uncased",
"license:cc-by-nc-sa-4.0",
"endpoints_compatible",
"region:us"
] |
document-question-answering
| 2023-09-27T07:48:00Z |
---
license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv2-base-uncased
tags:
- generated_from_trainer
model-index:
- name: layoutlmv2-base-uncased_finetuned_docvqa
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# layoutlmv2-base-uncased_finetuned_docvqa
This model is a fine-tuned version of [microsoft/layoutlmv2-base-uncased](https://huggingface.co/microsoft/layoutlmv2-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 5.3353
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.153 | 0.22 | 50 | 5.3909 |
| 0.2793 | 0.44 | 100 | 5.0150 |
| 0.2634 | 0.66 | 150 | 4.6620 |
| 0.5192 | 0.88 | 200 | 4.7826 |
| 0.3096 | 1.11 | 250 | 4.9532 |
| 0.2638 | 1.33 | 300 | 5.2584 |
| 0.4727 | 1.55 | 350 | 4.0943 |
| 0.2763 | 1.77 | 400 | 4.8408 |
| 1.0425 | 1.99 | 450 | 5.0344 |
| 0.4477 | 2.21 | 500 | 4.9084 |
| 0.3266 | 2.43 | 550 | 5.0996 |
| 0.3085 | 2.65 | 600 | 4.4858 |
| 0.4648 | 2.88 | 650 | 4.0630 |
| 0.1845 | 3.1 | 700 | 5.3969 |
| 0.1616 | 3.32 | 750 | 4.8225 |
| 0.1752 | 3.54 | 800 | 5.2945 |
| 0.1877 | 3.76 | 850 | 5.2358 |
| 0.3172 | 3.98 | 900 | 5.2205 |
| 0.1627 | 4.2 | 950 | 4.9991 |
| 0.2548 | 4.42 | 1000 | 4.6917 |
| 0.1566 | 4.65 | 1050 | 5.1266 |
| 0.2616 | 4.87 | 1100 | 4.3241 |
| 0.1199 | 5.09 | 1150 | 4.9821 |
| 0.1372 | 5.31 | 1200 | 5.0838 |
| 0.1198 | 5.53 | 1250 | 5.0156 |
| 0.0558 | 5.75 | 1300 | 4.8638 |
| 0.1331 | 5.97 | 1350 | 4.9492 |
| 0.0689 | 6.19 | 1400 | 4.6926 |
| 0.0912 | 6.42 | 1450 | 4.5153 |
| 0.0495 | 6.64 | 1500 | 4.6969 |
| 0.0853 | 6.86 | 1550 | 4.7690 |
| 0.1072 | 7.08 | 1600 | 4.6783 |
| 0.034 | 7.3 | 1650 | 4.7351 |
| 0.2999 | 7.52 | 1700 | 4.5185 |
| 0.0763 | 7.74 | 1750 | 4.5825 |
| 0.0799 | 7.96 | 1800 | 4.7218 |
| 0.0343 | 8.19 | 1850 | 5.1508 |
| 0.0396 | 8.41 | 1900 | 5.4893 |
| 0.033 | 8.63 | 1950 | 5.5167 |
| 0.0295 | 8.85 | 2000 | 5.6252 |
| 0.2303 | 9.07 | 2050 | 4.7031 |
| 0.088 | 9.29 | 2100 | 4.7323 |
| 0.0666 | 9.51 | 2150 | 4.8688 |
| 0.0597 | 9.73 | 2200 | 5.6007 |
| 0.0615 | 9.96 | 2250 | 5.5403 |
| 0.1003 | 10.18 | 2300 | 5.3198 |
| 0.0457 | 10.4 | 2350 | 5.4828 |
| 0.0391 | 10.62 | 2400 | 5.5312 |
| 0.0325 | 10.84 | 2450 | 5.7410 |
| 0.0147 | 11.06 | 2500 | 5.8749 |
| 0.1013 | 11.28 | 2550 | 5.6522 |
| 0.001 | 11.5 | 2600 | 5.7776 |
| 0.0002 | 11.73 | 2650 | 5.8431 |
| 0.03 | 11.95 | 2700 | 5.9751 |
| 0.0452 | 12.17 | 2750 | 5.6928 |
| 0.0002 | 12.39 | 2800 | 5.6264 |
| 0.0109 | 12.61 | 2850 | 5.2688 |
| 0.0801 | 12.83 | 2900 | 5.2780 |
| 0.0216 | 13.05 | 2950 | 5.3691 |
| 0.0002 | 13.27 | 3000 | 5.5237 |
| 0.0092 | 13.5 | 3050 | 5.3662 |
| 0.0124 | 13.72 | 3100 | 5.4474 |
| 0.0515 | 13.94 | 3150 | 5.3623 |
| 0.0032 | 14.16 | 3200 | 5.4168 |
| 0.0051 | 14.38 | 3250 | 5.2897 |
| 0.0002 | 14.6 | 3300 | 5.3205 |
| 0.014 | 14.82 | 3350 | 5.2114 |
| 0.0004 | 15.04 | 3400 | 5.2342 |
| 0.0104 | 15.27 | 3450 | 5.2562 |
| 0.0107 | 15.49 | 3500 | 5.1112 |
| 0.0002 | 15.71 | 3550 | 5.1515 |
| 0.0002 | 15.93 | 3600 | 5.2054 |
| 0.0002 | 16.15 | 3650 | 5.1968 |
| 0.0003 | 16.37 | 3700 | 5.3196 |
| 0.0246 | 16.59 | 3750 | 5.3111 |
| 0.0054 | 16.81 | 3800 | 5.3335 |
| 0.0001 | 17.04 | 3850 | 5.3488 |
| 0.0243 | 17.26 | 3900 | 5.2597 |
| 0.0217 | 17.48 | 3950 | 5.2834 |
| 0.0002 | 17.7 | 4000 | 5.2947 |
| 0.0002 | 17.92 | 4050 | 5.3131 |
| 0.0001 | 18.14 | 4100 | 5.3240 |
| 0.0016 | 18.36 | 4150 | 5.3129 |
| 0.0133 | 18.58 | 4200 | 5.3241 |
| 0.0002 | 18.81 | 4250 | 5.3382 |
| 0.0159 | 19.03 | 4300 | 5.3764 |
| 0.003 | 19.25 | 4350 | 5.3776 |
| 0.0516 | 19.47 | 4400 | 5.3389 |
| 0.016 | 19.69 | 4450 | 5.3275 |
| 0.0105 | 19.91 | 4500 | 5.3353 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cpu
- Datasets 2.14.5
- Tokenizers 0.13.3
|
amanpelago/pelago-sentence-transformer-v1
|
amanpelago
| 2023-10-10T10:13:38Z | 1 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2023-10-10T04:28:10Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# amanpelago/pelago-sentence-transformer-v1
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('amanpelago/pelago-sentence-transformer-v1')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=amanpelago/pelago-sentence-transformer-v1)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 3181 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.TripletLoss.TripletLoss` with parameters:
```
{'distance_metric': 'TripletDistanceMetric.EUCLIDEAN', 'triplet_margin': 5}
```
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 10000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False})
(2): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
digiplay/BeenReal_diffusers
|
digiplay
| 2023-10-10T10:11:22Z | 5,709 | 6 |
diffusers
|
[
"diffusers",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-06-25T22:47:35Z |
---
license: other
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
inference: true
---
Model info:
https://pixai.art/model/1621642635946443255
https://aitool.ai/model/76296
|
SparkExpedition/Ticket-Classifier-dolly-7B
|
SparkExpedition
| 2023-10-10T10:04:59Z | 0 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:diegi97/dolly-v2-6.9b-sharded-bf16",
"base_model:adapter:diegi97/dolly-v2-6.9b-sharded-bf16",
"region:us"
] | null | 2023-10-10T09:15:45Z |
---
library_name: peft
base_model: diegi97/dolly-v2-6.9b-sharded-bf16
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.6.0.dev0
|
ali5341/videomae-base-finetuned-ucf101-subset
|
ali5341
| 2023-10-10T10:03:57Z | 59 | 0 |
transformers
|
[
"transformers",
"pytorch",
"videomae",
"video-classification",
"generated_from_trainer",
"base_model:MCG-NJU/videomae-base",
"base_model:finetune:MCG-NJU/videomae-base",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] |
video-classification
| 2023-10-05T17:07:37Z |
---
license: cc-by-nc-4.0
base_model: MCG-NJU/videomae-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: videomae-base-finetuned-ucf101-subset
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# videomae-base-finetuned-ucf101-subset
This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6506
- Accuracy: 0.5587
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 29760
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 4.1471 | 0.2 | 5952 | 4.1601 | 0.0382 |
| 4.0729 | 1.2 | 11904 | 3.6134 | 0.1013 |
| 2.6787 | 2.2 | 17856 | 2.9397 | 0.2193 |
| 1.722 | 3.2 | 23808 | 2.0974 | 0.4241 |
| 0.6968 | 4.2 | 29760 | 1.7135 | 0.5362 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
|
Utkarshquytech/sd-german-shepherd
|
Utkarshquytech
| 2023-10-10T10:03:20Z | 0 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-10-10T09:50:27Z |
---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### sd-german-shepherd Dreambooth model trained by Utkarshquytech with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
.jpg)
.jpg)
.jpg)
.jpg)
.jpg)
.jpg)
|
digiplay/elegantEntropy_v1.1
|
digiplay
| 2023-10-10T09:57:04Z | 249 | 2 |
diffusers
|
[
"diffusers",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-06-22T01:44:34Z |
---
license: other
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
inference: true
---
Model info :
https://civitai.com/models/78341/elegant-entropy
Original Author's DEMO images :
Sample image I made:
|
aghorbani/opus-mt-tc-big-ar-en
|
aghorbani
| 2023-10-10T09:56:54Z | 112 | 2 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"ar",
"en",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2023-11-14T08:29:32Z |
---
language:
- ar
- en
tags:
- translation
- opus-mt-tc
license: cc-by-4.0
model-index:
- name: opus-mt-tc-big-ar-en
results:
- task:
name: Translation ara-eng
type: translation
args: ara-eng
dataset:
name: flores101-devtest
type: flores_101
args: ara eng devtest
metrics:
- name: BLEU
type: bleu
value: 42.6
- task:
name: Translation ara-eng
type: translation
args: ara-eng
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: ara-eng
metrics:
- name: BLEU
type: bleu
value: 47.3
- task:
name: Translation ara-eng
type: translation
args: ara-eng
dataset:
name: tico19-test
type: tico19-test
args: ara-eng
metrics:
- name: BLEU
type: bleu
value: 44.4
---
# opus-mt-tc-big-ar-en
Neural machine translation model for translating from Arabic (ar) to English (en).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trained using the amazing framework of [Marian NMT](https://marian-nmt.github.io/), an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is taken from [OPUS](https://opus.nlpl.eu/) and training pipelines use the procedures of [OPUS-MT-train](https://github.com/Helsinki-NLP/Opus-MT-train).
* Publications: [OPUS-MT – Building open translation services for the World](https://aclanthology.org/2020.eamt-1.61/) and [The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt-1.139/) (Please, cite if you use this model.)
```
@inproceedings{tiedemann-thottingal-2020-opus,
title = "{OPUS}-{MT} {--} Building open translation services for the World",
author = {Tiedemann, J{\"o}rg and Thottingal, Santhosh},
booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
month = nov,
year = "2020",
address = "Lisboa, Portugal",
publisher = "European Association for Machine Translation",
url = "https://aclanthology.org/2020.eamt-1.61",
pages = "479--480",
}
@inproceedings{tiedemann-2020-tatoeba,
title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}",
author = {Tiedemann, J{\"o}rg},
booktitle = "Proceedings of the Fifth Conference on Machine Translation",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.wmt-1.139",
pages = "1174--1182",
}
```
## Model info
* Release: 2022-03-09
* source language(s): afb ara arz
* target language(s): eng
* model: transformer-big
* data: opusTCv20210807+bt ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge))
* tokenization: SentencePiece (spm32k,spm32k)
* original model: [opusTCv20210807+bt_transformer-big_2022-03-09.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/ara-eng/opusTCv20210807+bt_transformer-big_2022-03-09.zip)
* more information released models: [OPUS-MT ara-eng README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ara-eng/README.md)
## Usage
A short example code:
```python
from transformers import MarianMTModel, MarianTokenizer
src_text = [
"اتبع قلبك فحسب.",
"وين راهي دّوش؟"
]
model_name = "pytorch-models/opus-mt-tc-big-ar-en"
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)
translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True))
for t in translated:
print( tokenizer.decode(t, skip_special_tokens=True) )
# expected output:
# Just follow your heart.
# Wayne Rahi Dosh?
```
You can also use OPUS-MT models with the transformers pipelines, for example:
```python
from transformers import pipeline
pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-ar-en")
print(pipe("اتبع قلبك فحسب."))
# expected output: Just follow your heart.
```
## Benchmarks
* test set translations: [opusTCv20210807+bt_transformer-big_2022-03-09.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/ara-eng/opusTCv20210807+bt_transformer-big_2022-03-09.test.txt)
* test set scores: [opusTCv20210807+bt_transformer-big_2022-03-09.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/ara-eng/opusTCv20210807+bt_transformer-big_2022-03-09.eval.txt)
* benchmark results: [benchmark_results.txt](benchmark_results.txt)
* benchmark output: [benchmark_translations.zip](benchmark_translations.zip)
| langpair | testset | chr-F | BLEU | #sent | #words |
|----------|---------|-------|-------|-------|--------|
| ara-eng | tatoeba-test-v2021-08-07 | 0.63477 | 47.3 | 10305 | 76975 |
| ara-eng | flores101-devtest | 0.66987 | 42.6 | 1012 | 24721 |
| ara-eng | tico19-test | 0.68521 | 44.4 | 2100 | 56323 |
## Acknowledgements
The work is supported by the [European Language Grid](https://www.european-language-grid.eu/) as [pilot project 2866](https://live.european-language-grid.eu/catalogue/#/resource/projects/2866), by the [FoTran project](https://www.helsinki.fi/en/researchgroups/natural-language-understanding-with-cross-lingual-grounding), funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 771113), and the [MeMAD project](https://memad.eu/), funded by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 780069. We are also grateful for the generous computational resources and IT infrastructure provided by [CSC -- IT Center for Science](https://www.csc.fi/), Finland.
## Model conversion info
* transformers version: 4.16.2
* OPUS-MT git hash: 3405783
* port time: Wed Apr 13 18:17:57 EEST 2022
* port machine: LM0-400-22516.local
|
Srish117/gpt2-wikitext2
|
Srish117
| 2023-10-10T09:53:51Z | 226 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-10-10T09:06:40Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: gpt2-wikitext2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt2-wikitext2
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 6.1117
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 6.5587 | 1.0 | 2249 | 6.4672 |
| 6.1907 | 2.0 | 4498 | 6.1993 |
| 6.0153 | 3.0 | 6747 | 6.1117 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
rbel/llama2-test-new
|
rbel
| 2023-10-10T09:43:01Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"question-answering",
"en",
"dataset:rbel/jobtitles",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-10-10T08:46:22Z |
---
license: apache-2.0
datasets:
- rbel/jobtitles
language:
- en
library_name: transformers
pipeline_tag: question-answering
---
|
m-aliabbas1/med_ner_2
|
m-aliabbas1
| 2023-10-10T09:34:55Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"base_model:prajjwal1/bert-tiny",
"base_model:finetune:prajjwal1/bert-tiny",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-10-10T09:34:42Z |
---
license: mit
base_model: prajjwal1/bert-tiny
tags:
- generated_from_trainer
model-index:
- name: med_ner_2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# med_ner_2
This model is a fine-tuned version of [prajjwal1/bert-tiny](https://huggingface.co/prajjwal1/bert-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0295
- Overall Precision: 1.0
- Overall Recall: 0.9831
- Overall F1: 0.9915
- Overall Accuracy: 0.9977
- Age F1: 0.9888
- Yob F1: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 250
### Training results
| Training Loss | Epoch | Step | Validation Loss | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | Age F1 | Yob F1 |
|:-------------:|:------:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|:------:|:------:|
| 0.0 | 47.62 | 1000 | 0.0364 | 1.0 | 0.9831 | 0.9915 | 0.9977 | 0.9888 | 1.0 |
| 0.0 | 95.24 | 2000 | 0.0363 | 1.0 | 0.9831 | 0.9915 | 0.9977 | 0.9888 | 1.0 |
| 0.0 | 142.86 | 3000 | 0.0279 | 1.0 | 0.9831 | 0.9915 | 0.9977 | 0.9888 | 1.0 |
| 0.0 | 190.48 | 4000 | 0.0265 | 1.0 | 0.9831 | 0.9915 | 0.9977 | 0.9888 | 1.0 |
| 0.0 | 238.1 | 5000 | 0.0295 | 1.0 | 0.9831 | 0.9915 | 0.9977 | 0.9888 | 1.0 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
|
shubhamgantayat/EleutherAI-gpt-neo-125m-wet-strength-model
|
shubhamgantayat
| 2023-10-10T09:34:20Z | 117 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"generated_from_trainer",
"base_model:EleutherAI/gpt-neo-125m",
"base_model:finetune:EleutherAI/gpt-neo-125m",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-10-10T09:25:52Z |
---
license: mit
base_model: EleutherAI/gpt-neo-125m
tags:
- generated_from_trainer
model-index:
- name: EleutherAI-gpt-neo-125m-wet-strength-model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# EleutherAI-gpt-neo-125m-wet-strength-model
This model is a fine-tuned version of [EleutherAI/gpt-neo-125m](https://huggingface.co/EleutherAI/gpt-neo-125m) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
|
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