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MartinoMensio/racism-models-m-vote-nonstrict-epoch-4
|
MartinoMensio
|
bert
| 4 | 4 |
transformers
| 0 |
text-classification
| true | false | false |
mit
|
['es']
| null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
[]
| false | true | true | 4,101 | false |
### Description
This model is a fine-tuned version of [BETO (spanish bert)](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) that has been trained on the *Datathon Against Racism* dataset (2022)
We performed several experiments that will be described in the upcoming paper "Estimating Ground Truth in a Low-labelled Data Regime:A Study of Racism Detection in Spanish" (NEATClasS 2022)
We applied 6 different methods ground-truth estimations, and for each one we performed 4 epochs of fine-tuning. The result is made of 24 models:
| method | epoch 1 | epoch 3 | epoch 3 | epoch 4 |
|--- |--- |--- |--- |--- |
| raw-label | [raw-label-epoch-1](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-1) | [raw-label-epoch-2](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-2) | [raw-label-epoch-3](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-3) | [raw-label-epoch-4](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-4) |
| m-vote-strict | [m-vote-strict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-1) | [m-vote-strict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-2) | [m-vote-strict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-3) | [m-vote-strict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-4) |
| m-vote-nonstrict | [m-vote-nonstrict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-1) | [m-vote-nonstrict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-2) | [m-vote-nonstrict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-3) | [m-vote-nonstrict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-4) |
| regression-w-m-vote | [regression-w-m-vote-epoch-1](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-1) | [regression-w-m-vote-epoch-2](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-2) | [regression-w-m-vote-epoch-3](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-3) | [regression-w-m-vote-epoch-4](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-4) |
| w-m-vote-strict | [w-m-vote-strict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-1) | [w-m-vote-strict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-2) | [w-m-vote-strict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-3) | [w-m-vote-strict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-4) |
| w-m-vote-nonstrict | [w-m-vote-nonstrict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-1) | [w-m-vote-nonstrict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-2) | [w-m-vote-nonstrict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-3) | [w-m-vote-nonstrict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-4) |
This model is `m-vote-nonstrict-epoch-4`
### Usage
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
model_name = 'm-vote-nonstrict-epoch-4'
tokenizer = AutoTokenizer.from_pretrained("dccuchile/bert-base-spanish-wwm-uncased")
full_model_path = f'MartinoMensio/racism-models-{model_name}'
model = AutoModelForSequenceClassification.from_pretrained(full_model_path)
pipe = pipeline("text-classification", model = model, tokenizer = tokenizer)
texts = [
'y porqué es lo que hay que hacer con los menas y con los adultos también!!!! NO a los inmigrantes ilegales!!!!',
'Es que los judíos controlan el mundo'
]
print(pipe(texts))
# [{'label': 'racist', 'score': 0.9791656136512756}, {'label': 'non-racist', 'score': 0.996966540813446}]
```
For more details, see https://github.com/preyero/neatclass22
|
359fcdb228b4e3631b68c2c947acd167
|
Geotrend/distilbert-base-en-zh-hi-cased
|
Geotrend
|
distilbert
| 6 | 6 |
transformers
| 0 |
fill-mask
| true | false | false |
apache-2.0
|
['multilingual']
|
['wikipedia']
| null | 1 | 1 | 0 | 0 | 0 | 0 | 0 |
[]
| false | true | true | 1,233 | false |
# distilbert-base-en-zh-hi-cased
We are sharing smaller versions of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) that handle a custom number of languages.
Our versions give exactly the same representations produced by the original model which preserves the original accuracy.
For more information please visit our paper: [Load What You Need: Smaller Versions of Multilingual BERT](https://www.aclweb.org/anthology/2020.sustainlp-1.16.pdf).
## How to use
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("Geotrend/distilbert-base-en-zh-hi-cased")
model = AutoModel.from_pretrained("Geotrend/distilbert-base-en-zh-hi-cased")
```
To generate other smaller versions of multilingual transformers please visit [our Github repo](https://github.com/Geotrend-research/smaller-transformers).
### How to cite
```bibtex
@inproceedings{smallermdistilbert,
title={Load What You Need: Smaller Versions of Mutlilingual BERT},
author={Abdaoui, Amine and Pradel, Camille and Sigel, Grégoire},
booktitle={SustaiNLP / EMNLP},
year={2020}
}
```
## Contact
Please contact amine@geotrend.fr for any question, feedback or request.
|
c3e211936a9a51ceeac86c744f3b6301
|
alxdfy/noggles-fastdb-4800
|
alxdfy
| null | 20 | 3 |
diffusers
| 0 |
text-to-image
| false | false | false |
creativeml-openrail-m
| null | null | null | 2 | 2 | 0 | 0 | 0 | 0 | 0 |
['text-to-image']
| false | true | true | 1,337 | false |
### noggles_fastdb_4800 on Stable Diffusion via Dreambooth trained on the [fast-DreamBooth.ipynb by TheLastBen](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
#### Model by alxdfy
This your the Stable Diffusion model fine-tuned the noggles_fastdb_4800 concept taught to Stable Diffusion with Dreambooth.
It can be used by modifying the `instance_prompt(s)`: **test.png**
You can also train your own concepts and upload them to the library by using [the fast-DremaBooth.ipynb by TheLastBen](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb).
You can run your new concept via A1111 Colab :[Fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Or you can run your new concept via `diffusers`: [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb), [Spaces with the Public Concepts loaded](https://huggingface.co/spaces/sd-dreambooth-library/stable-diffusion-dreambooth-concepts)
Sample pictures of this concept:
test.png
|
71c96e7c6dbaef6c68247aae2d14b7e0
|
CLTL/icf-levels-adm
|
CLTL
|
roberta
| 11 | 10 |
transformers
| 1 |
text-classification
| true | false | false |
mit
|
['nl']
| null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
[]
| false | true | true | 3,304 | false |
# Regression Model for Respiration Functioning Levels (ICF b440)
## Description
A fine-tuned regression model that assigns a functioning level to Dutch sentences describing respiration functions. The model is based on a pre-trained Dutch medical language model ([link to be added]()): a RoBERTa model, trained from scratch on clinical notes of the Amsterdam UMC. To detect sentences about respiration functions in clinical text in Dutch, use the [icf-domains](https://huggingface.co/CLTL/icf-domains) classification model.
## Functioning levels
Level | Meaning
---|---
4 | No problem with respiration, and/or respiratory rate is normal (EWS: 9-20).
3 | Shortness of breath in exercise (saturation ≥90), and/or respiratory rate is slightly increased (EWS: 21-30).
2 | Shortness of breath in rest (saturation ≥90), and/or respiratory rate is fairly increased (EWS: 31-35).
1 | Needs oxygen at rest or during exercise (saturation <90), and/or respiratory rate >35.
0 | Mechanical ventilation is needed.
The predictions generated by the model might sometimes be outside of the scale (e.g. 4.2); this is normal in a regression model.
## Intended uses and limitations
- The model was fine-tuned (trained, validated and tested) on medical records from the Amsterdam UMC (the two academic medical centers of Amsterdam). It might perform differently on text from a different hospital or text from non-hospital sources (e.g. GP records).
- The model was fine-tuned with the [Simple Transformers](https://simpletransformers.ai/) library. This library is based on Transformers but the model cannot be used directly with Transformers `pipeline` and classes; doing so would generate incorrect outputs. For this reason, the API on this page is disabled.
## How to use
To generate predictions with the model, use the [Simple Transformers](https://simpletransformers.ai/) library:
```
from simpletransformers.classification import ClassificationModel
model = ClassificationModel(
'roberta',
'CLTL/icf-levels-adm',
use_cuda=False,
)
example = 'Nu sinds 5-6 dagen progressieve benauwdheidsklachten (bij korte stukken lopen al kortademig), terwijl dit eerder niet zo was.'
_, raw_outputs = model.predict([example])
predictions = np.squeeze(raw_outputs)
```
The prediction on the example is:
```
2.26
```
The raw outputs look like this:
```
[[2.26074648]]
```
## Training data
- The training data consists of clinical notes from medical records (in Dutch) of the Amsterdam UMC. Due to privacy constraints, the data cannot be released.
- The annotation guidelines used for the project can be found [here](https://github.com/cltl/a-proof-zonmw/tree/main/resources/annotation_guidelines).
## Training procedure
The default training parameters of Simple Transformers were used, including:
- Optimizer: AdamW
- Learning rate: 4e-5
- Num train epochs: 1
- Train batch size: 8
## Evaluation results
The evaluation is done on a sentence-level (the classification unit) and on a note-level (the aggregated unit which is meaningful for the healthcare professionals).
| | Sentence-level | Note-level
|---|---|---
mean absolute error | 0.48 | 0.37
mean squared error | 0.55 | 0.34
root mean squared error | 0.74 | 0.58
## Authors and references
### Authors
Jenia Kim, Piek Vossen
### References
TBD
|
69d20ea7b372bfd1fdaa54688cbc027b
|
sd-concepts-library/sd-concepts-library-uma-meme
|
sd-concepts-library
| null | 39 | 0 | null | 0 | null | false | false | false |
mit
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
[]
| false | true | true | 5,310 | false |
### sd-concepts-library/uma-meme on Stable Diffusion
This is the `<uma-object-full>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as an `object`:
|
aadab92496e08f686bab0f49db23524e
|
dmrau/bow-bert
|
dmrau
|
bert
| 8 | 1 |
transformers
| 0 |
text-classification
| true | false | false |
afl-3.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
[]
| false | true | true | 902 | false |
<strong>Example on how to load and use BOW-BERT: <strong>
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
# load model
model = AutoModelForSequenceClassification.from_pretrained('dmrau/bow-bert')
# load tokenizer
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
# tokenize query and passage and concatenate them
inp = tokenizer(['this is a query','query a is this'], ['this is a passage', 'passage a is this'], return_tensors='pt')
# get estimated score
print('score', model(**inp).logits[:, 1])
### outputs identical scores for different
### word orders as the model is order invariant:
# scores: [-2.9463, -2.9463]
```
<strong> Cite us:<strong>
```
@article{rau2022role,
title={The Role of Complex NLP in Transformers for Text Ranking?},
author={Rau, David and Kamps, Jaap},
journal={arXiv preprint arXiv:2207.02522},
year={2022}
}
```
|
06b18c2d9c9e5eeec0784bf3e613bf1a
|
maisam/roberta-base-finetuned-ner
|
maisam
|
roberta
| 14 | 10 |
transformers
| 0 |
token-classification
| true | false | false |
mit
| null |
['conll2003']
| null | 0 | 0 | 0 | 0 | 1 | 1 | 0 |
['generated_from_trainer']
| true | true | true | 1,522 | false |
<!-- 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. -->
# roberta-base-finetuned-ner
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0497
- Precision: 0.9510
- Recall: 0.9602
- F1: 0.9556
- Accuracy: 0.9892
## 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
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2066 | 1.0 | 878 | 0.0699 | 0.9226 | 0.9294 | 0.9260 | 0.9828 |
| 0.0486 | 2.0 | 1756 | 0.0569 | 0.9465 | 0.9549 | 0.9507 | 0.9878 |
| 0.0254 | 3.0 | 2634 | 0.0497 | 0.9510 | 0.9602 | 0.9556 | 0.9892 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
76482aab9a367e512aa945f0279199b6
|
Aero/Tsubomi-Haruno
|
Aero
|
gpt2
| 9 | 3 |
transformers
| 0 |
conversational
| true | false | false |
mit
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['conversational']
| false | true | true | 1,252 | false |
# DialoGPT Trained on the Speech of a Game Character
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("r3dhummingbird/DialoGPT-medium-joshua")
model = AutoModelWithLMHead.from_pretrained("r3dhummingbird/DialoGPT-medium-joshua")
# Let's chat for 4 lines
for step in range(4):
# encode the new user input, add the eos_token and return a tensor in Pytorch
new_user_input_ids = tokenizer.encode(input(">> User:") + tokenizer.eos_token, return_tensors='pt')
# print(new_user_input_ids)
# append the new user input tokens to the chat history
bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids
# generated a response while limiting the total chat history to 1000 tokens,
chat_history_ids = model.generate(
bot_input_ids, max_length=200,
pad_token_id=tokenizer.eos_token_id,
no_repeat_ngram_size=3,
do_sample=True,
top_k=100,
top_p=0.7,
temperature=0.8
)
# pretty print last ouput tokens from bot
print("Tsubomi: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)))
```
|
0334af403b64731e225aee5f130025f6
|
mccaffary/finetuning-sentiment-model-3000-samples-DM
|
mccaffary
|
distilbert
| 13 | 9 |
transformers
| 0 |
text-classification
| true | false | false |
apache-2.0
| null |
['imdb']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,051 | false |
<!-- 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. -->
# finetuning-sentiment-model-3000-samples-DM
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3248
- Accuracy: 0.8667
- F1: 0.8734
## 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: 2
### Training results
### Framework versions
- Transformers 4.19.2
- Pytorch 1.8.0
- Datasets 2.2.2
- Tokenizers 0.12.1
|
e3d6cd5c89d4721ef30997d5643a2c9e
|
nateraw/yolov6t
|
nateraw
| null | 3 | 0 |
pytorch
| 0 |
object-detection
| true | false | false |
gpl-3.0
|
['en']
| null | null | 2 | 0 | 1 | 1 | 0 | 0 | 0 |
['object-detection', 'yolo', 'autogenerated-modelcard']
| false | true | true | 5,657 | false |
# Model Card for yolov6t
<!-- Provide a quick summary of what the model is/does. -->
# Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3. [Bias, Risks, and Limitations](#bias-risks-and-limitations)
4. [Training Details](#training-details)
5. [Evaluation](#evaluation)
6. [Model Examination](#model-examination)
7. [Environmental Impact](#environmental-impact)
8. [Technical Specifications](#technical-specifications-optional)
9. [Citation](#citation)
10. [Glossary](#glossary-optional)
11. [More Information](#more-information-optional)
12. [Model Card Authors](#model-card-authors-optional)
13. [Model Card Contact](#model-card-contact)
14. [How To Get Started With the Model](#how-to-get-started-with-the-model)
# Model Details
## Model Description
<!-- Provide a longer summary of what this model is. -->
YOLOv6 is a single-stage object detection framework dedicated to industrial applications, with hardware-friendly efficient design and high performance.
- **Developed by:** [More Information Needed]
- **Shared by [Optional]:** [@nateraw](https://hf.co/nateraw)
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Related Models:** [yolov6s](https://hf.co/nateraw/yolov6s), [yolov6n](https://hf.co/nateraw/yolov6n)
- **Parent Model:** N/A
- **Resources for more information:** The [official GitHub Repository](https://github.com/meituan/YOLOv6)
# 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. -->
This model is meant to be used as a general object detector.
## Downstream Use [Optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
You can fine-tune this model for your specific task
## Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
Don't be evil.
# Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
This model often classifies objects incorrectly, especially when applied to videos. It does not handle crowds very well.
## 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 recomendations.
# 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
[More Information Needed]
### Speeds, Sizes, Times
<!-- 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]
# Model Examination
[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
<!-- 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]
Please refer to the [official GitHub Repository](https://github.com/meituan/YOLOv6)
# Model Card Authors [optional]
[@nateraw](https://hf.co/nateraw)
# Model Card Contact
[@nateraw](https://hf.co/nateraw) - please leave a note in the discussions tab here
# How to Get Started with the Model
Use the code below to get started with the model.
<details>
<summary> Click to expand </summary>
[More Information Needed]
</details>
|
33bbe4eeeb0a3135453b5bf841d84a4a
|
Geotrend/distilbert-base-en-cased
|
Geotrend
|
distilbert
| 6 | 5 |
transformers
| 0 |
fill-mask
| true | false | false |
apache-2.0
|
['en']
|
['wikipedia']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
[]
| false | true | true | 1,215 | false |
# distilbert-base-en-cased
We are sharing smaller versions of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) that handle a custom number of languages.
Our versions give exactly the same representations produced by the original model which preserves the original accuracy.
For more information please visit our paper: [Load What You Need: Smaller Versions of Multilingual BERT](https://www.aclweb.org/anthology/2020.sustainlp-1.16.pdf).
## How to use
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("Geotrend/distilbert-base-en-cased")
model = AutoModel.from_pretrained("Geotrend/distilbert-base-en-cased")
```
To generate other smaller versions of multilingual transformers please visit [our Github repo](https://github.com/Geotrend-research/smaller-transformers).
### How to cite
```bibtex
@inproceedings{smallermdistilbert,
title={Load What You Need: Smaller Versions of Mutlilingual BERT},
author={Abdaoui, Amine and Pradel, Camille and Sigel, Grégoire},
booktitle={SustaiNLP / EMNLP},
year={2020}
}
```
## Contact
Please contact amine@geotrend.fr for any question, feedback or request.
|
5a3f52c932bedb9efea72d023504553f
|
taikunzhang/distilbert-base-uncased-finetuned-squad
|
taikunzhang
|
distilbert
| 30 | 3 |
transformers
| 0 |
question-answering
| true | false | false |
apache-2.0
| null |
['squad']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,279 | false |
<!-- 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-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7375
## 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: 3e-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
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.4419 | 1.0 | 557 | 1.7242 |
| 1.2397 | 2.0 | 1114 | 1.6714 |
| 0.9066 | 3.0 | 1671 | 1.7375 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2
|
04989a4836909fbb2c809e343d333fb8
|
aXhyra/demo_sentiment_42
|
aXhyra
|
distilbert
| 10 | 7 |
transformers
| 0 |
text-classification
| true | false | false |
apache-2.0
| null |
['tweet_eval']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,388 | false |
<!-- 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. -->
# demo_sentiment_42
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6332
- F1: 0.7114
## 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: 8.62486660723695e-06
- train_batch_size: 64
- eval_batch_size: 64
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.7592 | 1.0 | 713 | 0.6509 | 0.6834 |
| 0.6389 | 2.0 | 1426 | 0.6318 | 0.7011 |
| 0.5647 | 3.0 | 2139 | 0.6320 | 0.7041 |
| 0.5391 | 4.0 | 2852 | 0.6332 | 0.7114 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.9.1
- Datasets 1.16.1
- Tokenizers 0.10.3
|
d12575f3006136965c84a8224d5ac9b4
|
magitz/distilbert-base-uncased-finetuned-emotion
|
magitz
|
distilbert
| 12 | 1 |
transformers
| 0 |
text-classification
| true | false | false |
apache-2.0
| null |
['emotion']
| null | 1 | 1 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,338 | false |
<!-- 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: 0.2235
- Accuracy: 0.9265
- F1: 0.9268
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8101 | 1.0 | 250 | 0.3177 | 0.9045 | 0.9010 |
| 0.2472 | 2.0 | 500 | 0.2235 | 0.9265 | 0.9268 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.8.1
- Datasets 1.18.3
- Tokenizers 0.11.0
|
b18f4865fc97d133e7ab3de1c11c2ab7
|
din0s/t5-base-asqa-ob
|
din0s
|
t5
| 10 | 1 |
transformers
| 0 |
text2text-generation
| true | false | false |
apache-2.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 2,463 | false |
<!-- 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. -->
# t5-base-asqa-ob
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the [ASQA](https://huggingface.co/datasets/din0s/asqa) dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7356
- Rougelsum: 12.0879
## 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: 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: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:---------:|
| No log | 1.0 | 355 | 1.8545 | 11.6549 |
| 2.4887 | 2.0 | 710 | 1.8050 | 11.7533 |
| 1.9581 | 3.0 | 1065 | 1.7843 | 11.8327 |
| 1.9581 | 4.0 | 1420 | 1.7722 | 11.9442 |
| 1.9252 | 5.0 | 1775 | 1.7648 | 11.9331 |
| 1.8853 | 6.0 | 2130 | 1.7567 | 11.9788 |
| 1.8853 | 7.0 | 2485 | 1.7519 | 12.0300 |
| 1.8512 | 8.0 | 2840 | 1.7483 | 12.0225 |
| 1.8328 | 9.0 | 3195 | 1.7451 | 12.0402 |
| 1.8115 | 10.0 | 3550 | 1.7436 | 12.0444 |
| 1.8115 | 11.0 | 3905 | 1.7419 | 12.0850 |
| 1.7878 | 12.0 | 4260 | 1.7408 | 12.1047 |
| 1.774 | 13.0 | 4615 | 1.7394 | 12.0839 |
| 1.774 | 14.0 | 4970 | 1.7390 | 12.0910 |
| 1.7787 | 15.0 | 5325 | 1.7381 | 12.0880 |
| 1.7632 | 16.0 | 5680 | 1.7380 | 12.1088 |
| 1.7623 | 17.0 | 6035 | 1.7370 | 12.1046 |
| 1.7623 | 18.0 | 6390 | 1.7368 | 12.0997 |
| 1.7508 | 19.0 | 6745 | 1.7359 | 12.0902 |
| 1.7597 | 20.0 | 7100 | 1.7356 | 12.0879 |
### Framework versions
- Transformers 4.23.0.dev0
- Pytorch 1.12.1+cu102
- Datasets 2.4.0
- Tokenizers 0.12.1
|
97ca1b09e88992320df4000b16b8e04d
|
ALM/whisper-sk-small-augmented
|
ALM
|
whisper
| 20 | 0 |
transformers
| 0 |
automatic-speech-recognition
| true | false | false |
apache-2.0
|
['sk']
|
['mozilla-foundation/common_voice_11_0']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['whisper-event', 'generated_from_trainer']
| true | true | true | 1,568 | false |
<!-- 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 Small Slovak - Robust
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the mozilla-foundation/common_voice_11_0 sk dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7397
- Wer: 43.6221
## 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: 64
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0232 | 14.29 | 1000 | 0.7425 | 51.8801 |
| 0.0083 | 28.57 | 2000 | 0.7698 | 48.4888 |
| 0.0006 | 42.86 | 3000 | 0.7640 | 47.5964 |
| 0.0005 | 57.14 | 4000 | 0.7649 | 44.8953 |
| 0.0002 | 71.43 | 5000 | 0.7440 | 44.3598 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.8.0
- Tokenizers 0.13.2
|
51df8cd7a5106411bdc6849c8d8b9eea
|
jonatasgrosman/exp_w2v2t_ru_r-wav2vec2_s869
|
jonatasgrosman
|
wav2vec2
| 10 | 6 |
transformers
| 0 |
automatic-speech-recognition
| true | false | false |
apache-2.0
|
['ru']
|
['mozilla-foundation/common_voice_7_0']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['automatic-speech-recognition', 'ru']
| false | true | true | 462 | false |
# exp_w2v2t_ru_r-wav2vec2_s869
Fine-tuned [facebook/wav2vec2-large-robust](https://huggingface.co/facebook/wav2vec2-large-robust) for speech recognition using the train split of [Common Voice 7.0 (ru)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
94dab4ffb43cfc7ea75a6363ed351052
|
addy88/t5-base-finetuned-sn-to-en
|
addy88
|
t5
| 13 | 6 |
transformers
| 0 |
text2text-generation
| true | false | false |
apache-2.0
| null |
['itihasa']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 949 | false |
<!-- 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. -->
# t5-base-finetuned-sn-to-en
This model is a fine-tuned version of [google/t5-v1_1-base](https://huggingface.co/google/t5-v1_1-base) on the itihasa 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: 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: 1
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
431e00205e520b9a7a82b3feed6deb86
|
ultra-coder54732/4-way-detection-prop-16-deberta
|
ultra-coder54732
|
deberta
| 13 | 2 |
transformers
| 0 |
text-classification
| true | false | false |
mit
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 942 | false |
<!-- 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. -->
# 4-way-detection-prop-16-deberta
This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/deberta-base) on an unknown 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: 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: 10
### Training results
### Framework versions
- Transformers 4.21.1
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
ff94186c250675a76bee81be7046c105
|
russellc/wav2vec2-large-xls-r-300m-tr
|
russellc
|
wav2vec2
| 13 | 5 |
transformers
| 1 |
automatic-speech-recognition
| true | false | false |
apache-2.0
|
['tr-TR']
|
['common_voice, common_voice_6_1_0']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer', 'hf-asr-leaderboard']
| true | true | true | 1,718 | false |
<!-- 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. -->
# wav2vec2-large-xls-r-300m-tr
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2841
- Wer: 0.2904
## 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: 3e-05
- train_batch_size: 7
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 14
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 5.0805 | 4.03 | 1000 | 3.0333 | 1.0 |
| 1.5733 | 8.06 | 2000 | 0.5545 | 0.5080 |
| 0.6238 | 12.1 | 3000 | 0.3861 | 0.3977 |
| 0.4535 | 16.13 | 4000 | 0.3253 | 0.3408 |
| 0.3682 | 20.16 | 5000 | 0.3042 | 0.3177 |
| 0.3302 | 24.19 | 6000 | 0.2950 | 0.3015 |
| 0.2985 | 28.23 | 7000 | 0.2841 | 0.2904 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
b5f0fc803532347cfa60a4c30da91b49
|
kevinbror/xlmrobertaenepochz
|
kevinbror
|
xlm-roberta
| 4 | 5 |
transformers
| 0 |
question-answering
| false | true | false |
mit
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_keras_callback']
| true | true | true | 1,829 | false |
<!-- 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. -->
# xlmrobertaenepochz
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.1485
- Train End Logits Accuracy: 0.6933
- Train Start Logits Accuracy: 0.6537
- Validation Loss: 0.9772
- Validation End Logits Accuracy: 0.7275
- Validation Start Logits Accuracy: 0.6976
- 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', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 5599, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 1.1485 | 0.6933 | 0.6537 | 0.9772 | 0.7275 | 0.6976 | 0 |
### Framework versions
- Transformers 4.20.1
- TensorFlow 2.6.4
- Datasets 2.1.0
- Tokenizers 0.12.1
|
d17a1b44e8ae79201be560f7665f08ad
|
KKHyun/distilbert-base-uncased-finetuned-squad
|
KKHyun
|
distilbert
| 12 | 0 |
transformers
| 0 |
question-answering
| true | false | false |
apache-2.0
| null |
['squad']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,284 | false |
<!-- 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-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1664
## 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
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.2096 | 1.0 | 5533 | 1.1505 |
| 0.952 | 2.0 | 11066 | 1.1238 |
| 0.7347 | 3.0 | 16599 | 1.1664 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
8f6dbf332fcf8cd7bf714282e633ce9c
|
twieland/MIX2_en-ja_helsinki
|
twieland
|
marian
| 11 | 4 |
transformers
| 0 |
text2text-generation
| true | false | false |
apache-2.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 12,236 | false |
<!-- 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. -->
# MIX2_en-ja_helsinki
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-jap](https://huggingface.co/Helsinki-NLP/opus-mt-en-jap) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6703
## 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: 0.0003
- train_batch_size: 96
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:------:|:---------------:|
| 3.5357 | 0.02 | 4000 | 2.9519 |
| 2.8601 | 0.04 | 8000 | 2.6962 |
| 2.6183 | 0.06 | 12000 | 2.5156 |
| 2.4731 | 0.08 | 16000 | 2.4312 |
| 2.3731 | 0.1 | 20000 | 2.3575 |
| 2.2964 | 0.11 | 24000 | 2.3319 |
| 2.238 | 0.13 | 28000 | 2.2802 |
| 2.1919 | 0.15 | 32000 | 2.2552 |
| 2.1479 | 0.17 | 36000 | 2.2354 |
| 2.1104 | 0.19 | 40000 | 2.2210 |
| 2.0788 | 0.21 | 44000 | 2.1835 |
| 2.0552 | 0.23 | 48000 | 2.1391 |
| 2.0228 | 0.25 | 52000 | 2.1338 |
| 2.0062 | 0.27 | 56000 | 2.1115 |
| 1.9868 | 0.29 | 60000 | 2.1025 |
| 1.9628 | 0.31 | 64000 | 2.1334 |
| 1.9474 | 0.32 | 68000 | 2.0935 |
| 1.9318 | 0.34 | 72000 | 2.1030 |
| 1.9187 | 0.36 | 76000 | 2.0605 |
| 1.9019 | 0.38 | 80000 | 2.0388 |
| 1.8916 | 0.4 | 84000 | 2.0360 |
| 1.8775 | 0.42 | 88000 | 2.0356 |
| 1.8689 | 0.44 | 92000 | 2.0315 |
| 1.8558 | 0.46 | 96000 | 2.0169 |
| 1.8431 | 0.48 | 100000 | 2.0213 |
| 1.8373 | 0.5 | 104000 | 2.0071 |
| 1.8224 | 0.52 | 108000 | 2.0093 |
| 1.8181 | 0.53 | 112000 | 1.9952 |
| 1.8087 | 0.55 | 116000 | 1.9927 |
| 1.7998 | 0.57 | 120000 | 1.9726 |
| 1.7947 | 0.59 | 124000 | 1.9817 |
| 1.7874 | 0.61 | 128000 | 1.9650 |
| 1.7781 | 0.63 | 132000 | 1.9688 |
| 1.7712 | 0.65 | 136000 | 1.9655 |
| 1.7631 | 0.67 | 140000 | 1.9561 |
| 1.7577 | 0.69 | 144000 | 1.9529 |
| 1.7528 | 0.71 | 148000 | 1.9447 |
| 1.746 | 0.73 | 152000 | 1.9700 |
| 1.7386 | 0.74 | 156000 | 1.9413 |
| 1.7329 | 0.76 | 160000 | 1.9329 |
| 1.7285 | 0.78 | 164000 | 1.9289 |
| 1.7227 | 0.8 | 168000 | 1.9337 |
| 1.7186 | 0.82 | 172000 | 1.9263 |
| 1.7116 | 0.84 | 176000 | 1.9407 |
| 1.7072 | 0.86 | 180000 | 1.9059 |
| 1.7032 | 0.88 | 184000 | 1.9380 |
| 1.6932 | 0.9 | 188000 | 1.9183 |
| 1.6921 | 0.92 | 192000 | 1.9131 |
| 1.6875 | 0.94 | 196000 | 1.9180 |
| 1.6846 | 0.96 | 200000 | 1.9040 |
| 1.6797 | 0.97 | 204000 | 1.9089 |
| 1.6725 | 0.99 | 208000 | 1.9024 |
| 1.6589 | 1.01 | 212000 | 1.8909 |
| 1.6507 | 1.03 | 216000 | 1.8837 |
| 1.6441 | 1.05 | 220000 | 1.8906 |
| 1.6445 | 1.07 | 224000 | 1.8914 |
| 1.6394 | 1.09 | 228000 | 1.8833 |
| 1.6382 | 1.11 | 232000 | 1.8837 |
| 1.6376 | 1.13 | 236000 | 1.8869 |
| 1.6329 | 1.15 | 240000 | 1.8829 |
| 1.6294 | 1.17 | 244000 | 1.8845 |
| 1.6273 | 1.18 | 248000 | 1.8888 |
| 1.6243 | 1.2 | 252000 | 1.8709 |
| 1.6226 | 1.22 | 256000 | 1.8418 |
| 1.6177 | 1.24 | 260000 | 1.8587 |
| 1.6151 | 1.26 | 264000 | 1.8526 |
| 1.6111 | 1.28 | 268000 | 1.8494 |
| 1.6084 | 1.3 | 272000 | 1.8781 |
| 1.6043 | 1.32 | 276000 | 1.8390 |
| 1.6011 | 1.34 | 280000 | 1.8603 |
| 1.5999 | 1.36 | 284000 | 1.8515 |
| 1.5954 | 1.38 | 288000 | 1.8356 |
| 1.5936 | 1.39 | 292000 | 1.8530 |
| 1.5916 | 1.41 | 296000 | 1.8475 |
| 1.5886 | 1.43 | 300000 | 1.8410 |
| 1.5883 | 1.45 | 304000 | 1.8153 |
| 1.5828 | 1.47 | 308000 | 1.8254 |
| 1.582 | 1.49 | 312000 | 1.8139 |
| 1.578 | 1.51 | 316000 | 1.8366 |
| 1.5723 | 1.53 | 320000 | 1.8353 |
| 1.5705 | 1.55 | 324000 | 1.8230 |
| 1.5691 | 1.57 | 328000 | 1.8194 |
| 1.5656 | 1.59 | 332000 | 1.8069 |
| 1.566 | 1.6 | 336000 | 1.8204 |
| 1.5604 | 1.62 | 340000 | 1.8307 |
| 1.5573 | 1.64 | 344000 | 1.8209 |
| 1.5547 | 1.66 | 348000 | 1.8320 |
| 1.5545 | 1.68 | 352000 | 1.8179 |
| 1.5519 | 1.7 | 356000 | 1.8323 |
| 1.545 | 1.72 | 360000 | 1.8005 |
| 1.5483 | 1.74 | 364000 | 1.8034 |
| 1.5454 | 1.76 | 368000 | 1.7997 |
| 1.5393 | 1.78 | 372000 | 1.8078 |
| 1.5381 | 1.8 | 376000 | 1.8204 |
| 1.5347 | 1.81 | 380000 | 1.8071 |
| 1.5327 | 1.83 | 384000 | 1.7997 |
| 1.529 | 1.85 | 388000 | 1.8012 |
| 1.5287 | 1.87 | 392000 | 1.8028 |
| 1.5273 | 1.89 | 396000 | 1.8103 |
| 1.5194 | 1.91 | 400000 | 1.8008 |
| 1.5197 | 1.93 | 404000 | 1.8004 |
| 1.5218 | 1.95 | 408000 | 1.8024 |
| 1.514 | 1.97 | 412000 | 1.7852 |
| 1.5146 | 1.99 | 416000 | 1.7908 |
| 1.5045 | 2.01 | 420000 | 1.7864 |
| 1.4876 | 2.02 | 424000 | 1.7813 |
| 1.4846 | 2.04 | 428000 | 1.7822 |
| 1.4865 | 2.06 | 432000 | 1.7737 |
| 1.4857 | 2.08 | 436000 | 1.7668 |
| 1.4825 | 2.1 | 440000 | 1.7681 |
| 1.4828 | 2.12 | 444000 | 1.7685 |
| 1.4821 | 2.14 | 448000 | 1.7636 |
| 1.4778 | 2.16 | 452000 | 1.7778 |
| 1.4803 | 2.18 | 456000 | 1.7834 |
| 1.4766 | 2.2 | 460000 | 1.7801 |
| 1.4741 | 2.22 | 464000 | 1.7601 |
| 1.4705 | 2.23 | 468000 | 1.7665 |
| 1.4739 | 2.25 | 472000 | 1.7604 |
| 1.4694 | 2.27 | 476000 | 1.7803 |
| 1.4665 | 2.29 | 480000 | 1.7835 |
| 1.4668 | 2.31 | 484000 | 1.7670 |
| 1.4605 | 2.33 | 488000 | 1.7629 |
| 1.4626 | 2.35 | 492000 | 1.7612 |
| 1.4627 | 2.37 | 496000 | 1.7612 |
| 1.4569 | 2.39 | 500000 | 1.7557 |
| 1.455 | 2.41 | 504000 | 1.7599 |
| 1.4547 | 2.43 | 508000 | 1.7569 |
| 1.453 | 2.44 | 512000 | 1.7589 |
| 1.4515 | 2.46 | 516000 | 1.7679 |
| 1.4501 | 2.48 | 520000 | 1.7574 |
| 1.4446 | 2.5 | 524000 | 1.7526 |
| 1.4456 | 2.52 | 528000 | 1.7506 |
| 1.4445 | 2.54 | 532000 | 1.7484 |
| 1.4428 | 2.56 | 536000 | 1.7447 |
| 1.439 | 2.58 | 540000 | 1.7468 |
| 1.441 | 2.6 | 544000 | 1.7609 |
| 1.4358 | 2.62 | 548000 | 1.7498 |
| 1.4318 | 2.64 | 552000 | 1.7592 |
| 1.4276 | 2.65 | 556000 | 1.7452 |
| 1.4317 | 2.67 | 560000 | 1.7500 |
| 1.4277 | 2.69 | 564000 | 1.7392 |
| 1.4259 | 2.71 | 568000 | 1.7351 |
| 1.4239 | 2.73 | 572000 | 1.7385 |
| 1.4191 | 2.75 | 576000 | 1.7487 |
| 1.4204 | 2.77 | 580000 | 1.7392 |
| 1.4176 | 2.79 | 584000 | 1.7372 |
| 1.4147 | 2.81 | 588000 | 1.7347 |
| 1.4154 | 2.83 | 592000 | 1.7085 |
| 1.4134 | 2.85 | 596000 | 1.7103 |
| 1.4091 | 2.87 | 600000 | 1.7124 |
| 1.4091 | 2.88 | 604000 | 1.7369 |
| 1.406 | 2.9 | 608000 | 1.7142 |
| 1.4028 | 2.92 | 612000 | 1.7376 |
| 1.4019 | 2.94 | 616000 | 1.7201 |
| 1.4018 | 2.96 | 620000 | 1.7230 |
| 1.3959 | 2.98 | 624000 | 1.7206 |
| 1.3985 | 3.0 | 628000 | 1.7183 |
| 1.3681 | 3.02 | 632000 | 1.7283 |
| 1.3668 | 3.04 | 636000 | 1.7330 |
| 1.3687 | 3.06 | 640000 | 1.7187 |
| 1.3681 | 3.08 | 644000 | 1.7163 |
| 1.3687 | 3.09 | 648000 | 1.7249 |
| 1.364 | 3.11 | 652000 | 1.7283 |
| 1.364 | 3.13 | 656000 | 1.7091 |
| 1.3652 | 3.15 | 660000 | 1.7030 |
| 1.3623 | 3.17 | 664000 | 1.7058 |
| 1.3604 | 3.19 | 668000 | 1.7101 |
| 1.3598 | 3.21 | 672000 | 1.7104 |
| 1.3577 | 3.23 | 676000 | 1.7028 |
| 1.3574 | 3.25 | 680000 | 1.7023 |
| 1.3546 | 3.27 | 684000 | 1.7197 |
| 1.3549 | 3.29 | 688000 | 1.7045 |
| 1.3534 | 3.3 | 692000 | 1.6990 |
| 1.3511 | 3.32 | 696000 | 1.6971 |
| 1.3504 | 3.34 | 700000 | 1.6894 |
| 1.346 | 3.36 | 704000 | 1.6820 |
| 1.3467 | 3.38 | 708000 | 1.6920 |
| 1.3461 | 3.4 | 712000 | 1.6897 |
| 1.3425 | 3.42 | 716000 | 1.6962 |
| 1.34 | 3.44 | 720000 | 1.6864 |
| 1.3408 | 3.46 | 724000 | 1.6860 |
| 1.3387 | 3.48 | 728000 | 1.6924 |
| 1.3377 | 3.5 | 732000 | 1.6919 |
| 1.3378 | 3.51 | 736000 | 1.6858 |
| 1.334 | 3.53 | 740000 | 1.6816 |
| 1.3347 | 3.55 | 744000 | 1.6867 |
| 1.3307 | 3.57 | 748000 | 1.6859 |
| 1.3316 | 3.59 | 752000 | 1.6896 |
| 1.3257 | 3.61 | 756000 | 1.6824 |
| 1.3222 | 3.63 | 760000 | 1.6819 |
| 1.3247 | 3.65 | 764000 | 1.6809 |
| 1.3207 | 3.67 | 768000 | 1.6775 |
| 1.3227 | 3.69 | 772000 | 1.6807 |
| 1.3203 | 3.71 | 776000 | 1.6750 |
| 1.3203 | 3.72 | 780000 | 1.6758 |
| 1.316 | 3.74 | 784000 | 1.6787 |
| 1.3147 | 3.76 | 788000 | 1.6747 |
| 1.3146 | 3.78 | 792000 | 1.6718 |
| 1.3137 | 3.8 | 796000 | 1.6744 |
| 1.3143 | 3.82 | 800000 | 1.6733 |
| 1.3123 | 3.84 | 804000 | 1.6754 |
| 1.3069 | 3.86 | 808000 | 1.6734 |
| 1.3122 | 3.88 | 812000 | 1.6742 |
| 1.3074 | 3.9 | 816000 | 1.6742 |
| 1.3006 | 3.92 | 820000 | 1.6709 |
| 1.308 | 3.93 | 824000 | 1.6714 |
| 1.3063 | 3.95 | 828000 | 1.6727 |
| 1.3036 | 3.97 | 832000 | 1.6711 |
| 1.3048 | 3.99 | 836000 | 1.6703 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
f6a44ea9a0ca3fdb4a8388e7425eedaa
|
cyrilzhang/gpt2-numfix
|
cyrilzhang
| null | 6 | 1 |
transformers
| 0 | null | false | false | false |
mit
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
[]
| false | true | true | 819 | false |
## GPT-2 Tokenizer with unmerged digits
A fork of the GPT-2 tokenizer, which **removes multi-digit tokens**:
```python
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained('cyrilzhang/gpt2-numfix')
tokenizer('123.45') # [16, 17, 18, 13, 19, 20]
gpt2_tokenizer('123.45') # [10163, 13, 2231]
```
Backward-compatible:
```python
tokenizer.decode([10163, 46387]) # '<unused123> pigeon'
gpt2_tokenizer.decode([10163, 46387]) # '123 pigeon'
```
- This is for my investigations into the arithmetic capabilities of large language models. There is no model here, only a tokenizer.
- [PaLM](https://arxiv.org/abs/2204.02311) does this. I think it's very reasonable.
- Many models (illustriously, [GPT-3](https://arxiv.org/abs/2005.14165)) don't do this, because they use the GPT-2 tokenizer.
|
8de770c5de49fc72812f37e2d5c28042
|
elopezlopez/xlnet-base-cased_fold_3_binary_v1
|
elopezlopez
|
xlnet
| 12 | 1 |
transformers
| 0 |
text-classification
| true | false | false |
mit
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 2,637 | false |
<!-- 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. -->
# xlnet-base-cased_fold_3_binary_v1
This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8649
- F1: 0.8044
## 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: 25
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 289 | 0.4483 | 0.8000 |
| 0.4228 | 2.0 | 578 | 0.4264 | 0.8040 |
| 0.4228 | 3.0 | 867 | 0.5341 | 0.8056 |
| 0.2409 | 4.0 | 1156 | 0.9077 | 0.8103 |
| 0.2409 | 5.0 | 1445 | 1.1069 | 0.7889 |
| 0.1386 | 6.0 | 1734 | 1.0288 | 0.8093 |
| 0.0817 | 7.0 | 2023 | 1.2477 | 0.8049 |
| 0.0817 | 8.0 | 2312 | 1.5915 | 0.7872 |
| 0.0465 | 9.0 | 2601 | 1.5323 | 0.8035 |
| 0.0465 | 10.0 | 2890 | 1.4351 | 0.7989 |
| 0.0376 | 11.0 | 3179 | 1.4639 | 0.7916 |
| 0.0376 | 12.0 | 3468 | 1.6027 | 0.7956 |
| 0.0234 | 13.0 | 3757 | 1.7860 | 0.7931 |
| 0.0109 | 14.0 | 4046 | 1.8567 | 0.7934 |
| 0.0109 | 15.0 | 4335 | 1.8294 | 0.8053 |
| 0.0115 | 16.0 | 4624 | 1.7799 | 0.7971 |
| 0.0115 | 17.0 | 4913 | 1.5935 | 0.8000 |
| 0.0142 | 18.0 | 5202 | 1.8136 | 0.8066 |
| 0.0142 | 19.0 | 5491 | 1.7718 | 0.8063 |
| 0.0124 | 20.0 | 5780 | 1.8581 | 0.8053 |
| 0.0083 | 21.0 | 6069 | 1.8523 | 0.8056 |
| 0.0083 | 22.0 | 6358 | 1.8408 | 0.8035 |
| 0.0045 | 23.0 | 6647 | 1.8347 | 0.8040 |
| 0.0045 | 24.0 | 6936 | 1.8683 | 0.8067 |
| 0.0005 | 25.0 | 7225 | 1.8649 | 0.8044 |
### Framework versions
- Transformers 4.21.1
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
2037dd0c53298570c9ae05c4fd019c80
|
Fhrozen/voc_hifigan_multilingual
|
Fhrozen
| null | 4 | 0 |
espnet
| 1 |
audio-to-audio
| false | false | false |
cc-by-4.0
|
['multilingual']
|
['libritts', 'csj', 'css10', 'aishell3', 'jvs', 'jsss', 'jsut']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['espnet', 'audio', 'audio-to-audio', 'vocoder']
| false | true | true | 579 | false |
## Vocoder model - HifiGAN - Multilingual
**No support given.**
### Details
```
batch_size: 64
discriminator_params:
follow_official_norm: true
period_discriminator_params:
bias: true
channels: 32
downsample_scales:
- 3
- 3
- 3
- 3
- 1
in_channels: 1
kernel_sizes:
- 5
- 3
max_downsample_channels: 1024
nonlinear_activation: LeakyReLU
nonlinear_activation_params:
negative_slope: 0.1
out_channels: 1
use_spectral_norm: false
use_weight_norm: true
periods:
- 2
- 3
- 5
- 7
- 11
```
|
e7ba85ddf3286b9938f8e43dd291de73
|
Helsinki-NLP/opus-mt-nso-es
|
Helsinki-NLP
|
marian
| 10 | 8 |
transformers
| 0 |
translation
| true | true | false |
apache-2.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['translation']
| false | true | true | 776 | false |
### opus-mt-nso-es
* source languages: nso
* target languages: es
* OPUS readme: [nso-es](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/nso-es/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/nso-es/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/nso-es/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/nso-es/opus-2020-01-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.nso.es | 29.5 | 0.485 |
|
0e2dc387a63805aa88cf4d35dee617e2
|
autoevaluate/binary-classification-not-evaluated
|
autoevaluate
|
distilbert
| 15 | 5 |
transformers
| 0 |
text-classification
| true | false | false |
apache-2.0
| null |
['glue']
| null | 1 | 1 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| false | true | true | 1,211 | false |
<!-- 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. -->
# binary-classification
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3009
- Accuracy: 0.8968
## 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: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.175 | 1.0 | 4210 | 0.3009 | 0.8968 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
967eb53754bea5d064d12a9c15fafd33
|
DTAI-KULeuven/robbert-2022-dutch-base
|
DTAI-KULeuven
|
roberta
| 9 | 1,078 |
transformers
| 5 |
fill-mask
| true | false | false |
mit
|
['nl']
|
['oscar', 'dbrd', 'lassy-ud', 'europarl-mono', 'conll2002']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['Dutch', 'Flemish', 'RoBERTa', 'RobBERT']
| false | true | true | 9,632 | false |
<p align="center">
<img src="https://github.com/iPieter/RobBERT/raw/master/res/robbert_2022_logo_with_name.png" alt="RobBERT-2022: Updating a Dutch Language Model to Account for Evolving Language Use" width="75%">
</p>
# RobBERT-2022: Updating a Dutch Language Model to Account for Evolving Language Use.
RobBERT-2022 is the latest release of the [Dutch RobBERT model](https://pieter.ai/robbert/).
It further pretrained the original [pdelobelle/robbert-v2-dutch-base](https://huggingface.co/pdelobelle/robbert-v2-dutch-base) model on the 2022 version of the OSCAR version.
Thanks to this more recent dataset, this [DTAI-KULeuven/robbert-2022-dutch-base](https://huggingface.co/DTAI-KULeuven/robbert-2022-dutch-base) model shows increased performance on several tasks related to recent events, e.g. COVID-19-related tasks.
We also found that for some tasks that do not contain more recent information than 2019, the original [pdelobelle/robbert-v2-dutch-base](https://huggingface.co/pdelobelle/robbert-v2-dutch-base) RobBERT model can still outperform this newer one.
The original RobBERT model was released in January 2020. Dutch has evolved a lot since then, for example the COVID-19 pandemic introduced a wide range of new words that were suddenly used daily. Also, many other world facts that the original model considered true have also changed. To account for this and other changes in usage, we release a new Dutch BERT model trained on data from 2022: RobBERT 2022.
More in-depth information about RobBERT-2022 can be found in our [blog post](https://pieter.ai/robbert-2022/), [our paper](http://arxiv.org/abs/2211.08192), [the original RobBERT paper](https://arxiv.org/abs/2001.06286) and [the RobBERT Github repository](https://github.com/iPieter/RobBERT).
## How to use
RobBERT-2022 and RobBERT both use the [RoBERTa](https://arxiv.org/abs/1907.11692) architecture and pre-training but with a Dutch tokenizer and training data. RoBERTa is the robustly optimized English BERT model, making it even more powerful than the original BERT model. Given this same architecture, RobBERT can easily be finetuned and inferenced using [code to finetune RoBERTa](https://huggingface.co/transformers/model_doc/roberta.html) models and most code used for BERT models, e.g. as provided by [HuggingFace Transformers](https://huggingface.co/transformers/) library.
By default, RobBERT-2022 has the masked language model head used in training. This can be used as a zero-shot way to fill masks in sentences. It can be tested out for free on [RobBERT's Hosted infererence API of Huggingface](https://huggingface.co/pdelobelle/robbert-v2-dutch-base?text=De+hoofdstad+van+Belgi%C3%AB+is+%3Cmask%3E.). You can also create a new prediction head for your own task by using any of HuggingFace's [RoBERTa-runners](https://huggingface.co/transformers/v2.7.0/examples.html#language-model-training), [their fine-tuning notebooks](https://huggingface.co/transformers/v4.1.1/notebooks.html) by changing the model name to `DTAI-KULeuven/robbert-2022-dutch-base`.
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("DTAI-KULeuven/robbert-2022-dutch-base")
model = AutoModelForSequenceClassification.from_pretrained("DTAI-KULeuven/robbert-2022-dutch-base")
```
You can then use most of [HuggingFace's BERT-based notebooks](https://huggingface.co/transformers/v4.1.1/notebooks.html) for finetuning RobBERT-2022 on your type of Dutch language dataset.
## Comparison of Available Dutch BERT models
There is a wide variety of Dutch BERT-based models available for fine-tuning on your tasks.
Here's a quick summary to find the one that suits your need:
- [pdelobelle/robbert-v2-dutch-base](https://huggingface.co/pdelobelle/robbert-v2-dutch-base): The RobBERT model has for years been the best performing BERT-like model for most language tasks. It is trained on a large Dutch webcrawled dataset (OSCAR) and uses the superior [RoBERTa](https://huggingface.co/docs/transformers/model_doc/roberta) architecture, which robustly optimized the original [BERT model](https://huggingface.co/docs/transformers/model_doc/bert).
- [DTAI-KULeuven/robbertje-1-gb-merged](https://huggingface.co/DTAI-KULeuven/robbertje-1-gb-mergedRobBERTje): The RobBERTje model is a distilled version of RobBERT and about half the size and four times faster to perform inference on. This can help deploy more scalable language models for your language task
- [DTAI-KULeuven/robbert-2022-dutch-base](https://huggingface.co/DTAI-KULeuven/robbert-2022-dutch-base): The RobBERT-2022 is a further pre-trained RobBERT model on the OSCAR2022 dataset. It is helpful for tasks that rely on words and/or information about more recent events.
There's also the [GroNLP/bert-base-dutch-cased](https://huggingface.co/GroNLP/bert-base-dutch-cased) "BERTje" model. This model uses the outdated basic BERT model, and is trained on a smaller corpus of clean Dutch texts.
Thanks to RobBERT's more recent architecture as well as its larger and more real-world-like training corpus, most researchers and practitioners seem to achieve higher performance on their language tasks with the RobBERT model.
## Technical Details From The Paper
### Our Performance Evaluation Results
All experiments are described in more detail in our [paper](https://arxiv.org/abs/2001.06286), with the code in [our GitHub repository](https://github.com/iPieter/RobBERT).
### Sentiment analysis
Predicting whether a review is positive or negative using the [Dutch Book Reviews Dataset](https://github.com/benjaminvdb/110kDBRD).
| Model | Accuracy [%] |
|-------------------|--------------------------|
| ULMFiT | 93.8 |
| BERTje | 93.0 |
| RobBERT v2 | 94.4 |
| RobBERT 2022 | **95.1** |
### Die/Dat (coreference resolution)
We measured how well the models are able to do coreference resolution by predicting whether "die" or "dat" should be filled into a sentence.
For this, we used the [EuroParl corpus](https://www.statmt.org/europarl/).
#### Finetuning on whole dataset
| Model | Accuracy [%] | F1 [%] |
|-------------------|--------------------------|--------------|
| [Baseline](https://arxiv.org/abs/2001.02943) (LSTM) | | 75.03 |
| mBERT | 98.285 | 98.033 |
| BERTje | 98.268 | 98.014 |
| RobBERT v2 | **99.232** | **99.121** |
| RobBERT 2022 | 97.8 | |
#### Finetuning on 10K examples
We also measured the performance using only 10K training examples.
This experiment clearly illustrates that RobBERT outperforms other models when there is little data available.
| Model | Accuracy [%] | F1 [%] |
|-------------------|--------------------------|--------------|
| mBERT | 92.157 | 90.898 |
| BERTje | 93.096 | 91.279 |
| RobBERT v2 | **97.816** | **97.514** |
#### Using zero-shot word masking task
Since BERT models are pre-trained using the word masking task, we can use this to predict whether "die" or "dat" is more likely.
This experiment shows that RobBERT has internalised more information about Dutch than other models.
| Model | Accuracy [%] |
|-------------------|--------------------------|
| ZeroR | 66.70 |
| mBERT | 90.21 |
| BERTje | 94.94 |
| RobBERT v2 | **98.75** |
### Part-of-Speech Tagging.
Using the [Lassy UD dataset](https://universaldependencies.org/treebanks/nl_lassysmall/index.html).
| Model | Accuracy [%] |
|-------------------|--------------------------|
| Frog | 91.7 |
| mBERT | **96.5** |
| BERTje | 96.3 |
| RobBERT v2 | 96.4 |
| RobBERT 2022 | 96.1 |
## Credits and citation
This project is created by [Pieter Delobelle](https://people.cs.kuleuven.be/~pieter.delobelle), [Thomas Winters](https://thomaswinters.be) and [Bettina Berendt](https://people.cs.kuleuven.be/~bettina.berendt/).
If you would like to cite our paper or model, you can use the following BibTeX:
```
@inproceedings{delobelle2022robbert2022,
doi = {10.48550/ARXIV.2211.08192},
url = {https://arxiv.org/abs/2211.08192},
author = {Delobelle, Pieter and Winters, Thomas and Berendt, Bettina},
keywords = {Computation and Language (cs.CL), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {RobBERT-2022: Updating a Dutch Language Model to Account for Evolving Language Use},
venue = {arXiv},
year = {2022},
}
@inproceedings{delobelle2020robbert,
title = "{R}ob{BERT}: a {D}utch {R}o{BERT}a-based {L}anguage {M}odel",
author = "Delobelle, Pieter and
Winters, Thomas and
Berendt, Bettina",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.findings-emnlp.292",
doi = "10.18653/v1/2020.findings-emnlp.292",
pages = "3255--3265"
}
```
|
9f1f6ae4ee99c934084b193f297a9f71
|
birdaz/nagisa
|
birdaz
| null | 19 | 6 |
diffusers
| 1 |
text-to-image
| false | false | false |
creativeml-openrail-m
| null | null | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 |
['text-to-image', 'stable-diffusion']
| false | true | true | 414 | false |
### nagisa Dreambooth model trained by birdaz 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:
|
6093e73d930d4725252048cf1bcc8ac0
|
KoichiYasuoka/deberta-large-japanese-aozora-ud-head
|
KoichiYasuoka
|
deberta-v2
| 20 | 8 |
transformers
| 1 |
question-answering
| true | false | false |
cc-by-sa-4.0
|
['ja']
|
['universal_dependencies']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['japanese', 'question-answering', 'dependency-parsing']
| false | true | true | 3,868 | false |
# deberta-large-japanese-aozora-ud-head
## Model Description
This is a DeBERTa(V2) model pretrained on 青空文庫 for dependency-parsing (head-detection on long-unit-words) as question-answering, derived from [deberta-large-japanese-aozora](https://huggingface.co/KoichiYasuoka/deberta-large-japanese-aozora) and [UD_Japanese-GSDLUW](https://github.com/UniversalDependencies/UD_Japanese-GSDLUW). Use [MASK] inside `context` to avoid ambiguity when specifying a multiple-used word as `question`.
## How to Use
```py
from transformers import AutoTokenizer,AutoModelForQuestionAnswering,QuestionAnsweringPipeline
tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/deberta-large-japanese-aozora-ud-head")
model=AutoModelForQuestionAnswering.from_pretrained("KoichiYasuoka/deberta-large-japanese-aozora-ud-head")
qap=QuestionAnsweringPipeline(tokenizer=tokenizer,model=model,align_to_words=False)
print(qap(question="国語",context="全学年にわたって小学校の国語の教科書に挿し絵>が用いられている"))
```
or (with [ufal.chu-liu-edmonds](https://pypi.org/project/ufal.chu-liu-edmonds/))
```py
class TransformersUD(object):
def __init__(self,bert):
import os
from transformers import (AutoTokenizer,AutoModelForQuestionAnswering,
AutoModelForTokenClassification,AutoConfig,TokenClassificationPipeline)
self.tokenizer=AutoTokenizer.from_pretrained(bert)
self.model=AutoModelForQuestionAnswering.from_pretrained(bert)
x=AutoModelForTokenClassification.from_pretrained
if os.path.isdir(bert):
d,t=x(os.path.join(bert,"deprel")),x(os.path.join(bert,"tagger"))
else:
from transformers.utils import cached_file
c=AutoConfig.from_pretrained(cached_file(bert,"deprel/config.json"))
d=x(cached_file(bert,"deprel/pytorch_model.bin"),config=c)
s=AutoConfig.from_pretrained(cached_file(bert,"tagger/config.json"))
t=x(cached_file(bert,"tagger/pytorch_model.bin"),config=s)
self.deprel=TokenClassificationPipeline(model=d,tokenizer=self.tokenizer,
aggregation_strategy="simple")
self.tagger=TokenClassificationPipeline(model=t,tokenizer=self.tokenizer)
def __call__(self,text):
import numpy,torch,ufal.chu_liu_edmonds
w=[(t["start"],t["end"],t["entity_group"]) for t in self.deprel(text)]
z,n={t["start"]:t["entity"].split("|") for t in self.tagger(text)},len(w)
r,m=[text[s:e] for s,e,p in w],numpy.full((n+1,n+1),numpy.nan)
v,c=self.tokenizer(r,add_special_tokens=False)["input_ids"],[]
for i,t in enumerate(v):
q=[self.tokenizer.cls_token_id]+t+[self.tokenizer.sep_token_id]
c.append([q]+v[0:i]+[[self.tokenizer.mask_token_id]]+v[i+1:]+[[q[-1]]])
b=[[len(sum(x[0:j+1],[])) for j in range(len(x))] for x in c]
with torch.no_grad():
d=self.model(input_ids=torch.tensor([sum(x,[]) for x in c]),
token_type_ids=torch.tensor([[0]*x[0]+[1]*(x[-1]-x[0]) for x in b]))
s,e=d.start_logits.tolist(),d.end_logits.tolist()
for i in range(n):
for j in range(n):
m[i+1,0 if i==j else j+1]=s[i][b[i][j]]+e[i][b[i][j+1]-1]
h=ufal.chu_liu_edmonds.chu_liu_edmonds(m)[0]
if [0 for i in h if i==0]!=[0]:
i=([p for s,e,p in w]+["root"]).index("root")
j=i+1 if i<n else numpy.nanargmax(m[:,0])
m[0:j,0]=m[j+1:,0]=numpy.nan
h=ufal.chu_liu_edmonds.chu_liu_edmonds(m)[0]
u="# text = "+text.replace("\n"," ")+"\n"
for i,(s,e,p) in enumerate(w,1):
p="root" if h[i]==0 else "dep" if p=="root" else p
u+="\t".join([str(i),r[i-1],"_",z[s][0][2:],"_","|".join(z[s][1:]),
str(h[i]),p,"_","_" if i<n and e<w[i][0] else "SpaceAfter=No"])+"\n"
return u+"\n"
nlp=TransformersUD("KoichiYasuoka/deberta-large-japanese-aozora-ud-head")
print(nlp("全学年にわたって小学校の国語の教科書に挿し絵が用いられている"))
```
## Reference
安岡孝一: [青空文庫DeBERTaモデルによる国語研長単位係り受け解析](http://hdl.handle.net/2433/275409), 東洋学へのコンピュータ利用, 第35回研究セミナー (2022年7月), pp.29-43.
|
82aa721ab5b3fee4a6525aa519b9a8fc
|
Helsinki-NLP/opus-mt-fi-yap
|
Helsinki-NLP
|
marian
| 10 | 8 |
transformers
| 0 |
translation
| true | true | false |
apache-2.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['translation']
| false | true | true | 776 | false |
### opus-mt-fi-yap
* source languages: fi
* target languages: yap
* OPUS readme: [fi-yap](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fi-yap/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-08.zip](https://object.pouta.csc.fi/OPUS-MT-models/fi-yap/opus-2020-01-08.zip)
* test set translations: [opus-2020-01-08.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/fi-yap/opus-2020-01-08.test.txt)
* test set scores: [opus-2020-01-08.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/fi-yap/opus-2020-01-08.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.fi.yap | 25.4 | 0.445 |
|
d2255ce359fb5b9db77020e11a790b88
|
Graphcore/roberta-base-squad2
|
Graphcore
|
roberta
| 19 | 6 |
transformers
| 0 |
question-answering
| true | false | false |
apache-2.0
| null |
['squad_v2']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 4,188 | false |
# Graphcore/roberta-base-squad2
Optimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Graphcore’s IPUs - a completely new kind of massively parallel processor to accelerate machine intelligence. Learn more about how to take train Transformer models faster with IPUs at [hf.co/hardware/graphcore](https://huggingface.co/hardware/graphcore).
Through HuggingFace Optimum, Graphcore released ready-to-use IPU-trained model checkpoints and IPU configuration files to make it easy to train models with maximum efficiency in the IPU. Optimum shortens the development lifecycle of your AI models by letting you plug-and-play any public dataset and allows a seamless integration to our State-of-the-art hardware giving you a quicker time-to-value for your AI project.
## Model description
RoBERTa is based on BERT pretraining approach and improves on it by carefully evaluating a number of design decisions of BERT pretraining which it found to cause the model to be undertrained.
It suggested a way to improve the performance by training the model longer, with bigger batches over more data, removing the next sentence prediction objectives, training on longer sequences and dynamically changing the mask pattern applied to the training data.
As a result, it achieved state-of-the-art results on GLUE, RACE and SQuAD.
Paper link : [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://arxiv.org/pdf/1907.11692.pdf)
## Intended uses & limitations
This model is a fine-tuned version of [HuggingFace/roberta-base](https://huggingface.co/roberta-base) on the squad_v2 dataset.
## Training and evaluation data
Trained and evaluated on the SQuAD v2 dataset:
- [HuggingFace/squad_v2](https://huggingface.co/datasets/squad_v2).
## Training procedure
Trained on 16 Graphcore Mk2 IPUs using [optimum-graphcore](https://github.com/huggingface/optimum-graphcore).
Command line:
```
python examples/question-answering/run_qa.py \
--ipu_config_name Graphcore/roberta-base-ipu \
--model_name_or_path roberta-base \
--dataset_name squad_v2 \
--version_2_with_negative \
--do_train \
--do_eval \
--num_train_epochs 3 \
--per_device_train_batch_size 4 \
--per_device_eval_batch_size 2 \
--pod_type pod16 \
--learning_rate 7e-5 \
--max_seq_length 384 \
--doc_stride 128 \
--seed 1984 \
--lr_scheduler_type linear \
--loss_scaling 64 \
--weight_decay 0.01 \
--warmup_ratio 0.2 \
--logging_steps 1 \
--save_steps -1 \
--dataloader_num_workers 64 \
--output_dir roberta-base-squad2 \
--overwrite_output_dir \
--push_to_hub
```
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7e-05
- train_batch_size: 4
- eval_batch_size: 2
- seed: 1984
- distributed_type: IPU
- total_train_batch_size: 256
- total_eval_batch_size: 40
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 3.0
- training precision: Mixed Precision
### Training results
```
***** train metrics *****
epoch = 3.0
train_loss = 0.9982
train_runtime = 0:04:44.21
train_samples = 131823
train_samples_per_second = 1391.43
train_steps_per_second = 5.425
***** eval metrics *****
epoch = 3.0
eval_HasAns_exact = 78.1208
eval_HasAns_f1 = 84.6569
eval_HasAns_total = 5928
eval_NoAns_exact = 82.0353
eval_NoAns_f1 = 82.0353
eval_NoAns_total = 5945
eval_best_exact = 80.0809
eval_best_exact_thresh = 0.0
eval_best_f1 = 83.3442
eval_best_f1_thresh = 0.0
eval_exact = 80.0809
eval_f1 = 83.3442
eval_samples = 12165
eval_total = 11873
```
### Framework versions
- Transformers 4.18.0.dev0
- Pytorch 1.10.0+cpu
- Datasets 2.0.0
- Tokenizers 0.11.6
|
e83459dad80df2ecd74583964b5caf00
|
danurahul/wav2vec2-large-xlsr-pa-IN
|
danurahul
|
wav2vec2
| 9 | 6 |
transformers
| 1 |
automatic-speech-recognition
| true | false | true |
apache-2.0
|
['pa-IN']
|
['common_voice']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week']
| true | true | true | 3,679 | false |
# Wav2Vec2-Large-XLSR-53-Punjabi
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Punjabi using the [Common Voice](https://huggingface.co/datasets/common_voice).
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
test_dataset = load_dataset("common_voice", "pa-IN", split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("danurahul/wav2vec2-large-xlsr-pa-IN")
model = Wav2Vec2ForCTC.from_pretrained("danurahul/wav2vec2-large-xlsr-pa-IN")
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
predicted_ids = torch.argmax(logits, dim=-1)
print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][:2])
```
## Evaluation
The model can be evaluated as follows on the Punjabi test data of Common Voice.
```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
test_dataset = load_dataset("common_voice", "pa-IN", split="test")
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("danurahul/wav2vec2-large-xlsr-pa-IN")
model = Wav2Vec2ForCTC.from_pretrained("danurahul/wav2vec2-large-xlsr-pa-IN")
model.to("cuda")
chars_to_ignore_regex = '[\\\\\\\\\\\\\\\\,\\\\\\\\\\\\\\\\?\\\\\\\\\\\\\\\\.\\\\\\\\\\\\\\\\!\\\\\\\\\\\\\\\\-\\\\\\\\\\\\\\\\;\\\\\\\\\\\\\\\\:\\\\\\\\\\\\\\\\"\\\\\\\\\\\\\\\\“\\\\\\\\\\\\\\\\%\\\\\\\\\\\\\\\\‘\\\\\\\\\\\\\\\\”\\\\\\\\\\\\\\\\�]'
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def evaluate(batch):
inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
pred_ids = torch.argmax(logits, dim=-1)
batch["pred_strings"] = processor.batch_decode(pred_ids)
return batch
result = test_dataset.map(evaluate, batched=True, batch_size=8)
print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
```
**Test Result**: 100 %
## Training
The Common Voice `train`, `validation` was used for training as well as validation and testing #
The script used for training can be found https://github.com/rahul-art/huggingface_wav2vec2_punjabi/blob/main/Fine_Tune_XLSR_Wav2Vec2_on_Punjabi_ASR_with_%F0%9F%A4%97_Transformers.ipynb
|
4025ef0833027b7839229134b01c8404
|
Someman/bird-danphe
|
Someman
| null | 17 | 7 |
diffusers
| 0 |
text-to-image
| true | false | false |
creativeml-openrail-m
| null | null | null | 1 | 0 | 1 | 0 | 0 | 0 | 0 |
['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'wildcard']
| false | true | true | 714 | false |
# DreamBooth model for the bird concept trained by Someman on the Someman/danphe dataset.
This is a Stable Diffusion model fine-tuned on the bird concept with DreamBooth. It can be used by modifying the `instance_prompt`: **a photo of bird danphe**
This model was created as part of the DreamBooth Hackathon 🔥. Visit the [organisation page](https://huggingface.co/dreambooth-hackathon) for instructions on how to take part!
## Description
This is a Stable Diffusion model fine-tuned on `danphe` images for the wildcard theme.
## Usage
```python
from diffusers import StableDiffusionPipeline
pipeline = StableDiffusionPipeline.from_pretrained('Someman/bird-danphe')
image = pipeline().images[0]
image
```
|
cd1c242d1c3b341e9795e1055267187e
|
google/tapas-small
|
google
|
tapas
| 8 | 5 |
transformers
| 0 |
feature-extraction
| true | true | false |
apache-2.0
|
['en']
| null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['tapas', 'TapasModel']
| false | true | true | 4,610 | false |
# TAPAS small model
This model has 2 versions which can be used. The latest version, which is the default one, corresponds to the `tapas_inter_masklm_small_reset` checkpoint of the [original Github repository](https://github.com/google-research/tapas).
This model was pre-trained on MLM and an additional step which the authors call intermediate pre-training. It uses relative position embeddings by default (i.e. resetting the position index at every cell of the table).
The other (non-default) version which can be used is the one with absolute position embeddings:
- `revision="no_reset"`, which corresponds to `tapas_inter_masklm_small`
Disclaimer: The team releasing TAPAS did not write a model card for this model so this model card has been written by
the Hugging Face team and contributors.
## Model description
TAPAS is a BERT-like transformers model pretrained on a large corpus of English data from Wikipedia in a self-supervised fashion.
This means it was pretrained on the raw tables and associated texts only, with no humans labelling them in any way (which is why it
can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it
was pretrained with two objectives:
- Masked language modeling (MLM): taking a (flattened) table and associated context, the model randomly masks 15% of the words in
the input, then runs the entire (partially masked) sequence through the model. The model then has to predict the masked words.
This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other,
or from autoregressive models like GPT which internally mask the future tokens. It allows the model to learn a bidirectional
representation of a table and associated text.
- Intermediate pre-training: to encourage numerical reasoning on tables, the authors additionally pre-trained the model by creating
a balanced dataset of millions of syntactically created training examples. Here, the model must predict (classify) whether a sentence
is supported or refuted by the contents of a table. The training examples are created based on synthetic as well as counterfactual statements.
This way, the model learns an inner representation of the English language used in tables and associated texts, which can then be used
to extract features useful for downstream tasks such as answering questions about a table, or determining whether a sentence is entailed
or refuted by the contents of a table. Fine-tuning is done by adding one or more classification heads on top of the pre-trained model, and then
jointly train these randomly initialized classification heads with the base model on a downstream task.
## Intended uses & limitations
You can use the raw model for getting hidden representatons about table-question pairs, but it's mostly intended to be fine-tuned on a downstream task such as question answering or sequence classification. See the [model hub](https://huggingface.co/models?filter=tapas) to look for fine-tuned versions on a task that interests you.
## Training procedure
### Preprocessing
The texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,000. The inputs of the model are
then of the form:
```
[CLS] Sentence [SEP] Flattened table [SEP]
```
### Pre-training
The model was pre-trained on 32 Cloud TPU v3 cores for 1,000,000 steps with maximum sequence length 512 and batch size of 512.
In this setup, pre-training on MLM only takes around 3 days. Aditionally, the model has been further pre-trained on a second task (table entailment). See the original TAPAS [paper](https://www.aclweb.org/anthology/2020.acl-main.398/) and the [follow-up paper](https://www.aclweb.org/anthology/2020.findings-emnlp.27/) for more details.
The optimizer used is Adam with a learning rate of 5e-5, and a warmup
ratio of 0.01.
### BibTeX entry and citation info
```bibtex
@misc{herzig2020tapas,
title={TAPAS: Weakly Supervised Table Parsing via Pre-training},
author={Jonathan Herzig and Paweł Krzysztof Nowak and Thomas Müller and Francesco Piccinno and Julian Martin Eisenschlos},
year={2020},
eprint={2004.02349},
archivePrefix={arXiv},
primaryClass={cs.IR}
}
```
```bibtex
@misc{eisenschlos2020understanding,
title={Understanding tables with intermediate pre-training},
author={Julian Martin Eisenschlos and Syrine Krichene and Thomas Müller},
year={2020},
eprint={2010.00571},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
e2ceac0d75c51fd46581960140607381
|
HuggingFaceM4/opt-1.3b-bf16-8b-samples
|
HuggingFaceM4
|
opt
| 11 | 2 |
transformers
| 0 |
text-generation
| true | false | false |
openrail
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
[]
| false | true | true | 2,345 | false |
This model is an outcome of an experiment of training from scratch
https://huggingface.co/facebook/opt-1.3b for just 8B tokens in fp16, fp32 and bf16 which would allow
comparing the resulting models when they are used to train a multimodal model. But, of course, it can
be used for any other purpose, just be aware that these models are very undertrained. Most language
models are trained for about 300B tokens, this one was just 8B.
The 3 repositories are:
- https://huggingface.co/HuggingFaceM4/opt-1.3b-fp16-8b-samples
- https://huggingface.co/HuggingFaceM4/opt-1.3b-fp32-8b-samples
- https://huggingface.co/HuggingFaceM4/opt-1.3b-bf16-8b-samples
## The training
get transformers:
```
git clone https://github.com/huggingface/transformers
cd transformers
```
Prepare an initialized opt-1.3 model:
```
cat << EOT > prep-bf16.py
from transformers import AutoConfig, AutoModel, AutoTokenizer
import torch
mname = "facebook/opt-1.3b"
config = AutoConfig.from_pretrained(mname)
model = AutoModel.from_config(config, torch_dtype=torch.float16)
tokenizer = AutoTokenizer.from_pretrained(mname)
path = "opt-1.3b-bf16"
model.save_pretrained(path)
tokenizer.save_pretrained(path)
EOT
```
Run:
```
python prep-bf16.py
```
Train from scratch on a single 8x 80GB A100 node on `realnewslike` subset of https://huggingface.co/datasets/c4:
```
git clone https://github.com/huggingface/transformers
cd transformers
PYTHONPATH="src" python -m torch.distributed.run \
--nproc_per_node=8 \
--nnode=1 \
--node_rank=0 \
--master_addr=127.0.0.1 \
--master_port=9901 \
examples/pytorch/language-modeling/run_clm.py \
--bf16 \
--tf32 1 \
--seed 42 \
--dataset_name c4 \
--dataset_config_name realnewslike \
--model_name_or_path opt-1.3b-bf16 \
--per_device_train_batch_size 6 \
--per_device_eval_batch_size 6 \
--gradient_accumulation_steps 2 \
--do_train \
--logging_steps 5 \
--save_steps 1000 \
--eval_steps 1000 \
--weight_decay 0.1 \
--num_train_epochs 1 \
--adam_beta1 0.9 \
--adam_beta2 0.95 \
--learning_rate 0.0002 \
--lr_scheduler_type linear \
--warmup_steps 1000 \
--report_to tensorboard \
--output_dir saved \
--logging_dir tb \
--log_level warning \
--preprocessing_num_workers 32
```
The training took about 40h.
|
99a703c01f69211c035ca8affdce11e7
|
zoha/wav2vec2-base-common-voice-fa-demo-colab
|
zoha
|
wav2vec2
| 22 | 5 |
transformers
| 0 |
automatic-speech-recognition
| true | false | false |
apache-2.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,734 | false |
<!-- 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. -->
# wav2vec2-base-common-voice-fa-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0558
- Wer: 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
- lr_scheduler_warmup_steps: 1000
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---:|
| 5.1626 | 0.3 | 100 | 4.0692 | 1.0 |
| 5.1776 | 0.6 | 200 | 3.6640 | 1.0 |
| 3.6628 | 0.9 | 300 | 3.3832 | 1.0 |
| 3.2022 | 1.2 | 400 | 3.3492 | 1.0 |
| 3.1714 | 1.5 | 500 | 3.3215 | 1.0 |
| 3.0689 | 1.8 | 600 | 3.0806 | 1.0 |
| 3.1478 | 2.1 | 700 | 3.0624 | 1.0 |
| 3.1818 | 2.4 | 800 | 3.0777 | 1.0 |
| 3.159 | 2.7 | 900 | 3.0558 | 1.0 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
06c99f75854eb94f29b9d9f6aecd3dd7
|
jonatasgrosman/exp_w2v2r_de_vp-100k_age_teens-10_sixties-0_s362
|
jonatasgrosman
|
wav2vec2
| 10 | 0 |
transformers
| 0 |
automatic-speech-recognition
| true | false | false |
apache-2.0
|
['de']
|
['mozilla-foundation/common_voice_7_0']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['automatic-speech-recognition', 'de']
| false | true | true | 498 | false |
# exp_w2v2r_de_vp-100k_age_teens-10_sixties-0_s362
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (de)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
073905c38449aaf9d9b08e86345e64c7
|
Helsinki-NLP/opus-mt-war-en
|
Helsinki-NLP
|
marian
| 11 | 80 |
transformers
| 0 |
translation
| true | true | false |
apache-2.0
|
['war', 'en']
| null | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 |
['translation']
| false | true | true | 2,007 | false |
### war-eng
* source group: Waray (Philippines)
* target group: English
* OPUS readme: [war-eng](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/war-eng/README.md)
* model: transformer-align
* source language(s): war
* target language(s): eng
* model: transformer-align
* pre-processing: normalization + SentencePiece (spm4k,spm4k)
* download original weights: [opus-2020-06-16.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/war-eng/opus-2020-06-16.zip)
* test set translations: [opus-2020-06-16.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/war-eng/opus-2020-06-16.test.txt)
* test set scores: [opus-2020-06-16.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/war-eng/opus-2020-06-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.war.eng | 12.3 | 0.308 |
### System Info:
- hf_name: war-eng
- source_languages: war
- target_languages: eng
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/war-eng/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['war', 'en']
- src_constituents: {'war'}
- tgt_constituents: {'eng'}
- src_multilingual: False
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm4k,spm4k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/war-eng/opus-2020-06-16.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/war-eng/opus-2020-06-16.test.txt
- src_alpha3: war
- tgt_alpha3: eng
- short_pair: war-en
- chrF2_score: 0.308
- bleu: 12.3
- brevity_penalty: 1.0
- ref_len: 11345.0
- src_name: Waray (Philippines)
- tgt_name: English
- train_date: 2020-06-16
- src_alpha2: war
- tgt_alpha2: en
- prefer_old: False
- long_pair: war-eng
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41
|
10e4b8324e537ad376b0069b4a22ffed
|
anas-awadalla/t5-base-few-shot-k-128-finetuned-squad-seed-2
|
anas-awadalla
|
t5
| 17 | 1 |
transformers
| 0 |
text2text-generation
| true | false | false |
apache-2.0
| null |
['squad']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 961 | false |
<!-- 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. -->
# t5-base-few-shot-k-128-finetuned-squad-seed-2
This model is a fine-tuned version of [google/t5-v1_1-base](https://huggingface.co/google/t5-v1_1-base) on the squad 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: 8
- eval_batch_size: 8
- seed: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- training_steps: 1000
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.11.6
|
ff9ae7041d43f150a7aa123c81c77166
|
proxima/darkvictorian_artstyle
|
proxima
| null | 21 | 66 |
diffusers
| 41 |
text-to-image
| false | false | false |
creativeml-openrail-m
|
['en']
| null | null | 4 | 0 | 3 | 1 | 1 | 0 | 1 |
['stable-diffusion', 'text-to-image']
| false | true | true | 1,675 | false |
# finetuned on dark, moody, "victorian" imagery (ノ◕ヮ◕)ノ*:・゚✧
[<img src="https://colab.research.google.com/assets/colab-badge.svg">](https://colab.research.google.com/drive/13E3i6_Z1BWd3e6f71-TNd5bk8eGqaeZf?usp=sharing)
v1 was trained on SD 1.4, v2 on SD 1.5. check the pdf for examples with different prompts & settings. comparisons.zip has steps vs cfg scale x/y plots for euler_a and lms.
use the tokens "darkvictorian artstyle" in your prompt to use the style.
## random samples:
<a href='https://ko-fi.com/S6S6FUYKY' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://storage.ko-fi.com/cdn/kofi3.png?v=3' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a>
## License
This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.
The CreativeML OpenRAIL License specifies:
1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content
2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license
3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)
[Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license)
|
e3c8e6758ecfb0eca863ac396d5bd2d6
|
AndyChiang/cdgp-csg-roberta-cloth
|
AndyChiang
|
roberta
| 9 | 6 |
transformers
| 0 |
fill-mask
| true | false | false |
mit
|
['en']
|
['cloth']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['roberta', 'cloze', 'distractor', 'generation']
| false | true | true | 3,678 | false |
# cdgp-csg-roberta-cloth
## Model description
This model is a Candidate Set Generator in **"CDGP: Automatic Cloze Distractor Generation based on Pre-trained Language Model", Findings of EMNLP 2022**.
Its input are stem and answer, and output is candidate set of distractors. It is fine-tuned by [**CLOTH**](https://www.cs.cmu.edu/~glai1/data/cloth/) dataset based on [**roberta-base**](https://huggingface.co/roberta-base) model.
For more details, you can see our **paper** or [**GitHub**](https://github.com/AndyChiangSH/CDGP).
## How to use?
1. Download the model by hugging face transformers.
```python
from transformers import RobertaTokenizer, RobertaForMaskedLM, pipeline
tokenizer = RobertaTokenizer.from_pretrained("AndyChiang/cdgp-csg-roberta-cloth")
csg_model = RobertaForMaskedLM.from_pretrained("AndyChiang/cdgp-csg-roberta-cloth")
```
2. Create a unmasker.
```python
unmasker = pipeline("fill-mask", tokenizer=tokenizer, model=csg_model, top_k=10)
```
3. Use the unmasker to generate the candidate set of distractors.
```python
sent = "I feel <mask> now. </s> happy"
cs = unmasker(sent)
print(cs)
```
## Dataset
This model is fine-tuned by [CLOTH](https://www.cs.cmu.edu/~glai1/data/cloth/) dataset, which is a collection of nearly 100,000 cloze questions from middle school and high school English exams. The detail of CLOTH dataset is shown below.
| Number of questions | Train | Valid | Test |
| ------------------- | ----- | ----- | ----- |
| Middle school | 22056 | 3273 | 3198 |
| High school | 54794 | 7794 | 8318 |
| Total | 76850 | 11067 | 11516 |
You can also use the [dataset](https://huggingface.co/datasets/AndyChiang/cloth) we have already cleaned.
## Training
We use a special way to fine-tune model, which is called **"Answer-Relating Fine-Tune"**. More detail is in our paper.
### Training hyperparameters
The following hyperparameters were used during training:
- Pre-train language model: [roberta-base](https://huggingface.co/roberta-base)
- Optimizer: adam
- Learning rate: 0.0001
- Max length of input: 64
- Batch size: 64
- Epoch: 1
- Device: NVIDIA® Tesla T4 in Google Colab
## Testing
The evaluations of this model as a Candidate Set Generator in CDGP is as follows:
| P@1 | F1@3 | F1@10 | MRR | NDCG@10 |
| ----- | ---- | ----- | ----- | ------- |
| 10.50 | 9.83 | 10.25 | 20.42 | 28.17 |
## Other models
### Candidate Set Generator
| Models | CLOTH | DGen |
| ----------- | ----------------------------------------------------------------------------------- | -------------------------------------------------------------------------------- |
| **BERT** | [cdgp-csg-bert-cloth](https://huggingface.co/AndyChiang/cdgp-csg-bert-cloth) | [cdgp-csg-bert-dgen](https://huggingface.co/AndyChiang/cdgp-csg-bert-dgen) |
| **SciBERT** | [cdgp-csg-scibert-cloth](https://huggingface.co/AndyChiang/cdgp-csg-scibert-cloth) | [cdgp-csg-scibert-dgen](https://huggingface.co/AndyChiang/cdgp-csg-scibert-dgen) |
| **RoBERTa** | [*cdgp-csg-roberta-cloth*](https://huggingface.co/AndyChiang/cdgp-csg-roberta-cloth) | [cdgp-csg-roberta-dgen](https://huggingface.co/AndyChiang/cdgp-csg-roberta-dgen) |
| **BART** | [cdgp-csg-bart-cloth](https://huggingface.co/AndyChiang/cdgp-csg-bart-cloth) | [cdgp-csg-bart-dgen](https://huggingface.co/AndyChiang/cdgp-csg-bart-dgen) |
### Distractor Selector
**fastText**: [cdgp-ds-fasttext](https://huggingface.co/AndyChiang/cdgp-ds-fasttext)
## Citation
None
|
494053be485f7d48fbf4099a625d8bcd
|
hfl/chinese-legal-electra-small-discriminator
|
hfl
|
electra
| 9 | 1 |
transformers
| 1 | null | true | true | false |
apache-2.0
|
['zh']
| null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
[]
| false | true | true | 1,881 | false |
# This model is specifically designed for legal domain.
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.
For further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.
ELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.
This project is based on the official code of ELECTRA: [https://github.com/google-research/electra](https://github.com/google-research/electra)
You may also interested in,
- Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm
- Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA
- Chinese XLNet: https://github.com/ymcui/Chinese-XLNet
- Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer
More resources by HFL: https://github.com/ymcui/HFL-Anthology
## Citation
If you find our resource or paper is useful, please consider including the following citation in your paper.
- https://arxiv.org/abs/2004.13922
```
@inproceedings{cui-etal-2020-revisiting,
title = "Revisiting Pre-Trained Models for {C}hinese Natural Language Processing",
author = "Cui, Yiming and
Che, Wanxiang and
Liu, Ting and
Qin, Bing and
Wang, Shijin and
Hu, Guoping",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.findings-emnlp.58",
pages = "657--668",
}
```
|
7cce1888f9b096a1f4732e5972bc4bcf
|
tucan9389/distilbert-base-uncased-finetuned-cola
|
tucan9389
|
distilbert
| 13 | 2 |
transformers
| 0 |
text-classification
| true | false | false |
apache-2.0
| null |
['glue']
| null | 1 | 1 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,571 | false |
<!-- 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-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7501
- Matthews Correlation: 0.5309
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5286 | 1.0 | 535 | 0.5067 | 0.4301 |
| 0.3469 | 2.0 | 1070 | 0.5216 | 0.4802 |
| 0.2343 | 3.0 | 1605 | 0.6431 | 0.5002 |
| 0.1753 | 4.0 | 2140 | 0.7501 | 0.5309 |
| 0.1251 | 5.0 | 2675 | 0.8695 | 0.5222 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3
|
c03b272121b9552029cc938369cf83cc
|
KFlash/bert-finetuned-squad
|
KFlash
|
bert
| 14 | 3 |
transformers
| 0 |
question-answering
| true | false | false |
apache-2.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 938 | false |
<!-- 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-finetuned-squad
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown 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: 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
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Tokenizers 0.12.1
|
add72244449d6be7c32078cf935804cb
|
fathyshalab/all-roberta-large-v1-work-7-16-5
|
fathyshalab
|
roberta
| 11 | 3 |
transformers
| 0 |
text-classification
| true | false | false |
apache-2.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,509 | false |
<!-- 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. -->
# all-roberta-large-v1-work-7-16-5
This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3586
- Accuracy: 0.3689
## 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: 48
- eval_batch_size: 48
- 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 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.8058 | 1.0 | 1 | 2.6169 | 0.2356 |
| 2.3524 | 2.0 | 2 | 2.5215 | 0.2978 |
| 1.9543 | 3.0 | 3 | 2.4427 | 0.3422 |
| 1.5539 | 4.0 | 4 | 2.3874 | 0.36 |
| 1.4133 | 5.0 | 5 | 2.3586 | 0.3689 |
### Framework versions
- Transformers 4.20.0
- Pytorch 1.11.0+cu102
- Datasets 2.3.2
- Tokenizers 0.12.1
|
8bf80d3b983d99fec433799a3f00d6c4
|
Helsinki-NLP/opus-mt-fr-tll
|
Helsinki-NLP
|
marian
| 10 | 7 |
transformers
| 0 |
translation
| true | true | false |
apache-2.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['translation']
| false | true | true | 776 | false |
### opus-mt-fr-tll
* source languages: fr
* target languages: tll
* OPUS readme: [fr-tll](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fr-tll/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/fr-tll/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/fr-tll/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/fr-tll/opus-2020-01-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.fr.tll | 24.6 | 0.467 |
|
50efa12cf7a12f6b0ff157dbe42b24f8
|
DOOGLAK/Article_100v5_NER_Model_3Epochs_UNAUGMENTED
|
DOOGLAK
|
bert
| 13 | 5 |
transformers
| 0 |
token-classification
| true | false | false |
apache-2.0
| null |
['article100v5_wikigold_split']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,561 | false |
<!-- 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. -->
# Article_100v5_NER_Model_3Epochs_UNAUGMENTED
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the article100v5_wikigold_split dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5958
- Precision: 0.0241
- Recall: 0.0005
- F1: 0.0010
- Accuracy: 0.7822
## 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
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 13 | 0.7298 | 0.0 | 0.0 | 0.0 | 0.7816 |
| No log | 2.0 | 26 | 0.6272 | 0.0 | 0.0 | 0.0 | 0.7816 |
| No log | 3.0 | 39 | 0.5958 | 0.0241 | 0.0005 | 0.0010 | 0.7822 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.4.0
- Tokenizers 0.11.6
|
451603474a8739b2d0c1d4b3c88b040f
|
KoichiYasuoka/roberta-large-japanese-aozora-ud-goeswith
|
KoichiYasuoka
|
roberta
| 10 | 7 |
transformers
| 0 |
token-classification
| true | false | false |
cc-by-sa-4.0
|
['ja']
|
['universal_dependencies']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['japanese', 'pos', 'dependency-parsing']
| false | true | true | 2,862 | false |
# roberta-large-japanese-aozora-ud-goeswith
## Model Description
This is a RoBERTa model pretrained on 青空文庫 texts for POS-tagging and dependency-parsing (using `goeswith` for subwords), derived from [roberta-large-japanese-aozora](https://huggingface.co/KoichiYasuoka/roberta-large-japanese-aozora) and [UD_Japanese-GSDLUW](https://github.com/UniversalDependencies/UD_Japanese-GSDLUW).
## How to Use
```py
class UDgoeswith(object):
def __init__(self,bert):
from transformers import AutoTokenizer,AutoModelForTokenClassification
self.tokenizer=AutoTokenizer.from_pretrained(bert)
self.model=AutoModelForTokenClassification.from_pretrained(bert)
def __call__(self,text):
import numpy,torch,ufal.chu_liu_edmonds
w=self.tokenizer(text,return_offsets_mapping=True)
v=w["input_ids"]
x=[v[0:i]+[self.tokenizer.mask_token_id]+v[i+1:]+[j] for i,j in enumerate(v[1:-1],1)]
with torch.no_grad():
e=self.model(input_ids=torch.tensor(x)).logits.numpy()[:,1:-2,:]
r=[1 if i==0 else -1 if j.endswith("|root") else 0 for i,j in sorted(self.model.config.id2label.items())]
e+=numpy.where(numpy.add.outer(numpy.identity(e.shape[0]),r)==0,0,numpy.nan)
g=self.model.config.label2id["X|_|goeswith"]
r=numpy.tri(e.shape[0])
for i in range(e.shape[0]):
for j in range(i+2,e.shape[1]):
r[i,j]=r[i,j-1] if numpy.nanargmax(e[i,j-1])==g else 1
e[:,:,g]+=numpy.where(r==0,0,numpy.nan)
m=numpy.full((e.shape[0]+1,e.shape[1]+1),numpy.nan)
m[1:,1:]=numpy.nanmax(e,axis=2).transpose()
p=numpy.zeros(m.shape)
p[1:,1:]=numpy.nanargmax(e,axis=2).transpose()
for i in range(1,m.shape[0]):
m[i,0],m[i,i],p[i,0]=m[i,i],numpy.nan,p[i,i]
h=ufal.chu_liu_edmonds.chu_liu_edmonds(m)[0]
if [0 for i in h if i==0]!=[0]:
m[:,0]+=numpy.where(m[:,0]==numpy.nanmax(m[[i for i,j in enumerate(h) if j==0],0]),0,numpy.nan)
m[[i for i,j in enumerate(h) if j==0]]+=[0 if i==0 or j==0 else numpy.nan for i,j in enumerate(h)]
h=ufal.chu_liu_edmonds.chu_liu_edmonds(m)[0]
u="# text = "+text+"\n"
v=[(s,e) for s,e in w["offset_mapping"] if s<e]
for i,(s,e) in enumerate(v,1):
q=self.model.config.id2label[p[i,h[i]]].split("|")
u+="\t".join([str(i),text[s:e],"_",q[0],"_","|".join(q[1:-1]),str(h[i]),q[-1],"_","_" if i<len(v) and e<v[i][0] else "SpaceAfter=No"])+"\n"
return u+"\n"
nlp=UDgoeswith("KoichiYasuoka/roberta-large-japanese-aozora-ud-goeswith")
print(nlp("全学年にわたって小学校の国語の教科書に挿し絵が用いられている"))
```
with [ufal.chu-liu-edmonds](https://pypi.org/project/ufal.chu-liu-edmonds/).
Or without ufal.chu-liu-edmonds:
```
from transformers import pipeline
nlp=pipeline("universal-dependencies","KoichiYasuoka/roberta-large-japanese-aozora-ud-goeswith",trust_remote_code=True,aggregation_strategy="simple")
print(nlp("全学年にわたって小学校の国語の教科書に挿し絵が用いられている"))
```
|
df864b5d031440191e7ffe28da6ef1d7
|
asaduas/deberta-v3-xsmall-indonesia-squadv2
|
asaduas
|
deberta-v2
| 13 | 13 |
transformers
| 0 |
question-answering
| true | false | false |
mit
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 2,649 | false |
<!-- 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. -->
# deberta-v3-xsmall-indonesia-squadv2
This model is a fine-tuned version of [microsoft/deberta-v3-xsmall](https://huggingface.co/microsoft/deberta-v3-xsmall) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4182
## Model description
Deberta-V3-Xsmall from Microsft with model parameter:
Backbone Parameters 22M,
384 Hidden Size,
12 Layers
Based Deberta-V3
## Intended uses & limitations
More information needed
## Training and evaluation data
Training and evaluation data using Indonesia SQuAD V2, source from https://github.com/Wikidepia/SQuAD-id
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 4.5e-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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.6078 | 1.0 | 13505 | 1.5331 |
| 1.4216 | 2.0 | 27010 | 1.4344 |
| 1.2017 | 3.0 | 40515 | 1.4182 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
### Evaluation Results
```
{'exact': 55.34646711872568,
'f1': 67.22757187614371,
'total': 24923,
'HasAns_exact': 55.34646711872568,
'HasAns_f1': 67.22757187614371,
'HasAns_total': 24923,
'best_exact': 55.34646711872568,
'best_exact_thresh': 0.0,
'best_f1': 67.22757187614371,
'best_f1_thresh': 0.0}
```
### Simple Usage
```
from transformers import pipeline
qa_pipeline = pipeline(
"question-answering",
model="asaduas/deberta-v3-xsmall-indonesia-squadv2",
tokenizer="asaduas/deberta-v3-xsmall-indonesia-squadv2"
)
qa_pipeline(
{
'context': "Pada tahun 1512 juga Afonso de Albuquerque mengirim Antonio Albreu dan Franscisco Serrao untuk memimpin armadanya mencari jalan ke tempat asal rempah-rempah di Maluku. Sepanjang perjalanan, mereka singgah di Madura, Bali, dan Lombok. Dengan menggunakan nakhoda-nakhoda Jawa, armada itu tiba di Kepulauan Banda, terus menuju Aibku Utara sampai tiba di Ternate.",
'question': "Siapa yang dikirim oleh Afonso de Albuquerque Pada tahun 1512?"
}
)
```
### Output
```
[
{'score': 0.8919295072555542,
'start': 51,
'end': 88,
'answer': ' Antonio Albreu dan Franscisco Serrao'}
]
```
|
a9142f8d338c80e5f756aed1da1e6446
|
spoiled/t5_large_epoch_1_comve_triple
|
spoiled
|
t5
| 20 | 2 |
transformers
| 0 |
text2text-generation
| true | false | false |
apache-2.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,190 | false |
<!-- 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. -->
# t5_large_epoch_1_comve_triple
This model is a fine-tuned version of [t5-large](https://huggingface.co/t5-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.5605
## 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: 48
- eval_batch_size: 96
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 4 | 4.1923 |
| No log | 2.0 | 8 | 3.5605 |
### Framework versions
- Transformers 4.25.0.dev0
- Pytorch 1.10.1
- Datasets 2.6.1
- Tokenizers 0.13.1
|
496f6ed325642ea3ca22b939bd532049
|
osanseviero/my-helsinki-duplicate
|
osanseviero
|
marian
| 10 | 5 |
transformers
| 0 |
translation
| true | false | false |
apache-2.0
|
['zh', 'en']
| null | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 |
['translation']
| false | true | true | 2,496 | false |
### zho-eng
* source group: Chinese
* target group: English
* OPUS readme: [zho-eng](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/zho-eng/README.md)
* model: transformer
* source language(s): cjy_Hans cjy_Hant cmn cmn_Hans cmn_Hant gan lzh lzh_Hans nan wuu yue yue_Hans yue_Hant
* target language(s): eng
* model: transformer
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download original weights: [opus-2020-07-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/zho-eng/opus-2020-07-17.zip)
* test set translations: [opus-2020-07-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zho-eng/opus-2020-07-17.test.txt)
* test set scores: [opus-2020-07-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zho-eng/opus-2020-07-17.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.zho.eng | 36.1 | 0.548 |
### System Info:
- hf_name: zho-eng
- source_languages: zho
- target_languages: eng
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/zho-eng/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['zh', 'en']
- src_constituents: {'cmn_Hans', 'nan', 'nan_Hani', 'gan', 'yue', 'cmn_Kana', 'yue_Hani', 'wuu_Bopo', 'cmn_Latn', 'yue_Hira', 'cmn_Hani', 'cjy_Hans', 'cmn', 'lzh_Hang', 'lzh_Hira', 'cmn_Hant', 'lzh_Bopo', 'zho', 'zho_Hans', 'zho_Hant', 'lzh_Hani', 'yue_Hang', 'wuu', 'yue_Kana', 'wuu_Latn', 'yue_Bopo', 'cjy_Hant', 'yue_Hans', 'lzh', 'cmn_Hira', 'lzh_Yiii', 'lzh_Hans', 'cmn_Bopo', 'cmn_Hang', 'hak_Hani', 'cmn_Yiii', 'yue_Hant', 'lzh_Kana', 'wuu_Hani'}
- tgt_constituents: {'eng'}
- src_multilingual: False
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm32k,spm32k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/zho-eng/opus-2020-07-17.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/zho-eng/opus-2020-07-17.test.txt
- src_alpha3: zho
- tgt_alpha3: eng
- short_pair: zh-en
- chrF2_score: 0.5479999999999999
- bleu: 36.1
- brevity_penalty: 0.948
- ref_len: 82826.0
- src_name: Chinese
- tgt_name: English
- train_date: 2020-07-17
- src_alpha2: zh
- tgt_alpha2: en
- prefer_old: False
- long_pair: zho-eng
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41
|
7a02a2d02a67679f629f46b15783e4f1
|
yugkha3/avatar
|
yugkha3
| null | 18 | 78 |
diffusers
| 0 |
text-to-image
| false | false | false |
creativeml-openrail-m
| null | null | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 |
['text-to-image', 'stable-diffusion']
| false | true | true | 434 | false |
### Avatar Dreambooth model trained by yugkha3 with [buildspace's DreamBooth](https://colab.research.google.com/github/buildspace/diffusers/blob/main/examples/dreambooth/DreamBooth_Stable_Diffusion.ipynb) notebook
Build your own using the [AI Avatar project](https://buildspace.so/builds/ai-avatar)!
To get started head over to the [project dashboard](https://buildspace.so/p/build-ai-avatars).
Sample pictures of this concept:
|
c7d3a0d6348d145c020b112e1f81bb77
|
sd-dreambooth-library/abstract-patterns-in-nature
|
sd-dreambooth-library
| null | 18 | 5 |
diffusers
| 4 | null | false | false | false |
mit
| null | null | null | 3 | 0 | 3 | 0 | 0 | 0 | 0 |
[]
| false | true | true | 1,438 | false |
### abstract_patterns_in_nature on Stable Diffusion via Dreambooth trained on the [fast-DreamBooth.ipynb by TheLastBen](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
#### model by apurik-parv
This your the Stable Diffusion model fine-tuned the abstract_patterns_in_nature concept taught to Stable Diffusion with Dreambooth.
It can be used by modifying the `instance_prompt(s)`: **abnapa**
#### This is an attempt to teach symmetry and its scales to stable diffusion model. This first version was trained on abstract patterns from nature and it markedly produces different images from the original model sometime better sometimes no so better or even worse at times it even seems to correct the lighting and shadows. Users please give your comments after usage so that we can really understand what this model does.
You can also train your own concepts and upload them to the library by using [the fast-DremaBooth.ipynb by TheLastBen](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb).
And you can run your new concept via `diffusers`: [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb), [Spaces with the Public Concepts loaded](https://huggingface.co/spaces/sd-dreambooth-library/stable-diffusion-dreambooth-concepts)
|
76bdea258d2324a07450623a7b78d016
|
Helsinki-NLP/opus-mt-aed-es
|
Helsinki-NLP
|
marian
| 10 | 7 |
transformers
| 0 |
translation
| true | true | false |
apache-2.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['translation']
| false | true | true | 776 | false |
### opus-mt-aed-es
* source languages: aed
* target languages: es
* OPUS readme: [aed-es](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/aed-es/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-15.zip](https://object.pouta.csc.fi/OPUS-MT-models/aed-es/opus-2020-01-15.zip)
* test set translations: [opus-2020-01-15.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/aed-es/opus-2020-01-15.test.txt)
* test set scores: [opus-2020-01-15.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/aed-es/opus-2020-01-15.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.aed.es | 89.1 | 0.915 |
|
528f327c15909705209c31368c3ab9f2
|
zhas/distilbert-base-uncased-finetuned-tweet_eval-just
|
zhas
|
distilbert
| 12 | 2 |
transformers
| 0 |
text-classification
| true | false | false |
apache-2.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 922 | false |
<!-- 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-tweet_eval-just
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown 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: 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
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1+cu116
- Tokenizers 0.13.2
|
e4b9dbecc3a136ce16fdabb140d3f881
|
HuyenNguyen/Vin8-P3
|
HuyenNguyen
|
whisper
| 15 | 5 |
transformers
| 0 |
automatic-speech-recognition
| true | false | false |
apache-2.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,385 | false |
<!-- 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. -->
# Vin8-P3
This model is a fine-tuned version of [HuyenNguyen/Vin7-P3](https://huggingface.co/HuyenNguyen/Vin7-P3) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2177
- Wer: 11.8695
## 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 600
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.2704 | 0.51 | 200 | 0.2188 | 11.3771 |
| 0.225 | 1.03 | 400 | 0.2184 | 11.5308 |
| 0.1854 | 1.54 | 600 | 0.2177 | 11.8695 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
ac787fe3e8dd30acde5757fa73ed6802
|
arjunpatel/distilgpt2-finetuned-wikitext2
|
arjunpatel
|
gpt2
| 12 | 2 |
transformers
| 0 |
text-generation
| false | true | false |
apache-2.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_keras_callback']
| true | true | true | 1,179 | false |
<!-- 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. -->
# arjunpatel/distilgpt2-finetuned-wikitext2
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 3.7979
- Validation Loss: 3.6723
- 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': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 3.7979 | 3.6723 | 0 |
### Framework versions
- Transformers 4.18.0
- TensorFlow 2.8.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
eb126a4af959dc5b8cba52cb34e3780d
|
tfshaman/distilbert-base-uncased-distilled-clinc
|
tfshaman
|
distilbert
| 10 | 3 |
transformers
| 0 |
text-classification
| true | false | false |
apache-2.0
| null |
['clinc_oos']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,356 | false |
<!-- 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-distilled-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5565
- Accuracy: 0.8265
## 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: 48
- eval_batch_size: 48
- 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 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 3.2743 | 1.0 | 318 | 2.5809 | 0.7310 |
| 2.2148 | 2.0 | 636 | 1.7909 | 0.8071 |
| 1.7065 | 3.0 | 954 | 1.5565 | 0.8265 |
### Framework versions
- Transformers 4.21.0.dev0
- Pytorch 1.12.0
- Datasets 2.3.2
- Tokenizers 0.12.1
|
ae68e201f071dacd6bb86085d4d8aef5
|
jonatasgrosman/exp_w2v2t_ar_unispeech_s574
|
jonatasgrosman
|
unispeech
| 10 | 5 |
transformers
| 0 |
automatic-speech-recognition
| true | false | false |
apache-2.0
|
['ar']
|
['mozilla-foundation/common_voice_7_0']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['automatic-speech-recognition', 'ar']
| false | true | true | 469 | false |
# exp_w2v2t_ar_unispeech_s574
Fine-tuned [microsoft/unispeech-large-1500h-cv](https://huggingface.co/microsoft/unispeech-large-1500h-cv) for speech recognition using the train split of [Common Voice 7.0 (ar)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
7c9e547ab00bdb7de6a661b9dd3b6688
|
rinna/japanese-gpt2-small
|
rinna
|
gpt2
| 9 | 6,017 |
transformers
| 10 |
text-generation
| true | true | false |
mit
|
['ja']
|
['cc100', 'wikipedia']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['ja', 'japanese', 'gpt2', 'text-generation', 'lm', 'nlp']
| false | true | true | 1,364 | false |
# japanese-gpt2-small
This repository provides a small-sized Japanese GPT-2 model. The model was trained using code from Github repository [rinnakk/japanese-pretrained-models](https://github.com/rinnakk/japanese-pretrained-models) by [rinna Co., Ltd.](https://corp.rinna.co.jp/)
# How to use the model
*NOTE:* Use `T5Tokenizer` to initiate the tokenizer.
~~~~
from transformers import T5Tokenizer, GPT2LMHeadModel
tokenizer = T5Tokenizer.from_pretrained("rinna/japanese-gpt2-small")
tokenizer.do_lower_case = True # due to some bug of tokenizer config loading
model = GPT2LMHeadModel.from_pretrained("rinna/japanese-gpt2-small")
~~~~
# Model architecture
A 12-layer, 768-hidden-size transformer-based language model.
# Training
The model was trained on [Japanese CC-100](http://data.statmt.org/cc-100/ja.txt.xz) and [Japanese Wikipedia](https://dumps.wikimedia.org/other/cirrussearch) to optimize a traditional language modelling objective on 8\\*V100 GPUs for around 15 days. It reaches around 21 perplexity on a chosen validation set from CC-100.
# Tokenization
The model uses a [sentencepiece](https://github.com/google/sentencepiece)-based tokenizer, the vocabulary was trained on the Japanese Wikipedia using the official sentencepiece training script.
# Licenese
[The MIT license](https://opensource.org/licenses/MIT)
|
4be89a5c09dc9e0b9f9b6721e3239ef7
|
jonatasgrosman/exp_w2v2r_fr_xls-r_gender_male-5_female-5_s916
|
jonatasgrosman
|
wav2vec2
| 10 | 3 |
transformers
| 0 |
automatic-speech-recognition
| true | false | false |
apache-2.0
|
['fr']
|
['mozilla-foundation/common_voice_7_0']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['automatic-speech-recognition', 'fr']
| false | true | true | 476 | false |
# exp_w2v2r_fr_xls-r_gender_male-5_female-5_s916
Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
fe975da5d92e95e64c505442be8c9884
|
MultiBertGunjanPatrick/multiberts-seed-19
|
MultiBertGunjanPatrick
|
bert
| 7 | 2 |
transformers
| 0 | null | true | false | false |
apache-2.0
|
['en']
|
['bookcorpus', 'wikipedia']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['exbert', 'multiberts']
| false | true | true | 6,323 | false |
# MultiBERTs Seed 19 (uncased)
Seed 19 MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in
[this repository](https://github.com/google-research/language/tree/master/language/multiberts). This model is uncased: it does not make a difference
between english and English.
Disclaimer: The team releasing MultiBERTs did not write a model card for this model so this model card has been written by [gchhablani](https://hf.co/gchhablani).
## Model description
MultiBERTs models are transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it
was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of
publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it
was pretrained with two objectives:
- Masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then run
the entire masked sentence through the model and has to predict the masked words. This is different from traditional
recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like
GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the
sentence.
- Next sentence prediction (NSP): the models concatenates two masked sentences as inputs during pretraining. Sometimes
they correspond to sentences that were next to each other in the original text, sometimes not. The model then has to
predict if the two sentences were following each other or not.
This way, the model learns an inner representation of the English language that can then be used to extract features
useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard
classifier using the features produced by the MultiBERTs model as inputs.
## Intended uses & limitations
You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to
be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=multiberts) to look for
fine-tuned versions on a task that interests you.
Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)
to make decisions, such as sequence classification, token classification or question answering. For tasks such as text
generation you should look at model like GPT2.
### How to use
Here is how to use this model to get the features of a given text in PyTorch:
```python
from transformers import BertTokenizer, BertModel
tokenizer = BertTokenizer.from_pretrained('multiberts-seed-19')
model = BertModel.from_pretrained("multiberts-seed-19")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
### Limitations and bias
Even if the training data used for this model could be characterized as fairly neutral, this model can have biased
predictions. This bias will also affect all fine-tuned versions of this model. For an understanding of bias of this particular
checkpoint, please try out this checkpoint with the snippet present in the [Limitation and bias section](https://huggingface.co/bert-base-uncased#limitations-and-bias) of the [bert-base-uncased](https://huggingface.co/bert-base-uncased) checkpoint.
## Training data
The MultiBERTs models were pretrained on [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038
unpublished books and [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and
headers).
## Training procedure
### Preprocessing
The texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,000. The inputs of the model are
then of the form:
```
[CLS] Sentence A [SEP] Sentence B [SEP]
```
With probability 0.5, sentence A and sentence B correspond to two consecutive sentences in the original corpus and in
the other cases, it's another random sentence in the corpus. Note that what is considered a sentence here is a
consecutive span of text usually longer than a single sentence. The only constrain is that the result with the two
"sentences" has a combined length of less than 512 tokens.
The details of the masking procedure for each sentence are the following:
- 15% of the tokens are masked.
- In 80% of the cases, the masked tokens are replaced by `[MASK]`.
- In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace.
- In the 10% remaining cases, the masked tokens are left as is.
### Pretraining
The model was trained on 16 Cloud TPU v2 chips for two million steps with a batch size
of 256. The sequence length was set to 512 throughout. The optimizer
used is Adam with a learning rate of 1e-4, \\(\beta_{1} = 0.9\\) and \\(\beta_{2} = 0.999\\), a weight decay of 0.01,
learning rate warmup for 10,000 steps and linear decay of the learning rate after.
### BibTeX entry and citation info
```bibtex
@article{DBLP:journals/corr/abs-2106-16163,
author = {Thibault Sellam and
Steve Yadlowsky and
Jason Wei and
Naomi Saphra and
Alexander D'Amour and
Tal Linzen and
Jasmijn Bastings and
Iulia Turc and
Jacob Eisenstein and
Dipanjan Das and
Ian Tenney and
Ellie Pavlick},
title = {The MultiBERTs: {BERT} Reproductions for Robustness Analysis},
journal = {CoRR},
volume = {abs/2106.16163},
year = {2021},
url = {https://arxiv.org/abs/2106.16163},
eprinttype = {arXiv},
eprint = {2106.16163},
timestamp = {Mon, 05 Jul 2021 15:15:50 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-2106-16163.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
<a href="https://huggingface.co/exbert/?model=multiberts">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
|
166c2f50ca2676e1d033afd79a756d6d
|
gokceuludogan/WarmMolGenOne
|
gokceuludogan
|
encoder-decoder
| 7 | 3 |
transformers
| 0 |
text2text-generation
| true | false | false |
mit
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['molecule-generation', 'cheminformatics', 'targeted-drug-design', 'biochemical-language-models']
| false | true | true | 2,632 | false |
## WarmMolGenOne
A target specific molecule generator model which is warm started (i.e. initialized) from pretrained biochemical language models and trained on interacting protein-compound pairs, viewing targeted molecular generation as a translation task between protein and molecular languages. It was introduced in the paper, "Exploiting pretrained biochemical language models for
targeted drug design", which has been accepted for publication in *Bioinformatics* Published by Oxford University Press and first released in [this repository](https://github.com/boun-tabi/biochemical-lms-for-drug-design).
WarmMolGenOne is a Transformer-based encoder-decoder model initialized with [Protein RoBERTa](https://github.com/PaccMann/paccmann_proteomics) and [ChemBERTa](https://huggingface.co/seyonec/PubChem10M_SMILES_BPE_450k) checkpoints and trained on interacting protein-compound pairs filtered from [BindingDB](https://www.bindingdb.org/rwd/bind/index.jsp). The model takes a protein sequence as an input and outputs a SMILES sequence.
## How to use
```python
from transformers import EncoderDecoderModel, RobertaTokenizer, pipeline
protein_tokenizer = RobertaTokenizer.from_pretrained("gokceuludogan/WarmMolGenOne")
mol_tokenizer = RobertaTokenizer.from_pretrained("seyonec/PubChem10M_SMILES_BPE_450k")
model = EncoderDecoderModel.from_pretrained("gokceuludogan/WarmMolGenOne")
inputs = protein_tokenizer("MENTENSVDSKSIKNLEPKIIHGSESMDSGISLDNSYKMDYPEMGLCIIINNKNFHKSTG", >>> return_tensors="pt")
outputs = model.generate(**inputs, decoder_start_token_id=mol_tokenizer.bos_token_id,
eos_token_id=mol_tokenizer.eos_token_id, pad_token_id=mol_tokenizer.eos_token_id,
max_length=128, num_return_sequences=5, do_sample=True, top_p=0.95)
mol_tokenizer.batch_decode(outputs, skip_special_tokens=True)
# Sample output
# ['Cn1cc(nn1)-c1ccccc1NS(=O)(=O)c1ccc2[nH]ccc2c1',
# 'CC(C)(C)c1[se]nc2sc(cc12)C(O)=O',
# '[O-][N+](=O)c1ccc(CN2CCC(CC2)NC(=O)c2cccc3ccccc23)cc1',
# 'OC(=O)CNC(=O)CCC\\C=C\\CN1[C@@H](Cc2cn(nn2)-c2ccccc2)C(=O)N[C@@H](CCCN2C(S)=NC(C)(C2=O)c2ccc(F)cc2)C1=O',
# 'OCC1(CCC1)C(=O)NCC1CCN(CC1)c1nc(c(s1)-c1ccc2OCOc2c1)C(O)=O']
```
## Citation
```bibtex
@article{10.1093/bioinformatics/btac482,
author = {Uludoğan, Gökçe and Ozkirimli, Elif and Ulgen, Kutlu O. and Karalı, Nilgün Lütfiye and Özgür, Arzucan},
title = "{Exploiting Pretrained Biochemical Language Models for Targeted Drug Design}",
journal = {Bioinformatics},
year = {2022},
doi = {10.1093/bioinformatics/btac482},
url = {https://doi.org/10.1093/bioinformatics/btac482}
}
```
|
37c90b660213cc39f71d55db253f6f57
|
hysts/TADNE
|
hysts
| null | 5 | 0 | null | 3 | null | false | false | false |
cc0-1.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['computer-vision', 'image-generation', 'anime']
| false | true | true | 8,979 | false |
# TADNE (This Anime Does Not Exist) model
The original TADNE site is https://thisanimedoesnotexist.ai/.
## Original TensorFlow model
The original TADNE model is provided in [this site](https://www.gwern.net/Faces#tadne-download) under CC-0 license. ([Google Drive](https://drive.google.com/file/d/1A-E_E32WAtTHRlOzjhhYhyyBDXLJN9_H))
## Model Conversion
The model in the `models` directory is converted with the following repo:
https://github.com/rosinality/stylegan2-pytorch
### Apply patches
```diff
--- a/model.py
+++ b/model.py
@@ -395,6 +395,7 @@ class Generator(nn.Module):
style_dim,
n_mlp,
channel_multiplier=2,
+ additional_multiplier=2,
blur_kernel=[1, 3, 3, 1],
lr_mlp=0.01,
):
@@ -426,6 +427,9 @@ class Generator(nn.Module):
512: 32 * channel_multiplier,
1024: 16 * channel_multiplier,
}
+ if additional_multiplier > 1:
+ for k in list(self.channels.keys()):
+ self.channels[k] *= additional_multiplier
self.input = ConstantInput(self.channels[4])
self.conv1 = StyledConv(
@@ -518,7 +522,7 @@ class Generator(nn.Module):
getattr(self.noises, f"noise_{i}") for i in range(self.num_layers)
]
- if truncation < 1:
+ if truncation_latent is not None:
style_t = []
for style in styles:
```
```diff
--- a/convert_weight.py
+++ b/convert_weight.py
@@ -221,6 +221,7 @@ if __name__ == "__main__":
default=2,
help="channel multiplier factor. config-f = 2, else = 1",
)
+ parser.add_argument("--additional_multiplier", type=int, default=2)
parser.add_argument("path", metavar="PATH", help="path to the tensorflow weights")
args = parser.parse_args()
@@ -243,7 +244,8 @@ if __name__ == "__main__":
if layer[0].startswith('Dense'):
n_mlp += 1
- g = Generator(size, 512, n_mlp, channel_multiplier=args.channel_multiplier)
+ style_dim = 512 * args.additional_multiplier
+ g = Generator(size, style_dim, n_mlp, channel_multiplier=args.channel_multiplier, additional_multiplier=args.additional_multiplier)
state_dict = g.state_dict()
state_dict = fill_statedict(state_dict, g_ema.vars, size, n_mlp)
@@ -254,7 +256,7 @@ if __name__ == "__main__":
ckpt = {"g_ema": state_dict, "latent_avg": latent_avg}
if args.gen:
- g_train = Generator(size, 512, n_mlp, channel_multiplier=args.channel_multiplier)
+ g_train = Generator(size, style_dim, n_mlp, channel_multiplier=args.channel_multiplier, additional_multiplier=args.additional_multiplier)
g_train_state = g_train.state_dict()
g_train_state = fill_statedict(g_train_state, generator.vars, size, n_mlp)
ckpt["g"] = g_train_state
@@ -271,9 +273,12 @@ if __name__ == "__main__":
batch_size = {256: 16, 512: 9, 1024: 4}
n_sample = batch_size.get(size, 25)
+ if args.additional_multiplier > 1:
+ n_sample = 2
+
g = g.to(device)
- z = np.random.RandomState(0).randn(n_sample, 512).astype("float32")
+ z = np.random.RandomState(0).randn(n_sample, style_dim).astype("float32")
with torch.no_grad():
img_pt, _ = g(
```
### Build Docker image
```dockerfile
FROM nvidia/cuda:10.0-cudnn7-devel-ubuntu18.04
ENV DEBIAN_FRONTEND=noninteractive
RUN apt-get update -y && \
apt-get install -y --no-install-recommends \
git \
ninja-build \
# pyenv dependencies \
make \
build-essential \
libssl-dev \
zlib1g-dev \
libbz2-dev \
libreadline-dev \
libsqlite3-dev \
wget \
curl \
llvm \
libncursesw5-dev \
xz-utils \
tk-dev \
libxml2-dev \
libxmlsec1-dev \
libffi-dev \
liblzma-dev && \
apt-get clean && \
rm -rf /var/lib/apt/lists/*
ARG PYTHON_VERSION=3.7.12
ENV PYENV_ROOT /opt/pyenv
ENV PATH ${PYENV_ROOT}/shims:${PYENV_ROOT}/bin:${PATH}
RUN curl https://pyenv.run | bash
RUN pyenv install ${PYTHON_VERSION} && \
pyenv global ${PYTHON_VERSION}
RUN pip install --no-cache-dir -U requests tqdm opencv-python-headless
RUN pip install --no-cache-dir -U tensorflow-gpu==1.15.4
RUN pip install --no-cache-dir -U torch==1.10.2+cu102 torchvision==0.11.3+cu102 -f https://download.pytorch.org/whl/torch/ -f https://download.pytorch.org/whl/torchvision/
RUN rm -rf ${HOME}/.cache/pip
WORKDIR /work
ENV PYTHONPATH /work/:${PYTHONPATH}
```
```bash
docker build . -t stylegan2_pytorch
```
### Convert
```bash
git clone https://github.com/NVLabs/stylegan2
docker run --rm -it -u $(id -u):$(id -g) -e XDG_CACHE_HOME=/work --ipc host --gpus all -w /work -v `pwd`:/work stylegan2_pytorch python convert_weight.py --repo stylegan2 aydao-anime-danbooru2019s-512-5268480.pkl
```
## Usage
### Apply patch
```diff
--- a/generate.py
+++ b/generate.py
@@ -6,21 +6,25 @@ from model import Generator
from tqdm import tqdm
-def generate(args, g_ema, device, mean_latent):
+def generate(args, g_ema, device, mean_latent, randomize_noise):
with torch.no_grad():
g_ema.eval()
for i in tqdm(range(args.pics)):
- sample_z = torch.randn(args.sample, args.latent, device=device)
+ samples = []
+ for _ in range(args.split):
+ sample_z = torch.randn(args.sample // args.split, args.latent, device=device)
- sample, _ = g_ema(
- [sample_z], truncation=args.truncation, truncation_latent=mean_latent
- )
+ sample, _ = g_ema(
+ [sample_z], truncation=args.truncation, truncation_latent=mean_latent,
+ randomize_noise=randomize_noise
+ )
+ samples.extend(sample)
utils.save_image(
- sample,
- f"sample/{str(i).zfill(6)}.png",
- nrow=1,
+ samples,
+ f"{args.output_dir}/{str(i).zfill(6)}.{args.ext}",
+ nrow=args.ncol,
normalize=True,
range=(-1, 1),
)
@@ -30,6 +34,8 @@ if __name__ == "__main__":
device = "cuda"
parser = argparse.ArgumentParser(description="Generate samples from the generator")
+ parser.add_argument("--seed", type=int, default=0)
+ parser.add_argument("--output-dir", '-o', type=str, required=True)
parser.add_argument(
"--size", type=int, default=1024, help="output image size of the generator"
@@ -37,11 +43,14 @@ if __name__ == "__main__":
parser.add_argument(
"--sample",
type=int,
- default=1,
+ default=100,
help="number of samples to be generated for each image",
)
+ parser.add_argument("--ncol", type=int, default=10)
+ parser.add_argument("--split", type=int, default=4)
+ parser.add_argument("--ext", type=str, default='png')
parser.add_argument(
- "--pics", type=int, default=20, help="number of images to be generated"
+ "--pics", type=int, default=1, help="number of images to be generated"
)
parser.add_argument("--truncation", type=float, default=1, help="truncation ratio")
parser.add_argument(
@@ -62,23 +71,31 @@ if __name__ == "__main__":
default=2,
help="channel multiplier of the generator. config-f = 2, else = 1",
)
+ parser.add_argument("--additional_multiplier", type=int, default=1)
+ parser.add_argument("--load_latent_vec", action='store_true')
+ parser.add_argument("--no-randomize-noise", dest='randomize_noise', action='store_false')
+ parser.add_argument("--n_mlp", type=int, default=8)
args = parser.parse_args()
- args.latent = 512
- args.n_mlp = 8
+ seed = args.seed
+ torch.manual_seed(seed)
+ torch.cuda.manual_seed_all(seed)
+
+ args.latent = 512 * args.additional_multiplier
g_ema = Generator(
- args.size, args.latent, args.n_mlp, channel_multiplier=args.channel_multiplier
+ args.size, args.latent, args.n_mlp, channel_multiplier=args.channel_multiplier,
+ additional_multiplier=args.additional_multiplier
).to(device)
checkpoint = torch.load(args.ckpt)
- g_ema.load_state_dict(checkpoint["g_ema"])
+ g_ema.load_state_dict(checkpoint["g_ema"], strict=True)
- if args.truncation < 1:
+ if not args.load_latent_vec:
with torch.no_grad():
mean_latent = g_ema.mean_latent(args.truncation_mean)
else:
- mean_latent = None
+ mean_latent = checkpoint['latent_avg'].to(device)
- generate(args, g_ema, device, mean_latent)
+ generate(args, g_ema, device, mean_latent, randomize_noise=args.randomize_noise)
```
### Run
```bash
python generate.py --ckpt aydao-anime-danbooru2019s-512-5268480.pt --size 512 --n_mlp 4 --additional_multiplier 2 --load_latent_vec --no-randomize-noise -o out_images --truncation 0.6 --seed 333 --pics 1 --sample 48 --ncol 8 --ext jpg
```
|
14ad74f4018d853a06d4c85fd2d7dd1a
|
Helsinki-NLP/opus-mt-bi-en
|
Helsinki-NLP
|
marian
| 10 | 59 |
transformers
| 0 |
translation
| true | true | false |
apache-2.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['translation']
| false | true | true | 768 | false |
### opus-mt-bi-en
* source languages: bi
* target languages: en
* OPUS readme: [bi-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/bi-en/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-20.zip](https://object.pouta.csc.fi/OPUS-MT-models/bi-en/opus-2020-01-20.zip)
* test set translations: [opus-2020-01-20.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/bi-en/opus-2020-01-20.test.txt)
* test set scores: [opus-2020-01-20.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/bi-en/opus-2020-01-20.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.bi.en | 30.3 | 0.458 |
|
d8c05e635c545128b957b3f3e8c2fe74
|
Helsinki-NLP/opus-mt-en-cpf
|
Helsinki-NLP
|
marian
| 11 | 11 |
transformers
| 0 |
translation
| true | true | false |
apache-2.0
|
['en', 'ht', 'cpf']
| null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['translation']
| false | true | true | 2,318 | false |
### eng-cpf
* source group: English
* target group: Creoles and pidgins, French‑based
* OPUS readme: [eng-cpf](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-cpf/README.md)
* model: transformer
* source language(s): eng
* target language(s): gcf_Latn hat mfe
* model: transformer
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* a sentence initial language token is required in the form of `>>id<<` (id = valid target language ID)
* download original weights: [opus-2020-07-26.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-cpf/opus-2020-07-26.zip)
* test set translations: [opus-2020-07-26.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-cpf/opus-2020-07-26.test.txt)
* test set scores: [opus-2020-07-26.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-cpf/opus-2020-07-26.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.eng-gcf.eng.gcf | 6.2 | 0.262 |
| Tatoeba-test.eng-hat.eng.hat | 25.7 | 0.451 |
| Tatoeba-test.eng-mfe.eng.mfe | 80.1 | 0.900 |
| Tatoeba-test.eng.multi | 15.9 | 0.354 |
### System Info:
- hf_name: eng-cpf
- source_languages: eng
- target_languages: cpf
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-cpf/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['en', 'ht', 'cpf']
- src_constituents: {'eng'}
- tgt_constituents: {'gcf_Latn', 'hat', 'mfe'}
- src_multilingual: False
- tgt_multilingual: True
- prepro: normalization + SentencePiece (spm32k,spm32k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/eng-cpf/opus-2020-07-26.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/eng-cpf/opus-2020-07-26.test.txt
- src_alpha3: eng
- tgt_alpha3: cpf
- short_pair: en-cpf
- chrF2_score: 0.354
- bleu: 15.9
- brevity_penalty: 1.0
- ref_len: 1012.0
- src_name: English
- tgt_name: Creoles and pidgins, French‑based
- train_date: 2020-07-26
- src_alpha2: en
- tgt_alpha2: cpf
- prefer_old: False
- long_pair: eng-cpf
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41
|
56a67509d0b60515f01f3485e469bcc8
|
jonatasgrosman/wav2vec2-large-xlsr-53-greek
|
jonatasgrosman
|
wav2vec2
| 8 | 65 |
transformers
| 0 |
automatic-speech-recognition
| true | false | true |
apache-2.0
|
['el']
|
['common_voice']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week']
| true | true | true | 7,104 | false |
# Fine-tuned XLSR-53 large model for speech recognition in Greek
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Greek using the train and validation splits of [Common Voice 6.1](https://huggingface.co/datasets/common_voice) and [CSS10](https://github.com/Kyubyong/css10).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned thanks to the GPU credits generously given by the [OVHcloud](https://www.ovhcloud.com/en/public-cloud/ai-training/) :)
The script used for training can be found here: https://github.com/jonatasgrosman/wav2vec2-sprint
## Usage
The model can be used directly (without a language model) as follows...
Using the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) library:
```python
from huggingsound import SpeechRecognitionModel
model = SpeechRecognitionModel("jonatasgrosman/wav2vec2-large-xlsr-53-greek")
audio_paths = ["/path/to/file.mp3", "/path/to/another_file.wav"]
transcriptions = model.transcribe(audio_paths)
```
Writing your own inference script:
```python
import torch
import librosa
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
LANG_ID = "el"
MODEL_ID = "jonatasgrosman/wav2vec2-large-xlsr-53-greek"
SAMPLES = 5
test_dataset = load_dataset("common_voice", LANG_ID, split=f"test[:{SAMPLES}]")
processor = Wav2Vec2Processor.from_pretrained(MODEL_ID)
model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID)
# Preprocessing the datasets.
# We need to read the audio files as arrays
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = librosa.load(batch["path"], sr=16_000)
batch["speech"] = speech_array
batch["sentence"] = batch["sentence"].upper()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
predicted_ids = torch.argmax(logits, dim=-1)
predicted_sentences = processor.batch_decode(predicted_ids)
for i, predicted_sentence in enumerate(predicted_sentences):
print("-" * 100)
print("Reference:", test_dataset[i]["sentence"])
print("Prediction:", predicted_sentence)
```
| Reference | Prediction |
| ------------- | ------------- |
| ΤΟ ΒΑΣΙΛΌΠΟΥΛΟ, ΠΟΥ ΜΟΙΆΖΕΙ ΛΕΟΝΤΑΡΆΚΙ ΚΑΙ ΑΕΤΟΥΔΆΚΙ | ΤΟ ΒΑΣΙΛΌΠΟΥΛΟ ΠΟΥ ΜΙΑΣΕ ΛΙΟΝΤΑΡΑΚΉ ΚΑΙ ΑΪΤΟΥΔΆΚΙ |
| ΣΥΝΆΜΑ ΞΕΠΡΌΒΑΛΑΝ ΑΠΌ ΜΈΣΑ ΑΠΌ ΤΑ ΔΈΝΤΡΑ, ΔΕΞΙΆ, ΑΡΜΑΤΩΜΈΝΟΙ ΚΑΒΑΛΑΡΈΟΙ. | ΣΥΝΆΜΑ ΚΑΙ ΤΡΌΒΑΛΑΝ ΑΠΌ ΜΈΣΑ ΑΠΌ ΤΑ ΔΈΝΤΡΑ ΔΕΞΙΆ ΑΡΜΑΤΩΜΈΝΟΙ ΚΑΒΑΛΑΡΈΟΙ |
| ΤΑ ΣΥΣΚΕΥΑΣΜΈΝΑ ΒΙΟΛΟΓΙΚΆ ΛΑΧΑΝΙΚΆ ΔΕΝ ΠΕΡΙΈΧΟΥΝ ΣΥΝΤΗΡΗΤΙΚΆ ΚΑΙ ΟΡΜΌΝΕΣ | ΤΑ ΣΥΣΚΕΦΑΣΜΈΝΑ ΒΙΟΛΟΓΙΚΆ ΛΑΧΑΝΙΚΆ ΔΕΝ ΠΕΡΙΈΧΟΥΝ ΣΙΔΗΡΗΤΙΚΆ ΚΑΙ ΟΡΜΌΝΕΣ |
| ΑΚΟΛΟΥΘΉΣΕΤΕ ΜΕ! | ΑΚΟΛΟΥΘΉΣΤΕ ΜΕ |
| ΚΑΙ ΠΟΎ ΜΠΟΡΏ ΝΑ ΤΟΝ ΒΡΩ; | Ε ΠΟΎ ΜΠΟΡΏ ΝΑ ΤΙ ΕΒΡΩ |
| ΝΑΙ! ΑΠΟΚΡΊΘΗΚΕ ΤΟ ΠΑΙΔΊ | ΝΑΙ ΑΠΟΚΡΊΘΗΚΕ ΤΟ ΠΑΙΔΊ |
| ΤΟ ΠΑΛΆΤΙ ΜΟΥ ΤΟ ΠΡΟΜΉΘΕΥΕ. | ΤΟ ΠΑΛΆΤΙ ΜΟΥ ΤΟ ΠΡΟΜΉΘΕΥΕ |
| ΉΛΘΕ ΜΉΝΥΜΑ ΑΠΌ ΤΟ ΘΕΊΟ ΒΑΣΙΛΙΆ; | ΉΛΘΑ ΜΕΊΝΕΙ ΜΕ ΑΠΌ ΤΟ ΘΕΊΟ ΒΑΣΊΛΙΑ |
| ΠΑΡΑΚΆΤΩ, ΈΝΑ ΡΥΆΚΙ ΜΟΥΡΜΟΎΡΙΖΕ ΓΛΥΚΆ, ΚΥΛΏΝΤΑΣ ΤΑ ΚΡΥΣΤΑΛΛΈΝΙΑ ΝΕΡΆ ΤΟΥ ΑΝΆΜΕΣΑ ΣΤΑ ΠΥΚΝΆ ΧΑΜΌΔΕΝΤΡΑ. | ΠΑΡΑΚΆΤΩ ΈΝΑ ΡΥΆΚΙ ΜΟΥΡΜΟΎΡΙΖΕ ΓΛΥΚΆ ΚΥΛΏΝΤΑΣ ΤΑ ΚΡΥΣΤΑΛΛΈΝΙΑ ΝΕΡΆ ΤΟΥ ΑΝΆΜΕΣΑ ΣΤΑ ΠΥΚΡΆ ΧΑΜΌΔΕΝΤΡΑ |
| ΠΡΆΓΜΑΤΙ, ΕΊΝΑΙ ΑΣΤΕΊΟ ΝΑ ΠΆΡΕΙ Ο ΔΙΆΒΟΛΟΣ | ΠΡΆΓΜΑΤΗ ΕΊΝΑΙ ΑΣΤΕΊΟ ΝΑ ΠΆΡΕΙ Ο ΔΙΆΒΟΛΟΣ |
## Evaluation
The model can be evaluated as follows on the Greek test data of Common Voice.
```python
import torch
import re
import librosa
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
LANG_ID = "el"
MODEL_ID = "jonatasgrosman/wav2vec2-large-xlsr-53-greek"
DEVICE = "cuda"
CHARS_TO_IGNORE = [",", "?", "¿", ".", "!", "¡", ";", "；", ":", '""', "%", '"', "�", "ʿ", "·", "჻", "~", "՞",
"؟", "،", "।", "॥", "«", "»", "„", "“", "”", "「", "」", "‘", "’", "《", "》", "(", ")", "[", "]",
"{", "}", "=", "`", "_", "+", "<", ">", "…", "–", "°", "´", "ʾ", "‹", "›", "©", "®", "—", "→", "。",
"、", "﹂", "﹁", "‧", "～", "﹏", "，", "｛", "｝", "（", "）", "［", "］", "【", "】", "‥", "〽",
"『", "』", "〝", "〟", "⟨", "⟩", "〜", "：", "！", "？", "♪", "؛", "/", "\\\\", "º", "−", "^", "ʻ", "ˆ"]
test_dataset = load_dataset("common_voice", LANG_ID, split="test")
wer = load_metric("wer.py") # https://github.com/jonatasgrosman/wav2vec2-sprint/blob/main/wer.py
cer = load_metric("cer.py") # https://github.com/jonatasgrosman/wav2vec2-sprint/blob/main/cer.py
chars_to_ignore_regex = f"[{re.escape(''.join(CHARS_TO_IGNORE))}]"
processor = Wav2Vec2Processor.from_pretrained(MODEL_ID)
model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID)
model.to(DEVICE)
# Preprocessing the datasets.
# We need to read the audio files as arrays
def speech_file_to_array_fn(batch):
with warnings.catch_warnings():
warnings.simplefilter("ignore")
speech_array, sampling_rate = librosa.load(batch["path"], sr=16_000)
batch["speech"] = speech_array
batch["sentence"] = re.sub(chars_to_ignore_regex, "", batch["sentence"]).upper()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
# Preprocessing the datasets.
# We need to read the audio files as arrays
def evaluate(batch):
inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values.to(DEVICE), attention_mask=inputs.attention_mask.to(DEVICE)).logits
pred_ids = torch.argmax(logits, dim=-1)
batch["pred_strings"] = processor.batch_decode(pred_ids)
return batch
result = test_dataset.map(evaluate, batched=True, batch_size=8)
predictions = [x.upper() for x in result["pred_strings"]]
references = [x.upper() for x in result["sentence"]]
print(f"WER: {wer.compute(predictions=predictions, references=references, chunk_size=1000) * 100}")
print(f"CER: {cer.compute(predictions=predictions, references=references, chunk_size=1000) * 100}")
```
**Test Result**:
In the table below I report the Word Error Rate (WER) and the Character Error Rate (CER) of the model. I ran the evaluation script described above on other models as well (on 2021-04-22). Note that the table below may show different results from those already reported, this may have been caused due to some specificity of the other evaluation scripts used.
| Model | WER | CER |
| ------------- | ------------- | ------------- |
| lighteternal/wav2vec2-large-xlsr-53-greek | **10.13%** | **2.66%** |
| jonatasgrosman/wav2vec2-large-xlsr-53-greek | 11.62% | 3.36% |
| vasilis/wav2vec2-large-xlsr-53-greek | 19.09% | 5.88% |
| PereLluis13/wav2vec2-large-xlsr-53-greek | 20.16% | 5.71% |
## Citation
If you want to cite this model you can use this:
```bibtex
@misc{grosman2021xlsr53-large-greek,
title={Fine-tuned {XLSR}-53 large model for speech recognition in {G}reek},
author={Grosman, Jonatas},
howpublished={\url{https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-greek}},
year={2021}
}
```
|
eb8ca6eaeb7df661c59e3595a93c434f
|
akshaychaudhary/distilbert-base-uncased-finetuned-ner
|
akshaychaudhary
|
distilbert
| 27 | 7 |
transformers
| 0 |
token-classification
| true | false | false |
apache-2.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,542 | false |
<!-- 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-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9988
- Precision: 0.3
- Recall: 0.6
- F1: 0.4
- Accuracy: 0.7870
## 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
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 84 | 0.8399 | 0.2105 | 0.4 | 0.2759 | 0.75 |
| No log | 2.0 | 168 | 0.9664 | 0.3 | 0.6 | 0.4 | 0.7870 |
| No log | 3.0 | 252 | 0.9988 | 0.3 | 0.6 | 0.4 | 0.7870 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.2
- Tokenizers 0.11.0
|
1b0f198ffb226c7ffa721a0e9470d1b1
|
Akash7897/gpt2-wikitext2
|
Akash7897
|
gpt2
| 14 | 2 |
transformers
| 0 |
text-generation
| true | false | false |
mit
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,216 | false |
<!-- 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: 6.1079
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 6.558 | 1.0 | 2249 | 6.4672 |
| 6.1918 | 2.0 | 4498 | 6.1970 |
| 6.0019 | 3.0 | 6747 | 6.1079 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.6
|
2aeb5b0d382d41164168b4ce9cb52377
|
jnieus01/bert-emotion
|
jnieus01
|
distilbert
| 12 | 1 |
transformers
| 0 |
text-classification
| true | false | false |
apache-2.0
| null |
['tweet_eval']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,455 | false |
<!-- 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-emotion
This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3717
- Precision: 0.6917
- Recall: 0.7048
- Fscore: 0.6955
## 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
- 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 | Precision | Recall | Fscore |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|
| 0.8838 | 1.0 | 815 | 0.7944 | 0.7238 | 0.6662 | 0.6860 |
| 0.5708 | 2.0 | 1630 | 1.0606 | 0.6594 | 0.6139 | 0.6299 |
| 0.3045 | 3.0 | 2445 | 1.3717 | 0.6917 | 0.7048 | 0.6955 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
062d6b2b5b385ca4e420702c1fb93064
|
4ytk3/fakepaperbot_gpt-2
|
4ytk3
|
gpt2
| 12 | 6 |
transformers
| 0 |
text-generation
| true | false | false |
mit
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,015 | false |
<!-- 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. -->
# output
This model is a fine-tuned version of [rinna/japanese-gpt2-small](https://huggingface.co/rinna/japanese-gpt2-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4525
- Accuracy: 0.4155
## 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
- num_epochs: 10.0
### Training results
### Framework versions
- Transformers 4.25.0.dev0
- Pytorch 1.13.0
- Datasets 2.6.1
- Tokenizers 0.13.2
|
359d0364a22fd9f9cb31a48ac4f11cd5
|
CLTL/icf-levels-mbw
|
CLTL
|
roberta
| 11 | 10 |
transformers
| 1 |
text-classification
| true | false | false |
mit
|
['nl']
| null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
[]
| false | true | true | 3,260 | false |
# Regression Model for Weight Maintenance Functioning Levels (ICF b530)
## Description
A fine-tuned regression model that assigns a functioning level to Dutch sentences describing weight maintenance functions. The model is based on a pre-trained Dutch medical language model ([link to be added]()): a RoBERTa model, trained from scratch on clinical notes of the Amsterdam UMC. To detect sentences about weight maintenance functions in clinical text in Dutch, use the [icf-domains](https://huggingface.co/CLTL/icf-domains) classification model.
## Functioning levels
Level | Meaning
---|---
4 | Healthy weight, no unintentional weight loss or gain, SNAQ 0 or 1.
3 | Some unintentional weight loss or gain, or lost a lot of weight but gained some of it back afterwards.
2 | Moderate unintentional weight loss or gain (more than 3 kg in the last month), SNAQ 2.
1 | Severe unintentional weight loss or gain (more than 6 kg in the last 6 months), SNAQ ≥ 3.
0 | Severe unintentional weight loss or gain (more than 6 kg in the last 6 months) and admitted to ICU.
The predictions generated by the model might sometimes be outside of the scale (e.g. 4.2); this is normal in a regression model.
## Intended uses and limitations
- The model was fine-tuned (trained, validated and tested) on medical records from the Amsterdam UMC (the two academic medical centers of Amsterdam). It might perform differently on text from a different hospital or text from non-hospital sources (e.g. GP records).
- The model was fine-tuned with the [Simple Transformers](https://simpletransformers.ai/) library. This library is based on Transformers but the model cannot be used directly with Transformers `pipeline` and classes; doing so would generate incorrect outputs. For this reason, the API on this page is disabled.
## How to use
To generate predictions with the model, use the [Simple Transformers](https://simpletransformers.ai/) library:
```
from simpletransformers.classification import ClassificationModel
model = ClassificationModel(
'roberta',
'CLTL/icf-levels-mbw',
use_cuda=False,
)
example = 'Tijdens opname >10 kg afgevallen.'
_, raw_outputs = model.predict([example])
predictions = np.squeeze(raw_outputs)
```
The prediction on the example is:
```
1.95
```
The raw outputs look like this:
```
[[1.95429301]]
```
## Training data
- The training data consists of clinical notes from medical records (in Dutch) of the Amsterdam UMC. Due to privacy constraints, the data cannot be released.
- The annotation guidelines used for the project can be found [here](https://github.com/cltl/a-proof-zonmw/tree/main/resources/annotation_guidelines).
## Training procedure
The default training parameters of Simple Transformers were used, including:
- Optimizer: AdamW
- Learning rate: 4e-5
- Num train epochs: 1
- Train batch size: 8
## Evaluation results
The evaluation is done on a sentence-level (the classification unit) and on a note-level (the aggregated unit which is meaningful for the healthcare professionals).
| | Sentence-level | Note-level
|---|---|---
mean absolute error | 0.81 | 0.60
mean squared error | 0.83 | 0.56
root mean squared error | 0.91 | 0.75
## Authors and references
### Authors
Jenia Kim, Piek Vossen
### References
TBD
|
585b323f0fb150d335a0d5cafc5f7f9d
|
muhtasham/tiny-mlm-glue-wnli-target-glue-mnli
|
muhtasham
|
bert
| 10 | 2 |
transformers
| 0 |
text-classification
| true | false | false |
apache-2.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,811 | false |
<!-- 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. -->
# tiny-mlm-glue-wnli-target-glue-mnli
This model is a fine-tuned version of [muhtasham/tiny-mlm-glue-wnli](https://huggingface.co/muhtasham/tiny-mlm-glue-wnli) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8534
- Accuracy: 0.6159
## 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: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- training_steps: 5000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.0812 | 0.04 | 500 | 1.0475 | 0.4698 |
| 1.0185 | 0.08 | 1000 | 0.9640 | 0.5484 |
| 0.9627 | 0.12 | 1500 | 0.9279 | 0.5657 |
| 0.9401 | 0.16 | 2000 | 0.9181 | 0.5779 |
| 0.9307 | 0.2 | 2500 | 0.8954 | 0.5926 |
| 0.9249 | 0.24 | 3000 | 0.8846 | 0.5998 |
| 0.9083 | 0.29 | 3500 | 0.8752 | 0.6028 |
| 0.9022 | 0.33 | 4000 | 0.8636 | 0.6108 |
| 0.8841 | 0.37 | 4500 | 0.8628 | 0.6095 |
| 0.8857 | 0.41 | 5000 | 0.8534 | 0.6159 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu116
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2
|
e7618486424649679dfd3f1cbfe669b6
|
Helsinki-NLP/opus-mt-fr-crs
|
Helsinki-NLP
|
marian
| 10 | 8 |
transformers
| 0 |
translation
| true | true | false |
apache-2.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['translation']
| false | true | true | 776 | false |
### opus-mt-fr-crs
* source languages: fr
* target languages: crs
* OPUS readme: [fr-crs](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fr-crs/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-09.zip](https://object.pouta.csc.fi/OPUS-MT-models/fr-crs/opus-2020-01-09.zip)
* test set translations: [opus-2020-01-09.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/fr-crs/opus-2020-01-09.test.txt)
* test set scores: [opus-2020-01-09.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/fr-crs/opus-2020-01-09.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.fr.crs | 31.6 | 0.492 |
|
4beb33c5bd8e0303b3cac44497838bad
|
romin23/lilt-form-read
|
romin23
|
lilt
| 15 | 3 |
transformers
| 0 |
token-classification
| true | false | false |
mit
| null |
['funsd-layoutlmv3']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 7,752 | false |
<!-- 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. -->
# lilt-form-read
This model is a fine-tuned version of [SCUT-DLVCLab/lilt-roberta-en-base](https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base) on the funsd-layoutlmv3 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7208
- Answer: {'precision': 0.8635321100917431, 'recall': 0.9216646266829865, 'f1': 0.8916518650088809, 'number': 817}
- Header: {'precision': 0.6813186813186813, 'recall': 0.5210084033613446, 'f1': 0.5904761904761905, 'number': 119}
- Question: {'precision': 0.9005424954792043, 'recall': 0.924791086350975, 'f1': 0.9125057260650481, 'number': 1077}
- Overall Precision: 0.8753
- Overall Recall: 0.8997
- Overall F1: 0.8873
- Overall Accuracy: 0.8077
## 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
- training_steps: 2500
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Answer | Header | Question | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.4555 | 10.53 | 200 | 0.9514 | {'precision': 0.8207440811724915, 'recall': 0.8910648714810282, 'f1': 0.8544600938967137, 'number': 817} | {'precision': 0.6233766233766234, 'recall': 0.40336134453781514, 'f1': 0.48979591836734687, 'number': 119} | {'precision': 0.8611825192802056, 'recall': 0.9331476323119777, 'f1': 0.8957219251336899, 'number': 1077} | 0.8358 | 0.8847 | 0.8596 | 0.7991 |
| 0.0457 | 21.05 | 400 | 1.4096 | {'precision': 0.8654088050314466, 'recall': 0.8421052631578947, 'f1': 0.8535980148883374, 'number': 817} | {'precision': 0.5833333333333334, 'recall': 0.5294117647058824, 'f1': 0.5550660792951542, 'number': 119} | {'precision': 0.8606837606837607, 'recall': 0.9350046425255338, 'f1': 0.8963061860258122, 'number': 1077} | 0.8480 | 0.8733 | 0.8605 | 0.7914 |
| 0.0144 | 31.58 | 600 | 1.4435 | {'precision': 0.8720095693779905, 'recall': 0.8922888616891065, 'f1': 0.8820326678765881, 'number': 817} | {'precision': 0.6428571428571429, 'recall': 0.5294117647058824, 'f1': 0.5806451612903226, 'number': 119} | {'precision': 0.8682581786030061, 'recall': 0.9117920148560817, 'f1': 0.8894927536231884, 'number': 1077} | 0.8591 | 0.8813 | 0.8700 | 0.8033 |
| 0.008 | 42.11 | 800 | 1.5197 | {'precision': 0.8660287081339713, 'recall': 0.8861689106487148, 'f1': 0.8759830611010284, 'number': 817} | {'precision': 0.5798319327731093, 'recall': 0.5798319327731093, 'f1': 0.5798319327731093, 'number': 119} | {'precision': 0.8838248436103664, 'recall': 0.9182915506035283, 'f1': 0.9007285974499089, 'number': 1077} | 0.8592 | 0.8852 | 0.8720 | 0.7921 |
| 0.0039 | 52.63 | 1000 | 1.4373 | {'precision': 0.8733727810650888, 'recall': 0.9033047735618115, 'f1': 0.888086642599278, 'number': 817} | {'precision': 0.6019417475728155, 'recall': 0.5210084033613446, 'f1': 0.5585585585585585, 'number': 119} | {'precision': 0.8854351687388987, 'recall': 0.9257195914577531, 'f1': 0.9051293690422152, 'number': 1077} | 0.8664 | 0.8927 | 0.8794 | 0.8096 |
| 0.0028 | 63.16 | 1200 | 1.7146 | {'precision': 0.8490351872871736, 'recall': 0.9155446756425949, 'f1': 0.8810365135453475, 'number': 817} | {'precision': 0.6941176470588235, 'recall': 0.4957983193277311, 'f1': 0.5784313725490197, 'number': 119} | {'precision': 0.8852313167259787, 'recall': 0.9238625812441968, 'f1': 0.9041344843253067, 'number': 1077} | 0.8622 | 0.8952 | 0.8784 | 0.7971 |
| 0.0022 | 73.68 | 1400 | 1.5638 | {'precision': 0.8608893956670467, 'recall': 0.9241126070991432, 'f1': 0.8913813459268004, 'number': 817} | {'precision': 0.6565656565656566, 'recall': 0.5462184873949579, 'f1': 0.5963302752293578, 'number': 119} | {'precision': 0.8993536472760849, 'recall': 0.904363974001857, 'f1': 0.9018518518518519, 'number': 1077} | 0.8713 | 0.8912 | 0.8811 | 0.8051 |
| 0.0009 | 84.21 | 1600 | 1.7113 | {'precision': 0.8682080924855491, 'recall': 0.9192166462668299, 'f1': 0.8929845422116528, 'number': 817} | {'precision': 0.65625, 'recall': 0.5294117647058824, 'f1': 0.586046511627907, 'number': 119} | {'precision': 0.9085027726432532, 'recall': 0.9127205199628597, 'f1': 0.9106067623899953, 'number': 1077} | 0.8796 | 0.8927 | 0.8861 | 0.8039 |
| 0.0009 | 94.74 | 1800 | 1.6397 | {'precision': 0.8767942583732058, 'recall': 0.8971848225214198, 'f1': 0.8868723532970357, 'number': 817} | {'precision': 0.6274509803921569, 'recall': 0.5378151260504201, 'f1': 0.579185520361991, 'number': 119} | {'precision': 0.898458748866727, 'recall': 0.9201485608170845, 'f1': 0.9091743119266055, 'number': 1077} | 0.8760 | 0.8882 | 0.8821 | 0.8042 |
| 0.0004 | 105.26 | 2000 | 1.7362 | {'precision': 0.8690614136732329, 'recall': 0.9179926560587516, 'f1': 0.8928571428571428, 'number': 817} | {'precision': 0.6458333333333334, 'recall': 0.5210084033613446, 'f1': 0.5767441860465117, 'number': 119} | {'precision': 0.8928892889288929, 'recall': 0.9210770659238626, 'f1': 0.9067641681901281, 'number': 1077} | 0.8715 | 0.8962 | 0.8837 | 0.8040 |
| 0.0003 | 115.79 | 2200 | 1.7208 | {'precision': 0.8635321100917431, 'recall': 0.9216646266829865, 'f1': 0.8916518650088809, 'number': 817} | {'precision': 0.6813186813186813, 'recall': 0.5210084033613446, 'f1': 0.5904761904761905, 'number': 119} | {'precision': 0.9005424954792043, 'recall': 0.924791086350975, 'f1': 0.9125057260650481, 'number': 1077} | 0.8753 | 0.8997 | 0.8873 | 0.8077 |
| 0.0002 | 126.32 | 2400 | 1.7281 | {'precision': 0.8819362455726092, 'recall': 0.9143206854345165, 'f1': 0.8978365384615384, 'number': 817} | {'precision': 0.6631578947368421, 'recall': 0.5294117647058824, 'f1': 0.5887850467289719, 'number': 119} | {'precision': 0.8917710196779964, 'recall': 0.9257195914577531, 'f1': 0.9084282460136676, 'number': 1077} | 0.8772 | 0.8977 | 0.8873 | 0.8060 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.7.1
- Tokenizers 0.13.2
|
129c8cae67970868013f6fcf2da5c807
|
y-oikawa/Information-triage-for-disaster-tweets
|
y-oikawa
|
electra
| 7 | 1 |
transformers
| 1 |
text-classification
| true | false | false |
cc-by-sa-4.0
|
['ja']
| null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
[]
| false | true | true | 1,180 | false |
# ELECTRA Base Japanese for Information Triage
This is an ELECTRA model pretrained on approximately 200M Japanese sentences additionally finetuned for Information Triage.
The model was based on [transformers-ud-japanese-electra-base-discriminator](https://huggingface.co/megagonlabs/transformers-ud-japanese-electra-base-discriminator), and later finetuned on a dataset containing disaster tweets.
## Licenses
The finetuned model with all attached files is licensed under [CC BY-SA 4.0](http://creativecommons.org/licenses/by-sa/4.0/), or Creative Commons Attribution-ShareAlike 4.0 International License.
<a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-sa/4.0/88x31.png" /></a>
## Citations
Please, cite this model using the following citation.
```
@inproceedings{oikawa2022electra-base-triage,
title={北見工業大学 テキスト情報処理研究室 ELECTRA Base 情報トリアージモデル (megagon labs ver.)},
author={及川 佑人 and プタシンスキ ミハウ and 桝井 文人},
publisher={HuggingFace},
year={2022},
url = "https://huggingface.co/y-oikawa/Information-triage-for-disaster-tweets"
}
```
|
d620ec0621f949911cbed8d9d860a4bc
|
annahaz/xlm-roberta-base-misogyny-sexism-out-of-sample-test
|
annahaz
|
xlm-roberta
| 10 | 3 |
transformers
| 0 |
text-classification
| true | false | false |
mit
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 2,328 | false |
<!-- 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. -->
# xlm-roberta-base-misogyny-sexism-out-of-sample-test
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4319
- Accuracy: 0.6329
- F1: 0.5384
- Precision: 0.6311
- Recall: 0.4694
- Mae: 0.3671
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Mae |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|:------:|
| 0.3447 | 1.0 | 2157 | 0.8407 | 0.6264 | 0.4817 | 0.6555 | 0.3808 | 0.3736 |
| 0.3105 | 2.0 | 4314 | 0.9660 | 0.6244 | 0.4840 | 0.6480 | 0.3863 | 0.3756 |
| 0.3036 | 3.0 | 6471 | 1.0797 | 0.6218 | 0.5499 | 0.6014 | 0.5065 | 0.3782 |
| 0.2643 | 4.0 | 8628 | 1.6355 | 0.6301 | 0.4790 | 0.6696 | 0.3728 | 0.3699 |
| 0.2591 | 5.0 | 10785 | 1.4902 | 0.6173 | 0.5308 | 0.6020 | 0.4747 | 0.3827 |
| 0.2052 | 6.0 | 12942 | 1.6884 | 0.6236 | 0.5166 | 0.6235 | 0.4410 | 0.3764 |
| 0.2017 | 7.0 | 15099 | 2.1026 | 0.6323 | 0.5341 | 0.6325 | 0.4622 | 0.3677 |
| 0.1715 | 8.0 | 17256 | 2.3440 | 0.6292 | 0.5381 | 0.6229 | 0.4736 | 0.3708 |
| 0.1543 | 9.0 | 19413 | 2.2136 | 0.6301 | 0.5411 | 0.6230 | 0.4783 | 0.3699 |
| 0.1456 | 10.0 | 21570 | 2.4319 | 0.6329 | 0.5384 | 0.6311 | 0.4694 | 0.3671 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.9.0+cu111
- Datasets 2.3.2
- Tokenizers 0.12.1
|
5e60f1d45a40bfc6e9adc4a7cc0f8ba8
|
lewtun/minilm-finetuned-emotion
|
lewtun
|
bert
| 12 | 3 |
transformers
| 0 |
text-classification
| true | false | false |
mit
| null |
['emotion']
| null | 2 | 2 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,497 | false |
<!-- 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. -->
# minilm-finetuned-emotion
This model is a fine-tuned version of [microsoft/MiniLM-L12-H384-uncased](https://huggingface.co/microsoft/MiniLM-L12-H384-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3891
- F1: 0.9118
## 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: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.3957 | 1.0 | 250 | 1.0134 | 0.6088 |
| 0.8715 | 2.0 | 500 | 0.6892 | 0.8493 |
| 0.6085 | 3.0 | 750 | 0.4943 | 0.8920 |
| 0.4626 | 4.0 | 1000 | 0.4096 | 0.9078 |
| 0.3961 | 5.0 | 1250 | 0.3891 | 0.9118 |
### Framework versions
- Transformers 4.12.3
- Pytorch 1.6.0
- Datasets 1.15.1
- Tokenizers 0.10.3
|
ccf7d8bdc0b5f95867b768f44722eafa
|
yanaiela/roberta-base-epoch_65
|
yanaiela
|
roberta
| 9 | 2 |
transformers
| 0 |
fill-mask
| true | false | false |
mit
|
['en']
|
['wikipedia', 'bookcorpus']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['roberta-base', 'roberta-base-epoch_65']
| false | true | true | 2,102 | false |
# RoBERTa, Intermediate Checkpoint - Epoch 65
This model is part of our reimplementation of the [RoBERTa model](https://arxiv.org/abs/1907.11692),
trained on Wikipedia and the Book Corpus only.
We train this model for almost 100K steps, corresponding to 83 epochs.
We provide the 84 checkpoints (including the randomly initialized weights before the training)
to provide the ability to study the training dynamics of such models, and other possible use-cases.
These models were trained in part of a work that studies how simple statistics from data,
such as co-occurrences affects model predictions, which are described in the paper
[Measuring Causal Effects of Data Statistics on Language Model's `Factual' Predictions](https://arxiv.org/abs/2207.14251).
This is RoBERTa-base epoch_65.
## Model Description
This model was captured during a reproduction of
[RoBERTa-base](https://huggingface.co/roberta-base), for English: it
is a Transformers model pretrained on a large corpus of English data, using the
Masked Language Modelling (MLM).
The intended uses, limitations, training data and training procedure for the fully trained model are similar
to [RoBERTa-base](https://huggingface.co/roberta-base). Two major
differences with the original model:
* We trained our model for 100K steps, instead of 500K
* We only use Wikipedia and the Book Corpus, as corpora which are publicly available.
### How to use
Using code from
[RoBERTa-base](https://huggingface.co/roberta-base), here is an example based on
PyTorch:
```
from transformers import pipeline
model = pipeline("fill-mask", model='yanaiela/roberta-base-epoch_83', device=-1, top_k=10)
model("Hello, I'm the <mask> RoBERTa-base language model")
```
## Citation info
```bibtex
@article{2207.14251,
Author = {Yanai Elazar and Nora Kassner and Shauli Ravfogel and Amir Feder and Abhilasha Ravichander and Marius Mosbach and Yonatan Belinkov and Hinrich Schütze and Yoav Goldberg},
Title = {Measuring Causal Effects of Data Statistics on Language Model's `Factual' Predictions},
Year = {2022},
Eprint = {arXiv:2207.14251},
}
```
|
c4c808fe4e3d1f5c8d2df9f7a98799b6
|
Eleven/xlm-roberta-base-finetuned-panx-de-fr
|
Eleven
|
xlm-roberta
| 9 | 7 |
transformers
| 0 |
token-classification
| true | false | false |
mit
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,320 | false |
<!-- 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. -->
# xlm-roberta-base-finetuned-panx-de-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1644
- F1: 0.8617
## 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: 24
- eval_batch_size: 24
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2891 | 1.0 | 715 | 0.1780 | 0.8288 |
| 0.1471 | 2.0 | 1430 | 0.1627 | 0.8509 |
| 0.0947 | 3.0 | 2145 | 0.1644 | 0.8617 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
ab5a4b1ebacdf5214a12b276f68e5742
|
Geotrend/bert-base-it-cased
|
Geotrend
|
bert
| 8 | 10 |
transformers
| 0 |
fill-mask
| true | true | true |
apache-2.0
|
['it']
|
['wikipedia']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
[]
| false | true | true | 1,283 | false |
# bert-base-it-cased
We are sharing smaller versions of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) that handle a custom number of languages.
Unlike [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased), our versions give exactly the same representations produced by the original model which preserves the original accuracy.
For more information please visit our paper: [Load What You Need: Smaller Versions of Multilingual BERT](https://www.aclweb.org/anthology/2020.sustainlp-1.16.pdf).
## How to use
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("Geotrend/bert-base-it-cased")
model = AutoModel.from_pretrained("Geotrend/bert-base-it-cased")
```
To generate other smaller versions of multilingual transformers please visit [our Github repo](https://github.com/Geotrend-research/smaller-transformers).
### How to cite
```bibtex
@inproceedings{smallermbert,
title={Load What You Need: Smaller Versions of Mutlilingual BERT},
author={Abdaoui, Amine and Pradel, Camille and Sigel, Grégoire},
booktitle={SustaiNLP / EMNLP},
year={2020}
}
```
## Contact
Please contact amine@geotrend.fr for any question, feedback or request.
|
f8aa11ddfdca868a124c6d152a44adc8
|
nlp04/kobart_64x2_3e-5_datav2_min30_lp5.0_temperature1.0
|
nlp04
|
bart
| 15 | 3 |
transformers
| 0 |
text2text-generation
| true | false | false |
mit
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,098 | false |
<!-- 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. -->
# kobart_64x2_3e-5_datav2_min30_lp5.0_temperature1.0
This model is a fine-tuned version of [gogamza/kobart-base-v2](https://huggingface.co/gogamza/kobart-base-v2) 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: 3e-05
- train_batch_size: 64
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu117
- Datasets 2.7.1
- Tokenizers 0.13.2
|
6efff11387f9ef3d8316efb1a0ea5a11
|
Duskfallcrew/duskfall-tarot-card
|
Duskfallcrew
| null | 21 | 28 |
diffusers
| 0 |
text-to-image
| false | false | false |
creativeml-openrail-m
| null | null | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 |
['text-to-image']
| false | true | true | 840 | false |
### Duskfall Tarot Card Dreambooth model trained by Duskfallcrew with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v1-5 base model
You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb). Don't forget to use the concept prompts!
If you want to donate towards costs and don't want to subscribe:
https://ko-fi.com/DUSKFALLcrew
If you want to monthly support the EARTH & DUSK media projects and not just AI:
https://www.patreon.com/earthndusk
dsktaro1 (use that on your prompt)
|
650eb15e6cbb0a7506c9ab07a14ed434
|
sentence-transformers/xlm-r-distilroberta-base-paraphrase-v1
|
sentence-transformers
|
xlm-roberta
| 13 | 48,680 |
sentence-transformers
| 0 |
sentence-similarity
| true | true | false |
apache-2.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers']
| false | true | true | 3,624 | false |
# sentence-transformers/xlm-r-distilroberta-base-paraphrase-v1
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## 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('sentence-transformers/xlm-r-distilroberta-base-paraphrase-v1')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/xlm-r-distilroberta-base-paraphrase-v1')
model = AutoModel.from_pretrained('sentence-transformers/xlm-r-distilroberta-base-paraphrase-v1')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, max pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/xlm-r-distilroberta-base-paraphrase-v1)
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
This model was trained by [sentence-transformers](https://www.sbert.net/).
If you find this model helpful, feel free to cite our publication [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084):
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "http://arxiv.org/abs/1908.10084",
}
```
|
6542029544970387395cbb52cba276b8
|
henryscheible/eval_stsb
|
henryscheible
|
bert
| 11 | 1 |
transformers
| 0 |
text-classification
| true | false | false |
apache-2.0
|
['en']
|
['glue']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 885 | false |
<!-- 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. -->
# eval_stsb
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the GLUE STSB 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: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1
- Datasets 2.6.1
- Tokenizers 0.13.1
|
0943871084e5612899062b9e045596e8
|
DucHaiten/DucHaitenDreamWorld
|
DucHaiten
| null | 20 | 155 |
diffusers
| 7 |
text-to-image
| false | false | false |
creativeml-openrail-m
|
['en']
| null | null | 0 | 0 | 0 | 0 | 2 | 2 | 0 |
['stable-diffusion', 'text-to-image', 'image-to-image', 'diffusers']
| false | true | true | 3,240 | false |
After many days of not eating well, sleeping 4 hours at night. Finally, version 2.4.1 of the DucHaitenDreamWorld model is also completed, it will be a huge improvement, just looking at the sample image is enough to understand how great it is. At least not as bad as the previous version :)
Dream World is my model for art like Disney, Pixar.
xformer on, no ave (I haven't tried it with vae so I don't know if it's good or bad)
Please support me by becoming a patron:
https://www.patreon.com/duchaitenreal
![00376-1484770875-[uploaded e621], by Pino Daeni, by Ruan Jia, by Fumiko, by Alayna Lemmer, by Carlo Galli Bibiena, solo female ((Vulpix)) with ((.png](https://s3.amazonaws.com/moonup/production/uploads/1676126509917-630b58b279d18d5e53e3a5a9.png)
|
a8ee0fd83cf60c21c57f445aa7319d42
|
gary109/ai-light-dance_singing3_ft_wav2vec2-large-xlsr-53-v2
|
gary109
|
wav2vec2
| 20 | 2 |
transformers
| 0 |
automatic-speech-recognition
| true | false | false |
apache-2.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['automatic-speech-recognition', 'gary109/AI_Light_Dance', 'generated_from_trainer']
| true | true | true | 31,927 | false |
<!-- 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. -->
# ai-light-dance_singing3_ft_wav2vec2-large-xlsr-53-v2
This model is a fine-tuned version of [gary109/ai-light-dance_singing3_ft_wav2vec2-large-xlsr-53-v2](https://huggingface.co/gary109/ai-light-dance_singing3_ft_wav2vec2-large-xlsr-53-v2) on the GARY109/AI_LIGHT_DANCE - ONSET-SINGING3 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4660
- Wer: 0.2274
## 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: 4e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 500.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.4528 | 1.0 | 72 | 0.4860 | 0.2236 |
| 0.4403 | 2.0 | 144 | 0.4814 | 0.2222 |
| 0.4309 | 3.0 | 216 | 0.4952 | 0.2238 |
| 0.4193 | 4.0 | 288 | 0.4864 | 0.2190 |
| 0.427 | 5.0 | 360 | 0.5071 | 0.2261 |
| 0.4342 | 6.0 | 432 | 0.4932 | 0.2218 |
| 0.4205 | 7.0 | 504 | 0.4869 | 0.2222 |
| 0.437 | 8.0 | 576 | 0.5125 | 0.2224 |
| 0.4316 | 9.0 | 648 | 0.5095 | 0.2285 |
| 0.4383 | 10.0 | 720 | 0.5398 | 0.2346 |
| 0.4431 | 11.0 | 792 | 0.5177 | 0.2259 |
| 0.4555 | 12.0 | 864 | 0.5246 | 0.2335 |
| 0.4488 | 13.0 | 936 | 0.5248 | 0.2277 |
| 0.4449 | 14.0 | 1008 | 0.5196 | 0.2254 |
| 0.4629 | 15.0 | 1080 | 0.4933 | 0.2297 |
| 0.4565 | 16.0 | 1152 | 0.5469 | 0.2297 |
| 0.4396 | 17.0 | 1224 | 0.5356 | 0.2439 |
| 0.4452 | 18.0 | 1296 | 0.5298 | 0.2510 |
| 0.4449 | 19.0 | 1368 | 0.5024 | 0.2291 |
| 0.4437 | 20.0 | 1440 | 0.5288 | 0.2374 |
| 0.4572 | 21.0 | 1512 | 0.4954 | 0.2344 |
| 0.4633 | 22.0 | 1584 | 0.5043 | 0.2361 |
| 0.4486 | 23.0 | 1656 | 0.5076 | 0.2250 |
| 0.4386 | 24.0 | 1728 | 0.5564 | 0.2492 |
| 0.4478 | 25.0 | 1800 | 0.5299 | 0.2236 |
| 0.4654 | 26.0 | 1872 | 0.5076 | 0.2276 |
| 0.453 | 27.0 | 1944 | 0.5666 | 0.2395 |
| 0.4474 | 28.0 | 2016 | 0.5026 | 0.2254 |
| 0.4465 | 29.0 | 2088 | 0.5216 | 0.2352 |
| 0.4689 | 30.0 | 2160 | 0.5293 | 0.2370 |
| 0.4467 | 31.0 | 2232 | 0.4856 | 0.2303 |
| 0.4379 | 32.0 | 2304 | 0.5089 | 0.2240 |
| 0.4302 | 33.0 | 2376 | 0.4958 | 0.2173 |
| 0.4417 | 34.0 | 2448 | 0.5392 | 0.2337 |
| 0.4458 | 35.0 | 2520 | 0.5229 | 0.2416 |
| 0.4415 | 36.0 | 2592 | 0.5280 | 0.2344 |
| 0.4621 | 37.0 | 2664 | 0.5362 | 0.2459 |
| 0.44 | 38.0 | 2736 | 0.5071 | 0.2285 |
| 0.4288 | 39.0 | 2808 | 0.5264 | 0.2313 |
| 0.4594 | 40.0 | 2880 | 0.5238 | 0.2306 |
| 0.4428 | 41.0 | 2952 | 0.5375 | 0.2286 |
| 0.4233 | 42.0 | 3024 | 0.5214 | 0.2254 |
| 0.4462 | 43.0 | 3096 | 0.5145 | 0.2450 |
| 0.4282 | 44.0 | 3168 | 0.5519 | 0.2254 |
| 0.454 | 45.0 | 3240 | 0.5401 | 0.2382 |
| 0.4494 | 46.0 | 3312 | 0.5117 | 0.2229 |
| 0.4292 | 47.0 | 3384 | 0.5295 | 0.2352 |
| 0.4321 | 48.0 | 3456 | 0.4953 | 0.2299 |
| 0.4145 | 49.0 | 3528 | 0.5233 | 0.2297 |
| 0.4278 | 50.0 | 3600 | 0.5151 | 0.2258 |
| 0.4395 | 51.0 | 3672 | 0.4660 | 0.2274 |
| 0.4298 | 52.0 | 3744 | 0.5083 | 0.2409 |
| 0.4279 | 53.0 | 3816 | 0.4855 | 0.2219 |
| 0.4164 | 54.0 | 3888 | 0.5074 | 0.2267 |
| 0.4386 | 55.0 | 3960 | 0.5016 | 0.2241 |
| 0.4497 | 56.0 | 4032 | 0.5378 | 0.2305 |
| 0.4267 | 57.0 | 4104 | 0.5199 | 0.2344 |
| 0.4083 | 58.0 | 4176 | 0.5134 | 0.2249 |
| 0.4163 | 59.0 | 4248 | 0.4975 | 0.2316 |
| 0.4271 | 60.0 | 4320 | 0.5298 | 0.2291 |
| 0.43 | 61.0 | 4392 | 0.4991 | 0.2289 |
| 0.437 | 62.0 | 4464 | 0.5154 | 0.2298 |
| 0.415 | 63.0 | 4536 | 0.5167 | 0.2224 |
| 0.4308 | 64.0 | 4608 | 0.5324 | 0.2287 |
| 0.4247 | 65.0 | 4680 | 0.5396 | 0.2224 |
| 0.4076 | 66.0 | 4752 | 0.5354 | 0.2274 |
| 0.4196 | 67.0 | 4824 | 0.5523 | 0.2225 |
| 0.4216 | 68.0 | 4896 | 0.5180 | 0.2166 |
| 0.4132 | 69.0 | 4968 | 0.5111 | 0.2212 |
| 0.4306 | 70.0 | 5040 | 0.5534 | 0.2416 |
| 0.4327 | 71.0 | 5112 | 0.5628 | 0.2473 |
| 0.4301 | 72.0 | 5184 | 0.5216 | 0.2252 |
| 0.4328 | 73.0 | 5256 | 0.5154 | 0.2250 |
| 0.4021 | 74.0 | 5328 | 0.5686 | 0.2245 |
| 0.465 | 75.0 | 5400 | 0.5236 | 0.2419 |
| 0.416 | 76.0 | 5472 | 0.5614 | 0.2365 |
| 0.4337 | 77.0 | 5544 | 0.5275 | 0.2302 |
| 0.4157 | 78.0 | 5616 | 0.5126 | 0.2293 |
| 0.4143 | 79.0 | 5688 | 0.5260 | 0.2376 |
| 0.4174 | 80.0 | 5760 | 0.5254 | 0.2317 |
| 0.4174 | 81.0 | 5832 | 0.4971 | 0.2191 |
| 0.4082 | 82.0 | 5904 | 0.5245 | 0.2320 |
| 0.4263 | 83.0 | 5976 | 0.5692 | 0.2401 |
| 0.4164 | 84.0 | 6048 | 0.5209 | 0.2312 |
| 0.4144 | 85.0 | 6120 | 0.5164 | 0.2340 |
| 0.4189 | 86.0 | 6192 | 0.5545 | 0.2459 |
| 0.4311 | 87.0 | 6264 | 0.5349 | 0.2477 |
| 0.4224 | 88.0 | 6336 | 0.5093 | 0.2375 |
| 0.4069 | 89.0 | 6408 | 0.5664 | 0.2443 |
| 0.4082 | 90.0 | 6480 | 0.5426 | 0.2391 |
| 0.411 | 91.0 | 6552 | 0.5219 | 0.2339 |
| 0.4085 | 92.0 | 6624 | 0.5468 | 0.2360 |
| 0.4012 | 93.0 | 6696 | 0.5514 | 0.2526 |
| 0.3863 | 94.0 | 6768 | 0.5440 | 0.2344 |
| 0.4098 | 95.0 | 6840 | 0.5355 | 0.2362 |
| 0.4136 | 96.0 | 6912 | 0.5400 | 0.2409 |
| 0.4066 | 97.0 | 6984 | 0.5117 | 0.2313 |
| 0.4131 | 98.0 | 7056 | 0.5365 | 0.2375 |
| 0.3852 | 99.0 | 7128 | 0.5172 | 0.2326 |
| 0.3935 | 100.0 | 7200 | 0.5085 | 0.2296 |
| 0.4093 | 101.0 | 7272 | 0.5650 | 0.2525 |
| 0.3938 | 102.0 | 7344 | 0.5246 | 0.2324 |
| 0.4016 | 103.0 | 7416 | 0.5084 | 0.2292 |
| 0.412 | 104.0 | 7488 | 0.5308 | 0.2211 |
| 0.3903 | 105.0 | 7560 | 0.5047 | 0.2201 |
| 0.396 | 106.0 | 7632 | 0.5302 | 0.2223 |
| 0.3891 | 107.0 | 7704 | 0.5367 | 0.2222 |
| 0.3886 | 108.0 | 7776 | 0.5459 | 0.2328 |
| 0.379 | 109.0 | 7848 | 0.5486 | 0.2340 |
| 0.4009 | 110.0 | 7920 | 0.5080 | 0.2186 |
| 0.3967 | 111.0 | 7992 | 0.5389 | 0.2193 |
| 0.3988 | 112.0 | 8064 | 0.5488 | 0.2281 |
| 0.3952 | 113.0 | 8136 | 0.5409 | 0.2294 |
| 0.3884 | 114.0 | 8208 | 0.5304 | 0.2326 |
| 0.3939 | 115.0 | 8280 | 0.5542 | 0.2211 |
| 0.3927 | 116.0 | 8352 | 0.5676 | 0.2259 |
| 0.3944 | 117.0 | 8424 | 0.5221 | 0.2210 |
| 0.3941 | 118.0 | 8496 | 0.5474 | 0.2247 |
| 0.3912 | 119.0 | 8568 | 0.5451 | 0.2185 |
| 0.4209 | 120.0 | 8640 | 0.5282 | 0.2282 |
| 0.3882 | 121.0 | 8712 | 0.5263 | 0.2184 |
| 0.3891 | 122.0 | 8784 | 0.5301 | 0.2194 |
| 0.3964 | 123.0 | 8856 | 0.5608 | 0.2220 |
| 0.3918 | 124.0 | 8928 | 0.5233 | 0.2230 |
| 0.3834 | 125.0 | 9000 | 0.5286 | 0.2195 |
| 0.3952 | 126.0 | 9072 | 0.5410 | 0.2258 |
| 0.3812 | 127.0 | 9144 | 0.5183 | 0.2207 |
| 0.3904 | 128.0 | 9216 | 0.5393 | 0.2244 |
| 0.3797 | 129.0 | 9288 | 0.5213 | 0.2226 |
| 0.3802 | 130.0 | 9360 | 0.5470 | 0.2207 |
| 0.4097 | 131.0 | 9432 | 0.5206 | 0.2254 |
| 0.3771 | 132.0 | 9504 | 0.5075 | 0.2182 |
| 0.3732 | 133.0 | 9576 | 0.5153 | 0.2255 |
| 0.3727 | 134.0 | 9648 | 0.5107 | 0.2212 |
| 0.3751 | 135.0 | 9720 | 0.5147 | 0.2259 |
| 0.3858 | 136.0 | 9792 | 0.5519 | 0.2220 |
| 0.3889 | 137.0 | 9864 | 0.5606 | 0.2222 |
| 0.3916 | 138.0 | 9936 | 0.5401 | 0.2252 |
| 0.3775 | 139.0 | 10008 | 0.5393 | 0.2269 |
| 0.3963 | 140.0 | 10080 | 0.5504 | 0.2322 |
| 0.3941 | 141.0 | 10152 | 0.5338 | 0.2342 |
| 0.3801 | 142.0 | 10224 | 0.5115 | 0.2276 |
| 0.3809 | 143.0 | 10296 | 0.4966 | 0.2261 |
| 0.3751 | 144.0 | 10368 | 0.4910 | 0.2240 |
| 0.3827 | 145.0 | 10440 | 0.5291 | 0.2204 |
| 0.384 | 146.0 | 10512 | 0.5702 | 0.2278 |
| 0.3728 | 147.0 | 10584 | 0.5340 | 0.2283 |
| 0.3963 | 148.0 | 10656 | 0.5513 | 0.2286 |
| 0.3802 | 149.0 | 10728 | 0.5424 | 0.2264 |
| 0.3874 | 150.0 | 10800 | 0.5219 | 0.2200 |
| 0.3743 | 151.0 | 10872 | 0.5147 | 0.2161 |
| 0.3931 | 152.0 | 10944 | 0.5318 | 0.2324 |
| 0.3755 | 153.0 | 11016 | 0.5457 | 0.2252 |
| 0.3744 | 154.0 | 11088 | 0.5448 | 0.2260 |
| 0.3799 | 155.0 | 11160 | 0.5276 | 0.2171 |
| 0.3953 | 156.0 | 11232 | 0.5546 | 0.2263 |
| 0.3716 | 157.0 | 11304 | 0.5110 | 0.2246 |
| 0.3725 | 158.0 | 11376 | 0.5385 | 0.2193 |
| 0.364 | 159.0 | 11448 | 0.5114 | 0.2216 |
| 0.3666 | 160.0 | 11520 | 0.5584 | 0.2248 |
| 0.3797 | 161.0 | 11592 | 0.5313 | 0.2238 |
| 0.3704 | 162.0 | 11664 | 0.5542 | 0.2281 |
| 0.362 | 163.0 | 11736 | 0.5674 | 0.2241 |
| 0.3551 | 164.0 | 11808 | 0.5484 | 0.2210 |
| 0.3765 | 165.0 | 11880 | 0.5380 | 0.2252 |
| 0.3821 | 166.0 | 11952 | 0.5441 | 0.2267 |
| 0.3608 | 167.0 | 12024 | 0.4983 | 0.2186 |
| 0.3595 | 168.0 | 12096 | 0.5065 | 0.2166 |
| 0.3652 | 169.0 | 12168 | 0.5211 | 0.2150 |
| 0.3635 | 170.0 | 12240 | 0.5341 | 0.2164 |
| 0.3614 | 171.0 | 12312 | 0.5059 | 0.2183 |
| 0.3522 | 172.0 | 12384 | 0.5530 | 0.2199 |
| 0.3522 | 173.0 | 12456 | 0.5581 | 0.2142 |
| 0.3503 | 174.0 | 12528 | 0.5394 | 0.2211 |
| 0.3583 | 175.0 | 12600 | 0.5460 | 0.2252 |
| 0.3562 | 176.0 | 12672 | 0.5199 | 0.2223 |
| 0.351 | 177.0 | 12744 | 0.5248 | 0.2146 |
| 0.3667 | 178.0 | 12816 | 0.5400 | 0.2169 |
| 0.3407 | 179.0 | 12888 | 0.5349 | 0.2095 |
| 0.3563 | 180.0 | 12960 | 0.5259 | 0.2116 |
| 0.3656 | 181.0 | 13032 | 0.5130 | 0.2115 |
| 0.3714 | 182.0 | 13104 | 0.5071 | 0.2151 |
| 0.3565 | 183.0 | 13176 | 0.5419 | 0.2205 |
| 0.3521 | 184.0 | 13248 | 0.5380 | 0.2250 |
| 0.3605 | 185.0 | 13320 | 0.5437 | 0.2230 |
| 0.3508 | 186.0 | 13392 | 0.5391 | 0.2225 |
| 0.3746 | 187.0 | 13464 | 0.5426 | 0.2274 |
| 0.3478 | 188.0 | 13536 | 0.5824 | 0.2247 |
| 0.3475 | 189.0 | 13608 | 0.5233 | 0.2103 |
| 0.3676 | 190.0 | 13680 | 0.5214 | 0.2122 |
| 0.3579 | 191.0 | 13752 | 0.5267 | 0.2124 |
| 0.3563 | 192.0 | 13824 | 0.5343 | 0.2132 |
| 0.3531 | 193.0 | 13896 | 0.5205 | 0.2205 |
| 0.3424 | 194.0 | 13968 | 0.5196 | 0.2196 |
| 0.3617 | 195.0 | 14040 | 0.5302 | 0.2222 |
| 0.3461 | 196.0 | 14112 | 0.5366 | 0.2204 |
| 0.3524 | 197.0 | 14184 | 0.5383 | 0.2212 |
| 0.3354 | 198.0 | 14256 | 0.5279 | 0.2166 |
| 0.3501 | 199.0 | 14328 | 0.5235 | 0.2165 |
| 0.3384 | 200.0 | 14400 | 0.5330 | 0.2152 |
| 0.3565 | 201.0 | 14472 | 0.5262 | 0.2211 |
| 0.3385 | 202.0 | 14544 | 0.5404 | 0.2173 |
| 0.3533 | 203.0 | 14616 | 0.5465 | 0.2209 |
| 0.3503 | 204.0 | 14688 | 0.5243 | 0.2223 |
| 0.3529 | 205.0 | 14760 | 0.5611 | 0.2276 |
| 0.3555 | 206.0 | 14832 | 0.5437 | 0.2209 |
| 0.3548 | 207.0 | 14904 | 0.5401 | 0.2249 |
| 0.3417 | 208.0 | 14976 | 0.5643 | 0.2304 |
| 0.3271 | 209.0 | 15048 | 0.5356 | 0.2183 |
| 0.344 | 210.0 | 15120 | 0.5300 | 0.2173 |
| 0.3416 | 211.0 | 15192 | 0.5343 | 0.2169 |
| 0.3393 | 212.0 | 15264 | 0.5677 | 0.2206 |
| 0.3356 | 213.0 | 15336 | 0.5514 | 0.2194 |
| 0.3344 | 214.0 | 15408 | 0.5527 | 0.2198 |
| 0.3303 | 215.0 | 15480 | 0.5590 | 0.2146 |
| 0.3503 | 216.0 | 15552 | 0.5681 | 0.2242 |
| 0.339 | 217.0 | 15624 | 0.5318 | 0.2186 |
| 0.3361 | 218.0 | 15696 | 0.5369 | 0.2247 |
| 0.334 | 219.0 | 15768 | 0.5173 | 0.2152 |
| 0.3222 | 220.0 | 15840 | 0.5965 | 0.2236 |
| 0.3247 | 221.0 | 15912 | 0.5543 | 0.2165 |
| 0.338 | 222.0 | 15984 | 0.5836 | 0.2178 |
| 0.3112 | 223.0 | 16056 | 0.5573 | 0.2171 |
| 0.3203 | 224.0 | 16128 | 0.5830 | 0.2196 |
| 0.3294 | 225.0 | 16200 | 0.5815 | 0.2198 |
| 0.3392 | 226.0 | 16272 | 0.5641 | 0.2163 |
| 0.3332 | 227.0 | 16344 | 0.5770 | 0.2204 |
| 0.3365 | 228.0 | 16416 | 0.5843 | 0.2181 |
| 0.3186 | 229.0 | 16488 | 0.5835 | 0.2231 |
| 0.3329 | 230.0 | 16560 | 0.5867 | 0.2220 |
| 0.3257 | 231.0 | 16632 | 0.6081 | 0.2196 |
| 0.3183 | 232.0 | 16704 | 0.5944 | 0.2220 |
| 0.3315 | 233.0 | 16776 | 0.6060 | 0.2222 |
| 0.3269 | 234.0 | 16848 | 0.6268 | 0.2260 |
| 0.3191 | 235.0 | 16920 | 0.5796 | 0.2183 |
| 0.3395 | 236.0 | 16992 | 0.6140 | 0.2257 |
| 0.3186 | 237.0 | 17064 | 0.6302 | 0.2277 |
| 0.3264 | 238.0 | 17136 | 0.5752 | 0.2194 |
| 0.3181 | 239.0 | 17208 | 0.6066 | 0.2196 |
| 0.3201 | 240.0 | 17280 | 0.6013 | 0.2223 |
| 0.3242 | 241.0 | 17352 | 0.5960 | 0.2207 |
| 0.3194 | 242.0 | 17424 | 0.6093 | 0.2311 |
| 0.3203 | 243.0 | 17496 | 0.6047 | 0.2281 |
| 0.3173 | 244.0 | 17568 | 0.6260 | 0.2285 |
| 0.3118 | 245.0 | 17640 | 0.5961 | 0.2243 |
| 0.3172 | 246.0 | 17712 | 0.6315 | 0.2242 |
| 0.332 | 247.0 | 17784 | 0.6413 | 0.2250 |
| 0.3315 | 248.0 | 17856 | 0.6260 | 0.2290 |
| 0.3222 | 249.0 | 17928 | 0.6175 | 0.2307 |
| 0.3291 | 250.0 | 18000 | 0.6005 | 0.2283 |
| 0.3321 | 251.0 | 18072 | 0.6299 | 0.2311 |
| 0.3338 | 252.0 | 18144 | 0.6011 | 0.2310 |
| 0.3274 | 253.0 | 18216 | 0.5662 | 0.2203 |
| 0.3148 | 254.0 | 18288 | 0.6139 | 0.2344 |
| 0.3295 | 255.0 | 18360 | 0.6183 | 0.2461 |
| 0.3169 | 256.0 | 18432 | 0.6136 | 0.2283 |
| 0.3431 | 257.0 | 18504 | 0.6445 | 0.2446 |
| 0.3209 | 258.0 | 18576 | 0.6124 | 0.2437 |
| 0.3405 | 259.0 | 18648 | 0.6210 | 0.2446 |
| 0.3317 | 260.0 | 18720 | 0.6088 | 0.2350 |
| 0.3265 | 261.0 | 18792 | 0.5792 | 0.2324 |
| 0.332 | 262.0 | 18864 | 0.6326 | 0.2427 |
| 0.3179 | 263.0 | 18936 | 0.6174 | 0.2256 |
| 0.3119 | 264.0 | 19008 | 0.6338 | 0.2277 |
| 0.3223 | 265.0 | 19080 | 0.6236 | 0.2213 |
| 0.315 | 266.0 | 19152 | 0.6025 | 0.2263 |
| 0.3214 | 267.0 | 19224 | 0.5881 | 0.2243 |
| 0.3184 | 268.0 | 19296 | 0.5942 | 0.2225 |
| 0.3083 | 269.0 | 19368 | 0.5836 | 0.2209 |
| 0.3098 | 270.0 | 19440 | 0.5844 | 0.2192 |
| 0.2992 | 271.0 | 19512 | 0.5972 | 0.2218 |
| 0.3118 | 272.0 | 19584 | 0.5768 | 0.2220 |
| 0.3112 | 273.0 | 19656 | 0.5926 | 0.2167 |
| 0.2994 | 274.0 | 19728 | 0.6056 | 0.2227 |
| 0.3041 | 275.0 | 19800 | 0.5793 | 0.2245 |
| 0.3072 | 276.0 | 19872 | 0.6188 | 0.2277 |
| 0.3042 | 277.0 | 19944 | 0.5931 | 0.2251 |
| 0.3107 | 278.0 | 20016 | 0.6205 | 0.2216 |
| 0.3077 | 279.0 | 20088 | 0.6001 | 0.2209 |
| 0.2903 | 280.0 | 20160 | 0.6002 | 0.2141 |
| 0.3124 | 281.0 | 20232 | 0.5782 | 0.2168 |
| 0.3043 | 282.0 | 20304 | 0.6105 | 0.2187 |
| 0.3007 | 283.0 | 20376 | 0.6105 | 0.2213 |
| 0.3023 | 284.0 | 20448 | 0.6011 | 0.2232 |
| 0.3062 | 285.0 | 20520 | 0.5967 | 0.2195 |
| 0.3093 | 286.0 | 20592 | 0.6571 | 0.2258 |
| 0.3041 | 287.0 | 20664 | 0.5956 | 0.2213 |
| 0.3083 | 288.0 | 20736 | 0.5904 | 0.2253 |
| 0.3037 | 289.0 | 20808 | 0.6096 | 0.2295 |
| 0.3064 | 290.0 | 20880 | 0.5958 | 0.2232 |
| 0.3136 | 291.0 | 20952 | 0.6134 | 0.2250 |
| 0.3042 | 292.0 | 21024 | 0.6144 | 0.2189 |
| 0.2967 | 293.0 | 21096 | 0.6086 | 0.2282 |
| 0.2952 | 294.0 | 21168 | 0.6178 | 0.2285 |
| 0.301 | 295.0 | 21240 | 0.5924 | 0.2189 |
| 0.3058 | 296.0 | 21312 | 0.6032 | 0.2193 |
| 0.2983 | 297.0 | 21384 | 0.5823 | 0.2183 |
| 0.2793 | 298.0 | 21456 | 0.5930 | 0.2195 |
| 0.2936 | 299.0 | 21528 | 0.6166 | 0.2215 |
| 0.298 | 300.0 | 21600 | 0.5864 | 0.2159 |
| 0.2949 | 301.0 | 21672 | 0.6049 | 0.2160 |
| 0.2948 | 302.0 | 21744 | 0.5745 | 0.2173 |
| 0.2809 | 303.0 | 21816 | 0.5699 | 0.2173 |
| 0.2854 | 304.0 | 21888 | 0.5894 | 0.2243 |
| 0.2908 | 305.0 | 21960 | 0.6123 | 0.2229 |
| 0.2948 | 306.0 | 22032 | 0.5966 | 0.2162 |
| 0.2997 | 307.0 | 22104 | 0.6030 | 0.2180 |
| 0.2906 | 308.0 | 22176 | 0.5920 | 0.2185 |
| 0.2778 | 309.0 | 22248 | 0.5913 | 0.2121 |
| 0.281 | 310.0 | 22320 | 0.6020 | 0.2121 |
| 0.2852 | 311.0 | 22392 | 0.5814 | 0.2170 |
| 0.278 | 312.0 | 22464 | 0.5931 | 0.2151 |
| 0.2743 | 313.0 | 22536 | 0.6073 | 0.2179 |
| 0.2757 | 314.0 | 22608 | 0.6174 | 0.2153 |
| 0.2907 | 315.0 | 22680 | 0.5729 | 0.2171 |
| 0.2801 | 316.0 | 22752 | 0.6014 | 0.2214 |
| 0.2908 | 317.0 | 22824 | 0.6098 | 0.2130 |
| 0.2824 | 318.0 | 22896 | 0.5942 | 0.2191 |
| 0.2799 | 319.0 | 22968 | 0.6374 | 0.2230 |
| 0.2725 | 320.0 | 23040 | 0.6424 | 0.2206 |
| 0.2821 | 321.0 | 23112 | 0.6465 | 0.2203 |
| 0.2795 | 322.0 | 23184 | 0.6163 | 0.2182 |
| 0.2764 | 323.0 | 23256 | 0.6257 | 0.2209 |
| 0.2739 | 324.0 | 23328 | 0.6374 | 0.2194 |
| 0.2712 | 325.0 | 23400 | 0.6228 | 0.2166 |
| 0.275 | 326.0 | 23472 | 0.6394 | 0.2214 |
| 0.275 | 327.0 | 23544 | 0.6359 | 0.2213 |
| 0.2702 | 328.0 | 23616 | 0.6430 | 0.2207 |
| 0.2676 | 329.0 | 23688 | 0.6321 | 0.2145 |
| 0.2735 | 330.0 | 23760 | 0.6583 | 0.2168 |
| 0.2815 | 331.0 | 23832 | 0.6368 | 0.2178 |
| 0.2823 | 332.0 | 23904 | 0.6373 | 0.2197 |
| 0.2885 | 333.0 | 23976 | 0.6352 | 0.2200 |
| 0.2751 | 334.0 | 24048 | 0.6431 | 0.2159 |
| 0.2717 | 335.0 | 24120 | 0.6339 | 0.2213 |
| 0.286 | 336.0 | 24192 | 0.6566 | 0.2245 |
| 0.2678 | 337.0 | 24264 | 0.6443 | 0.2194 |
| 0.2692 | 338.0 | 24336 | 0.6352 | 0.2225 |
| 0.273 | 339.0 | 24408 | 0.6497 | 0.2187 |
| 0.2686 | 340.0 | 24480 | 0.6788 | 0.2214 |
| 0.2699 | 341.0 | 24552 | 0.6615 | 0.2198 |
| 0.2636 | 342.0 | 24624 | 0.6765 | 0.2196 |
| 0.2545 | 343.0 | 24696 | 0.6737 | 0.2202 |
| 0.2612 | 344.0 | 24768 | 0.6891 | 0.2240 |
| 0.2705 | 345.0 | 24840 | 0.6550 | 0.2204 |
| 0.2658 | 346.0 | 24912 | 0.6591 | 0.2200 |
| 0.2701 | 347.0 | 24984 | 0.6222 | 0.2216 |
| 0.2743 | 348.0 | 25056 | 0.6263 | 0.2186 |
| 0.2657 | 349.0 | 25128 | 0.6509 | 0.2186 |
| 0.2635 | 350.0 | 25200 | 0.6570 | 0.2207 |
| 0.2601 | 351.0 | 25272 | 0.6496 | 0.2155 |
| 0.2695 | 352.0 | 25344 | 0.6305 | 0.2169 |
| 0.2586 | 353.0 | 25416 | 0.6269 | 0.2223 |
| 0.2529 | 354.0 | 25488 | 0.6418 | 0.2204 |
| 0.2739 | 355.0 | 25560 | 0.6472 | 0.2175 |
| 0.2738 | 356.0 | 25632 | 0.6416 | 0.2187 |
| 0.2775 | 357.0 | 25704 | 0.6470 | 0.2208 |
| 0.2775 | 358.0 | 25776 | 0.6483 | 0.2201 |
| 0.2622 | 359.0 | 25848 | 0.6233 | 0.2164 |
| 0.2727 | 360.0 | 25920 | 0.6438 | 0.2178 |
| 0.275 | 361.0 | 25992 | 0.6459 | 0.2222 |
| 0.2688 | 362.0 | 26064 | 0.6329 | 0.2188 |
| 0.2658 | 363.0 | 26136 | 0.6482 | 0.2207 |
| 0.2693 | 364.0 | 26208 | 0.6337 | 0.2194 |
| 0.2599 | 365.0 | 26280 | 0.6458 | 0.2189 |
| 0.2683 | 366.0 | 26352 | 0.6483 | 0.2213 |
| 0.2665 | 367.0 | 26424 | 0.6576 | 0.2203 |
| 0.2529 | 368.0 | 26496 | 0.6629 | 0.2200 |
| 0.2536 | 369.0 | 26568 | 0.6665 | 0.2208 |
| 0.2562 | 370.0 | 26640 | 0.6545 | 0.2171 |
| 0.2713 | 371.0 | 26712 | 0.6433 | 0.2231 |
| 0.2545 | 372.0 | 26784 | 0.6330 | 0.2202 |
| 0.2513 | 373.0 | 26856 | 0.6474 | 0.2154 |
| 0.2564 | 374.0 | 26928 | 0.6519 | 0.2191 |
| 0.266 | 375.0 | 27000 | 0.6577 | 0.2199 |
| 0.2623 | 376.0 | 27072 | 0.6508 | 0.2187 |
| 0.2666 | 377.0 | 27144 | 0.6358 | 0.2171 |
| 0.2503 | 378.0 | 27216 | 0.6515 | 0.2195 |
| 0.252 | 379.0 | 27288 | 0.6479 | 0.2221 |
| 0.2558 | 380.0 | 27360 | 0.6344 | 0.2203 |
| 0.2673 | 381.0 | 27432 | 0.6717 | 0.2196 |
| 0.2615 | 382.0 | 27504 | 0.6393 | 0.2178 |
| 0.2603 | 383.0 | 27576 | 0.6375 | 0.2167 |
| 0.2522 | 384.0 | 27648 | 0.6381 | 0.2195 |
| 0.2532 | 385.0 | 27720 | 0.6566 | 0.2209 |
| 0.2544 | 386.0 | 27792 | 0.6640 | 0.2231 |
| 0.2529 | 387.0 | 27864 | 0.6531 | 0.2207 |
| 0.2578 | 388.0 | 27936 | 0.6915 | 0.2202 |
| 0.2517 | 389.0 | 28008 | 0.6902 | 0.2238 |
| 0.2453 | 390.0 | 28080 | 0.6727 | 0.2249 |
| 0.2634 | 391.0 | 28152 | 0.6667 | 0.2235 |
| 0.2515 | 392.0 | 28224 | 0.6554 | 0.2212 |
| 0.249 | 393.0 | 28296 | 0.6672 | 0.2214 |
| 0.2524 | 394.0 | 28368 | 0.6693 | 0.2164 |
| 0.2529 | 395.0 | 28440 | 0.6572 | 0.2186 |
| 0.256 | 396.0 | 28512 | 0.6420 | 0.2171 |
| 0.2498 | 397.0 | 28584 | 0.6712 | 0.2168 |
| 0.2565 | 398.0 | 28656 | 0.6890 | 0.2175 |
| 0.2477 | 399.0 | 28728 | 0.6905 | 0.2185 |
| 0.2486 | 400.0 | 28800 | 0.7010 | 0.2191 |
| 0.259 | 401.0 | 28872 | 0.6983 | 0.2169 |
| 0.2555 | 402.0 | 28944 | 0.6877 | 0.2189 |
| 0.2579 | 403.0 | 29016 | 0.6864 | 0.2188 |
| 0.2421 | 404.0 | 29088 | 0.6603 | 0.2175 |
| 0.2531 | 405.0 | 29160 | 0.6882 | 0.2223 |
| 0.254 | 406.0 | 29232 | 0.6813 | 0.2209 |
| 0.2517 | 407.0 | 29304 | 0.6707 | 0.2205 |
| 0.2521 | 408.0 | 29376 | 0.6835 | 0.2234 |
| 0.2494 | 409.0 | 29448 | 0.6896 | 0.2216 |
| 0.2516 | 410.0 | 29520 | 0.6760 | 0.2218 |
| 0.2605 | 411.0 | 29592 | 0.7055 | 0.2207 |
| 0.2514 | 412.0 | 29664 | 0.6707 | 0.2232 |
| 0.242 | 413.0 | 29736 | 0.6853 | 0.2183 |
| 0.2505 | 414.0 | 29808 | 0.6869 | 0.2232 |
| 0.2398 | 415.0 | 29880 | 0.6732 | 0.2228 |
| 0.2549 | 416.0 | 29952 | 0.6559 | 0.2222 |
| 0.2496 | 417.0 | 30024 | 0.6675 | 0.2232 |
| 0.2538 | 418.0 | 30096 | 0.6695 | 0.2240 |
| 0.246 | 419.0 | 30168 | 0.6917 | 0.2268 |
| 0.2462 | 420.0 | 30240 | 0.6842 | 0.2288 |
| 0.2527 | 421.0 | 30312 | 0.6628 | 0.2207 |
| 0.2469 | 422.0 | 30384 | 0.6683 | 0.2225 |
| 0.2493 | 423.0 | 30456 | 0.6632 | 0.2189 |
| 0.239 | 424.0 | 30528 | 0.6848 | 0.2198 |
| 0.2373 | 425.0 | 30600 | 0.6834 | 0.2223 |
| 0.245 | 426.0 | 30672 | 0.6902 | 0.2251 |
| 0.239 | 427.0 | 30744 | 0.6917 | 0.2223 |
| 0.2441 | 428.0 | 30816 | 0.6859 | 0.2232 |
| 0.2306 | 429.0 | 30888 | 0.6844 | 0.2208 |
| 0.2373 | 430.0 | 30960 | 0.6740 | 0.2185 |
| 0.2495 | 431.0 | 31032 | 0.6823 | 0.2214 |
| 0.2457 | 432.0 | 31104 | 0.6686 | 0.2219 |
| 0.2474 | 433.0 | 31176 | 0.6856 | 0.2215 |
| 0.2434 | 434.0 | 31248 | 0.6876 | 0.2199 |
| 0.2377 | 435.0 | 31320 | 0.6827 | 0.2234 |
| 0.2566 | 436.0 | 31392 | 0.6920 | 0.2213 |
| 0.2384 | 437.0 | 31464 | 0.6734 | 0.2234 |
| 0.2477 | 438.0 | 31536 | 0.6992 | 0.2242 |
| 0.2347 | 439.0 | 31608 | 0.6837 | 0.2217 |
| 0.2345 | 440.0 | 31680 | 0.6852 | 0.2222 |
| 0.2457 | 441.0 | 31752 | 0.6891 | 0.2230 |
| 0.2512 | 442.0 | 31824 | 0.6976 | 0.2263 |
| 0.25 | 443.0 | 31896 | 0.6889 | 0.2232 |
| 0.2341 | 444.0 | 31968 | 0.6841 | 0.2266 |
| 0.252 | 445.0 | 32040 | 0.6981 | 0.2249 |
| 0.2486 | 446.0 | 32112 | 0.6958 | 0.2281 |
| 0.2402 | 447.0 | 32184 | 0.6826 | 0.2249 |
| 0.2477 | 448.0 | 32256 | 0.6867 | 0.2247 |
| 0.2304 | 449.0 | 32328 | 0.7022 | 0.2243 |
| 0.2376 | 450.0 | 32400 | 0.6948 | 0.2222 |
| 0.2388 | 451.0 | 32472 | 0.6771 | 0.2221 |
| 0.2544 | 452.0 | 32544 | 0.6841 | 0.2249 |
| 0.2428 | 453.0 | 32616 | 0.6886 | 0.2220 |
| 0.2438 | 454.0 | 32688 | 0.6903 | 0.2214 |
| 0.2463 | 455.0 | 32760 | 0.6781 | 0.2219 |
| 0.2355 | 456.0 | 32832 | 0.6784 | 0.2198 |
| 0.237 | 457.0 | 32904 | 0.6849 | 0.2231 |
| 0.2381 | 458.0 | 32976 | 0.6892 | 0.2220 |
| 0.23 | 459.0 | 33048 | 0.6782 | 0.2207 |
| 0.2359 | 460.0 | 33120 | 0.6789 | 0.2238 |
| 0.2382 | 461.0 | 33192 | 0.6829 | 0.2236 |
| 0.2438 | 462.0 | 33264 | 0.6928 | 0.2236 |
| 0.233 | 463.0 | 33336 | 0.6860 | 0.2216 |
| 0.2358 | 464.0 | 33408 | 0.6857 | 0.2236 |
| 0.2226 | 465.0 | 33480 | 0.6818 | 0.2202 |
| 0.2478 | 466.0 | 33552 | 0.6801 | 0.2222 |
| 0.2274 | 467.0 | 33624 | 0.6797 | 0.2203 |
| 0.2339 | 468.0 | 33696 | 0.6915 | 0.2224 |
| 0.2259 | 469.0 | 33768 | 0.6919 | 0.2220 |
| 0.2327 | 470.0 | 33840 | 0.6877 | 0.2225 |
| 0.2341 | 471.0 | 33912 | 0.6892 | 0.2235 |
| 0.2502 | 472.0 | 33984 | 0.6900 | 0.2227 |
| 0.234 | 473.0 | 34056 | 0.6839 | 0.2242 |
| 0.2289 | 474.0 | 34128 | 0.6885 | 0.2243 |
| 0.2311 | 475.0 | 34200 | 0.6911 | 0.2231 |
| 0.2374 | 476.0 | 34272 | 0.6834 | 0.2234 |
| 0.235 | 477.0 | 34344 | 0.6790 | 0.2223 |
| 0.2292 | 478.0 | 34416 | 0.6857 | 0.2233 |
| 0.2243 | 479.0 | 34488 | 0.6737 | 0.2243 |
| 0.235 | 480.0 | 34560 | 0.6831 | 0.2222 |
| 0.2337 | 481.0 | 34632 | 0.6769 | 0.2207 |
| 0.2258 | 482.0 | 34704 | 0.6784 | 0.2232 |
| 0.2276 | 483.0 | 34776 | 0.6917 | 0.2241 |
| 0.2379 | 484.0 | 34848 | 0.6806 | 0.2251 |
| 0.229 | 485.0 | 34920 | 0.6859 | 0.2232 |
| 0.2312 | 486.0 | 34992 | 0.6850 | 0.2236 |
| 0.2412 | 487.0 | 35064 | 0.6776 | 0.2221 |
| 0.2328 | 488.0 | 35136 | 0.6835 | 0.2230 |
| 0.2373 | 489.0 | 35208 | 0.6879 | 0.2222 |
| 0.234 | 490.0 | 35280 | 0.6868 | 0.2214 |
| 0.2274 | 491.0 | 35352 | 0.6869 | 0.2222 |
| 0.2332 | 492.0 | 35424 | 0.6861 | 0.2214 |
| 0.2291 | 493.0 | 35496 | 0.6881 | 0.2206 |
| 0.2301 | 494.0 | 35568 | 0.6877 | 0.2205 |
| 0.2258 | 495.0 | 35640 | 0.6898 | 0.2203 |
| 0.2351 | 496.0 | 35712 | 0.6883 | 0.2212 |
| 0.2345 | 497.0 | 35784 | 0.6915 | 0.2213 |
| 0.23 | 498.0 | 35856 | 0.6922 | 0.2217 |
| 0.2257 | 499.0 | 35928 | 0.6925 | 0.2216 |
| 0.2273 | 500.0 | 36000 | 0.6914 | 0.2205 |
### Framework versions
- Transformers 4.21.0.dev0
- Pytorch 1.9.1+cu102
- Datasets 2.3.3.dev0
- Tokenizers 0.12.1
|
0f32bfd50c481f5b1be0cf5d16f746a2
|
pinot/wav2vec2-large-xls-r-300m-j-phoneme-colab-3
|
pinot
|
wav2vec2
| 12 | 7 |
transformers
| 0 |
automatic-speech-recognition
| true | false | false |
apache-2.0
| null |
['common_voice_10_0']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 3,760 | false |
<!-- 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. -->
# wav2vec2-large-xls-r-300m-j-phoneme-colab-3
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice_10_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6478
- Wer: 0.3336
## 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: 0.0003
- train_batch_size: 2
- eval_batch_size: 8
- 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: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 40
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| No log | 1.0 | 397 | 1.0586 | 0.9425 |
| No log | 2.0 | 794 | 0.5773 | 0.5847 |
| 1.9827 | 3.0 | 1191 | 0.5243 | 0.4882 |
| 1.9827 | 4.0 | 1588 | 0.4735 | 0.4624 |
| 1.9827 | 5.0 | 1985 | 0.4967 | 0.4789 |
| 0.6004 | 6.0 | 2382 | 0.4703 | 0.4246 |
| 0.6004 | 7.0 | 2779 | 0.4555 | 0.4194 |
| 0.4911 | 8.0 | 3176 | 0.4692 | 0.4284 |
| 0.4911 | 9.0 | 3573 | 0.4589 | 0.3997 |
| 0.4911 | 10.0 | 3970 | 0.4988 | 0.4286 |
| 0.4275 | 11.0 | 4367 | 0.4851 | 0.4153 |
| 0.4275 | 12.0 | 4764 | 0.5020 | 0.4039 |
| 0.3784 | 13.0 | 5161 | 0.5491 | 0.4169 |
| 0.3784 | 14.0 | 5558 | 0.5211 | 0.4080 |
| 0.3784 | 15.0 | 5955 | 0.5124 | 0.3950 |
| 0.3362 | 16.0 | 6352 | 0.5121 | 0.3909 |
| 0.3362 | 17.0 | 6749 | 0.5503 | 0.3728 |
| 0.3046 | 18.0 | 7146 | 0.5363 | 0.3915 |
| 0.3046 | 19.0 | 7543 | 0.6112 | 0.4076 |
| 0.3046 | 20.0 | 7940 | 0.5884 | 0.3755 |
| 0.2785 | 21.0 | 8337 | 0.5639 | 0.3793 |
| 0.2785 | 22.0 | 8734 | 0.6246 | 0.3742 |
| 0.2513 | 23.0 | 9131 | 0.6014 | 0.3714 |
| 0.2513 | 24.0 | 9528 | 0.6195 | 0.3697 |
| 0.2513 | 25.0 | 9925 | 0.6004 | 0.3729 |
| 0.2296 | 26.0 | 10322 | 0.5793 | 0.3585 |
| 0.2296 | 27.0 | 10719 | 0.6178 | 0.3628 |
| 0.2114 | 28.0 | 11116 | 0.5974 | 0.3507 |
| 0.2114 | 29.0 | 11513 | 0.6056 | 0.3432 |
| 0.2114 | 30.0 | 11910 | 0.6190 | 0.3536 |
| 0.1944 | 31.0 | 12307 | 0.6293 | 0.3550 |
| 0.1944 | 32.0 | 12704 | 0.6236 | 0.3535 |
| 0.1777 | 33.0 | 13101 | 0.6456 | 0.3503 |
| 0.1777 | 34.0 | 13498 | 0.6629 | 0.3444 |
| 0.1777 | 35.0 | 13895 | 0.6585 | 0.3432 |
| 0.1644 | 36.0 | 14292 | 0.6528 | 0.3455 |
| 0.1644 | 37.0 | 14689 | 0.6460 | 0.3437 |
| 0.1521 | 38.0 | 15086 | 0.6441 | 0.3360 |
| 0.1521 | 39.0 | 15483 | 0.6531 | 0.3350 |
| 0.1521 | 40.0 | 15880 | 0.6478 | 0.3336 |
### Framework versions
- Transformers 4.21.3
- Pytorch 1.10.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
09bedccd9222d434f213e6a31fd53266
|
dipteshkanojia/hing-roberta-CM-run-1
|
dipteshkanojia
|
xlm-roberta
| 9 | 3 |
transformers
| 0 |
text-classification
| true | false | false |
cc-by-4.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 3,101 | false |
<!-- 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. -->
# hing-roberta-CM-run-1
This model is a fine-tuned version of [l3cube-pune/hing-roberta](https://huggingface.co/l3cube-pune/hing-roberta) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4241
- Accuracy: 0.7787
- Precision: 0.7367
- Recall: 0.7378
- F1: 0.7357
## 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: 3e-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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.8552 | 1.0 | 497 | 0.6797 | 0.7103 | 0.6657 | 0.6872 | 0.6648 |
| 0.5998 | 2.0 | 994 | 0.6946 | 0.7304 | 0.6870 | 0.7108 | 0.6933 |
| 0.4146 | 3.0 | 1491 | 0.9422 | 0.7465 | 0.7215 | 0.6734 | 0.6887 |
| 0.2592 | 4.0 | 1988 | 1.3122 | 0.7626 | 0.7240 | 0.7130 | 0.7126 |
| 0.1644 | 5.0 | 2485 | 1.7526 | 0.7344 | 0.6856 | 0.6901 | 0.6875 |
| 0.1022 | 6.0 | 2982 | 1.9479 | 0.7746 | 0.7331 | 0.7317 | 0.7316 |
| 0.0764 | 7.0 | 3479 | 2.0772 | 0.7626 | 0.7190 | 0.7214 | 0.7202 |
| 0.0468 | 8.0 | 3976 | 2.2799 | 0.7626 | 0.7184 | 0.7044 | 0.7099 |
| 0.0472 | 9.0 | 4473 | 2.2257 | 0.7586 | 0.7103 | 0.7176 | 0.7135 |
| 0.0306 | 10.0 | 4970 | 2.3307 | 0.7505 | 0.7068 | 0.7081 | 0.7074 |
| 0.0351 | 11.0 | 5467 | 2.2555 | 0.7666 | 0.7198 | 0.7254 | 0.7219 |
| 0.0328 | 12.0 | 5964 | 2.4425 | 0.7626 | 0.7258 | 0.7124 | 0.7179 |
| 0.0225 | 13.0 | 6461 | 2.5229 | 0.7666 | 0.7237 | 0.7138 | 0.7179 |
| 0.0232 | 14.0 | 6958 | 2.5717 | 0.7646 | 0.7202 | 0.7115 | 0.7144 |
| 0.0191 | 15.0 | 7455 | 2.4027 | 0.7606 | 0.7110 | 0.7202 | 0.7152 |
| 0.0175 | 16.0 | 7952 | 2.3918 | 0.7666 | 0.7216 | 0.7241 | 0.7226 |
| 0.0087 | 17.0 | 8449 | 2.4176 | 0.7767 | 0.7347 | 0.7365 | 0.7345 |
| 0.0077 | 18.0 | 8946 | 2.4231 | 0.7686 | 0.7201 | 0.7265 | 0.7230 |
| 0.0095 | 19.0 | 9443 | 2.4162 | 0.7827 | 0.7392 | 0.7436 | 0.7406 |
| 0.0063 | 20.0 | 9940 | 2.4241 | 0.7787 | 0.7367 | 0.7378 | 0.7357 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.10.1+cu111
- Datasets 2.3.2
- Tokenizers 0.12.1
|
57ebc98bc5bdc17b48293e7780ac77c9
|
btjiong/robbert-twitter-sentiment
|
btjiong
|
roberta
| 11 | 28 |
transformers
| 0 |
text-classification
| true | false | false |
mit
| null |
['dutch_social']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,468 | false |
<!-- 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. -->
# robbert-twitter-sentiment
This model is a fine-tuned version of [pdelobelle/robbert-v2-dutch-base](https://huggingface.co/pdelobelle/robbert-v2-dutch-base) on the dutch_social dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6818
- Accuracy: 0.749
- F1: 0.7492
- Precision: 0.7494
- Recall: 0.749
## 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: 16
- 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 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.7485 | 1.0 | 188 | 0.7670 | 0.692 | 0.6915 | 0.6920 | 0.692 |
| 0.5202 | 2.0 | 376 | 0.6818 | 0.749 | 0.7492 | 0.7494 | 0.749 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cpu
- Datasets 2.0.0
- Tokenizers 0.12.0
|
70b1c25d1f9d0f86adcfaaf9fd0ee795
|
cdinh2022/distilbert-base-uncased-finetuned-emotion
|
cdinh2022
|
distilbert
| 14 | 1 |
transformers
| 0 |
text-classification
| true | false | false |
apache-2.0
| null |
['emotion']
| null | 1 | 1 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,172 | false |
<!-- 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.
## 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: 0.1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 0.1 | 25 | 1.4889 | 0.5195 | 0.3976 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
f97ad3a44e5150c9ad1ab7c9f9575c61
|
fuh990202/distilbert-base-uncased-finetuned-squad
|
fuh990202
|
distilbert
| 12 | 3 |
transformers
| 0 |
question-answering
| true | false | false |
apache-2.0
| null |
['squad']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 1,278 | false |
<!-- 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-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1634
## 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
- 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 2.0874 | 1.0 | 1113 | 1.7948 |
| 1.1106 | 2.0 | 2226 | 1.7791 |
| 0.4632 | 3.0 | 3339 | 2.1634 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2
|
08eea8d70917f7aba6bb57d6048a981a
|
google/multiberts-seed_2-step_2000k
|
google
|
bert
| 8 | 38 |
transformers
| 0 | null | true | true | false |
apache-2.0
|
['en']
| null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['multiberts', 'multiberts-seed_2', 'multiberts-seed_2-step_2000k']
| false | true | true | 3,527 | false |
# MultiBERTs, Intermediate Checkpoint - Seed 2, Step 2000k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://github.com/google-research/bert) but
with different random seeds, which causes variations in the initial weights and order of
training instances. The aim is to distinguish findings that apply to a specific
artifact (i.e., a particular instance of the model) from those that apply to the
more general procedure.
We also provide 140 intermediate checkpoints captured
during the course of pre-training (we saved 28 checkpoints for the first 5 runs).
The models were originally released through
[http://goo.gle/multiberts](http://goo.gle/multiberts). We describe them in our
paper
[The MultiBERTs: BERT Reproductions for Robustness Analysis](https://arxiv.org/abs/2106.16163).
This is model #2, captured at step 2000k (max: 2000k, i.e., 2M steps).
## Model Description
This model was captured during a reproduction of
[BERT-base uncased](https://github.com/google-research/bert), for English: it
is a Transformers model pretrained on a large corpus of English data, using the
Masked Language Modelling (MLM) and the Next Sentence Prediction (NSP)
objectives.
The intended uses, limitations, training data and training procedure for the fully trained model are similar
to [BERT-base uncased](https://github.com/google-research/bert). Two major
differences with the original model:
* We pre-trained the MultiBERTs models for 2 million steps using sequence
length 512 (instead of 1 million steps using sequence length 128 then 512).
* We used an alternative version of Wikipedia and Books Corpus, initially
collected for [Turc et al., 2019](https://arxiv.org/abs/1908.08962).
This is a best-effort reproduction, and so it is probable that differences with
the original model have gone unnoticed. The performance of MultiBERTs on GLUE after full training is oftentimes comparable to that of original
BERT, but we found significant differences on the dev set of SQuAD (MultiBERTs outperforms original BERT).
See our [technical report](https://arxiv.org/abs/2106.16163) for more details.
### How to use
Using code from
[BERT-base uncased](https://huggingface.co/bert-base-uncased), here is an example based on
Tensorflow:
```
from transformers import BertTokenizer, TFBertModel
tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_2-step_2000k')
model = TFBertModel.from_pretrained("google/multiberts-seed_2-step_2000k")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
PyTorch version:
```
from transformers import BertTokenizer, BertModel
tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_2-step_2000k')
model = BertModel.from_pretrained("google/multiberts-seed_2-step_2000k")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
## Citation info
```bibtex
@article{sellam2021multiberts,
title={The MultiBERTs: BERT Reproductions for Robustness Analysis},
author={Thibault Sellam and Steve Yadlowsky and Jason Wei and Naomi Saphra and Alexander D'Amour and Tal Linzen and Jasmijn Bastings and Iulia Turc and Jacob Eisenstein and Dipanjan Das and Ian Tenney and Ellie Pavlick},
journal={arXiv preprint arXiv:2106.16163},
year={2021}
}
```
|
f35ea15bad74fd4e8e669c5f1c3d6186
|
stanfordnlp/stanza-en
|
stanfordnlp
| null | 73 | 5,899 |
stanza
| 6 |
token-classification
| false | false | false |
apache-2.0
|
['en']
| null | null | 1 | 0 | 0 | 1 | 0 | 0 | 0 |
['stanza', 'token-classification']
| false | true | true | 580 | false |
# Stanza model for English (en)
Stanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza brings state-of-the-art NLP models to languages of your choosing.
Find more about it in [our website](https://stanfordnlp.github.io/stanza) and our [GitHub repository](https://github.com/stanfordnlp/stanza).
This card and repo were automatically prepared with `hugging_stanza.py` in the `stanfordnlp/huggingface-models` repo
Last updated 2022-12-09 20:32:45.956
|
cf3c13d61df24b537e6d2da44cc4927d
|
jayanta/google-vit-base-patch16-224-cartoon-face-recognition
|
jayanta
|
vit
| 14 | 17 |
transformers
| 0 |
image-classification
| true | false | false |
apache-2.0
| null |
['imagefolder']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_trainer']
| true | true | true | 3,248 | false |
<!-- 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. -->
# google-vit-base-patch16-224-cartoon-face-recognition
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3707
- Accuracy: 0.9005
- Precision: 0.9066
- Recall: 0.9005
- F1: 0.8984
## 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: 0.00012
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| No log | 0.89 | 6 | 0.5459 | 0.8611 | 0.8683 | 0.8611 | 0.8577 |
| 0.0812 | 1.89 | 12 | 0.4703 | 0.8796 | 0.8833 | 0.8796 | 0.8764 |
| 0.0812 | 2.89 | 18 | 0.4430 | 0.8935 | 0.8969 | 0.8935 | 0.8906 |
| 0.0307 | 3.89 | 24 | 0.4045 | 0.8819 | 0.8849 | 0.8819 | 0.8767 |
| 0.0091 | 4.89 | 30 | 0.3672 | 0.9005 | 0.9025 | 0.9005 | 0.8980 |
| 0.0091 | 5.89 | 36 | 0.3841 | 0.9028 | 0.9125 | 0.9028 | 0.9011 |
| 0.0043 | 6.89 | 42 | 0.3926 | 0.9005 | 0.9073 | 0.9005 | 0.8972 |
| 0.0043 | 7.89 | 48 | 0.3786 | 0.8958 | 0.9005 | 0.8958 | 0.8931 |
| 0.0031 | 8.89 | 54 | 0.3791 | 0.9028 | 0.9091 | 0.9028 | 0.9007 |
| 0.002 | 9.89 | 60 | 0.3677 | 0.9028 | 0.9106 | 0.9028 | 0.9001 |
| 0.002 | 10.89 | 66 | 0.3740 | 0.9028 | 0.9099 | 0.9028 | 0.9007 |
| 0.0027 | 11.89 | 72 | 0.3869 | 0.8981 | 0.9043 | 0.8981 | 0.8956 |
| 0.0027 | 12.89 | 78 | 0.3801 | 0.8981 | 0.9021 | 0.8981 | 0.8954 |
| 0.004 | 13.89 | 84 | 0.3674 | 0.9051 | 0.9113 | 0.9051 | 0.9028 |
| 0.0024 | 14.89 | 90 | 0.3620 | 0.9051 | 0.9096 | 0.9051 | 0.9027 |
| 0.0024 | 15.89 | 96 | 0.3670 | 0.9028 | 0.9089 | 0.9028 | 0.9006 |
| 0.0021 | 16.89 | 102 | 0.3827 | 0.9005 | 0.9065 | 0.9005 | 0.8980 |
| 0.0021 | 17.89 | 108 | 0.3748 | 0.8981 | 0.9049 | 0.8981 | 0.8958 |
| 0.0022 | 18.89 | 114 | 0.3825 | 0.9028 | 0.9101 | 0.9028 | 0.9006 |
| 0.0019 | 19.89 | 120 | 0.3707 | 0.9005 | 0.9066 | 0.9005 | 0.8984 |
### Framework versions
- Transformers 4.24.0.dev0
- Pytorch 1.11.0+cu102
- Datasets 2.6.1
- Tokenizers 0.13.1
|
8cf544beae9e00901318d40fae8f150b
|
itsGanni/Cardinal__Catholicism_-clustered
|
itsGanni
|
distilbert
| 8 | 0 |
transformers
| 0 |
question-answering
| false | true | false |
apache-2.0
| null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['generated_from_keras_callback']
| true | true | true | 1,860 | false |
<!-- 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. -->
# itsGanni/Cardinal__Catholicism_-clustered
This model is a fine-tuned version of [nandysoham/11-clustered](https://huggingface.co/nandysoham/11-clustered) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.4076
- Train End Logits Accuracy: 0.8889
- Train Start Logits Accuracy: 0.9132
- Validation Loss: 0.6765
- Validation End Logits Accuracy: 0.75
- Validation Start Logits Accuracy: 0.75
- 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', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 18, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 0.4076 | 0.8889 | 0.9132 | 0.6765 | 0.75 | 0.75 | 0 |
### Framework versions
- Transformers 4.26.0
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2
|
508597e2205c507b2e9454c5ed342375
|
s3nh/SegFormer-b4-person-segmentation
|
s3nh
|
segformer
| 4 | 2 |
transformers
| 0 |
image-segmentation
| true | false | false |
openrail
|
['en']
| null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
[]
| false | true | true | 390,223 | false |
<a href="https://www.buymeacoffee.com/s3nh"><img src="https://www.buymeacoffee.com/assets/img/guidelines/download-assets-sm-1.svg" alt=""></a>
<img src = 'https://images.unsplash.com/photo-1438761681033-6461ffad8d80?ixlib=rb-4.0.3&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=1170&q=80'>
### Description
Semantic segmentation is a computer vision technique for assigning a label to each pixel in an image, representing the semantic class of the objects or regions in the image.
It's a form of dense prediction because it involves assigning a label to each pixel in an image, instead of just boxes around objects or key points as in object detection or instance segmentation.
The goal of semantic segmentation is to recognize and understand the objects and scenes in an image, and partition the image into segments corresponding to different entities.
## Parameters
```
model = SegformerForSemanticSegmentation.from_pretrained("nvidia/mit-b4",
num_labels=2,
id2label=id2label,
label2id=label2id, )
```
## Usage
```python
from torch import nn
import numpy as np
import matplotlib.pyplot as plt
# Transforms
_transform = A.Compose([
A.Resize(height = 512, width=512),
ToTensorV2(),
])
trans_image = _transform(image=np.array(image))
outputs = model(trans_image['image'].float().unsqueeze(0))
logits = outputs.logits.cpu()
print(logits.shape)
# First, rescale logits to original image size
upsampled_logits = nn.functional.interpolate(logits,
size=image.size[::-1], # (height, width)
mode='bilinear',
align_corners=False)
seg = upsampled_logits.argmax(dim=1)[0]
color_seg = np.zeros((seg.shape[0], seg.shape[1], 3), dtype=np.uint8) # height, width, 3
palette = np.array([[0, 0, 0],[255, 255, 255]])
for label, color in enumerate(palette):
color_seg[seg == label, :] = color
# Convert to BGR
color_seg = color_seg[..., ::-1]
```
<img src = 'data:image/png;base64,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'>
#Metric
Todo
#Note
This model was not built by using Huggingface based feature extractor, so automatic api could not work.
|
620ecfff92e25117c88ed6b24d9acb82
|
lmqg/t5-small-squad-ae
|
lmqg
|
t5
| 13 | 66 |
transformers
| 0 |
text2text-generation
| true | false | false |
cc-by-4.0
|
['en']
|
['lmqg/qg_squad']
| null | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
['answer extraction']
| true | true | true | 4,290 | false |
# Model Card of `lmqg/t5-small-squad-ae`
This model is fine-tuned version of [t5-small](https://huggingface.co/t5-small) for answer extraction on the [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
### Overview
- **Language model:** [t5-small](https://huggingface.co/t5-small)
- **Language:** en
- **Training data:** [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (default)
- **Online Demo:** [https://autoqg.net/](https://autoqg.net/)
- **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation)
- **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)
### Usage
- With [`lmqg`](https://github.com/asahi417/lm-question-generation#lmqg-language-model-for-question-generation-)
```python
from lmqg import TransformersQG
# initialize model
model = TransformersQG(language="en", model="lmqg/t5-small-squad-ae")
# model prediction
answers = model.generate_a("William Turner was an English painter who specialised in watercolour landscapes")
```
- With `transformers`
```python
from transformers import pipeline
pipe = pipeline("text2text-generation", "lmqg/t5-small-squad-ae")
output = pipe("extract answers: <hl> Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records. <hl> Her performance in the film received praise from critics, and she garnered several nominations for her portrayal of James, including a Satellite Award nomination for Best Supporting Actress, and a NAACP Image Award nomination for Outstanding Supporting Actress.")
```
## Evaluation
- ***Metric (Answer Extraction)***: [raw metric file](https://huggingface.co/lmqg/t5-small-squad-ae/raw/main/eval/metric.first.answer.paragraph_sentence.answer.lmqg_qg_squad.default.json)
| | Score | Type | Dataset |
|:-----------------|--------:|:--------|:---------------------------------------------------------------|
| AnswerExactMatch | 56.15 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| AnswerF1Score | 68.06 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| BERTScore | 91.2 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| Bleu_1 | 52.42 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| Bleu_2 | 47.81 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| Bleu_3 | 43.22 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| Bleu_4 | 39.23 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| METEOR | 42.5 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| MoverScore | 80.92 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| ROUGE_L | 67.58 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
## Training hyperparameters
The following hyperparameters were used during fine-tuning:
- dataset_path: lmqg/qg_squad
- dataset_name: default
- input_types: ['paragraph_sentence']
- output_types: ['answer']
- prefix_types: ['ae']
- model: t5-small
- max_length: 512
- max_length_output: 32
- epoch: 7
- batch: 64
- lr: 0.0001
- fp16: False
- random_seed: 1
- gradient_accumulation_steps: 1
- label_smoothing: 0.15
The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/t5-small-squad-ae/raw/main/trainer_config.json).
## Citation
```
@inproceedings{ushio-etal-2022-generative,
title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
author = "Ushio, Asahi and
Alva-Manchego, Fernando and
Camacho-Collados, Jose",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, U.A.E.",
publisher = "Association for Computational Linguistics",
}
```
|
6b8fd5c133401d8ebdca8d26df63a09e
|
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