modelId
string | author
string | last_modified
timestamp[us, tz=UTC] | downloads
int64 | likes
int64 | library_name
string | tags
list | pipeline_tag
string | createdAt
timestamp[us, tz=UTC] | card
string |
|---|---|---|---|---|---|---|---|---|---|
bigmorning/whisper_0015
|
bigmorning
| 2022-11-08T14:43:13Z | 32 | 0 |
transformers
|
[
"transformers",
"tf",
"whisper",
"automatic-speech-recognition",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-11-08T14:42:41Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: whisper_0015
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# whisper_0015
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.3281
- Train Accuracy: 0.0322
- Validation Loss: 0.5841
- Validation Accuracy: 0.0311
- Epoch: 14
## 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': 1e-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 | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 5.0856 | 0.0116 | 4.4440 | 0.0123 | 0 |
| 4.3149 | 0.0131 | 4.0521 | 0.0142 | 1 |
| 3.9260 | 0.0146 | 3.7264 | 0.0153 | 2 |
| 3.5418 | 0.0160 | 3.3026 | 0.0174 | 3 |
| 2.7510 | 0.0198 | 2.0157 | 0.0241 | 4 |
| 1.6782 | 0.0250 | 1.3567 | 0.0273 | 5 |
| 1.1705 | 0.0274 | 1.0678 | 0.0286 | 6 |
| 0.9126 | 0.0287 | 0.9152 | 0.0294 | 7 |
| 0.7514 | 0.0296 | 0.8057 | 0.0299 | 8 |
| 0.6371 | 0.0302 | 0.7409 | 0.0302 | 9 |
| 0.5498 | 0.0307 | 0.6854 | 0.0306 | 10 |
| 0.4804 | 0.0312 | 0.6518 | 0.0307 | 11 |
| 0.4214 | 0.0316 | 0.6200 | 0.0310 | 12 |
| 0.3713 | 0.0319 | 0.5947 | 0.0311 | 13 |
| 0.3281 | 0.0322 | 0.5841 | 0.0311 | 14 |
### Framework versions
- Transformers 4.25.0.dev0
- TensorFlow 2.9.2
- Tokenizers 0.13.2
|
rosamondthalken/t5-base-sci-names
|
rosamondthalken
| 2022-11-08T14:39:36Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"scientific names",
"text generation",
"en",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-09-16T15:00:05Z |
---
language:
- en
tags:
- scientific names
- text generation
license: cc-by-sa-4.0
---
# t5-base-sci-names
Biodiversity literature is dedicated to the identification, documentation, and categorization of plants, fungi, animals, and other living organisms. Correctly extracting the name of an organism within these documents involves finding the entire scientific name–including the genus, specific epithet, and author name. Extracting these names allows biologists to access documents about a species more comprehensively, and to track an organism’s history of documentation, which includes biological changes and changes in how scientists describe them.
**t5-base-sci-names** uses advances in text-to-text generation to generate scientific names and authors from biodiversity literature. This model was trained on hand-labeled biodiversity texts, including labeled information about a mentioned organism's genus (abbreviated and expanded), specific epithet, and author. This model was trained to output 0-N scientific names with specific prefixes (e.g. "genus = " or "epithet = ") and performs best with anywhere from 20-120 words.
You can also use the model in this tutorial for [scientific names generation](https://colab.research.google.com/drive/1GEpnCaMJYiPIhuZiDJ1X1pZsGtGSm8Ds?usp=sharing).
Thanks to Damon Little and Nelson Salinas at the New York Botanical Gardens for their support.
*Note that this model is still a work in progress. Any feedback is welcome.*
|
troesy/roBERTa-3epoch
|
troesy
| 2022-11-08T14:38:27Z | 10 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-11-08T14:21:53Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: roBERTa-3epoch
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roBERTa-3epoch
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1328
- Precision: 0.0
- Recall: 0.0
- F1: 0.0
- Accuracy: 0.9526
## 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: 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 | 174 | 0.1814 | 0.0 | 0.0 | 0.0 | 0.9318 |
| No log | 2.0 | 348 | 0.1445 | 0.0 | 0.0 | 0.0 | 0.9477 |
| 0.214 | 3.0 | 522 | 0.1328 | 0.0 | 0.0 | 0.0 | 0.9526 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2
|
bigmorning/whisper_0010
|
bigmorning
| 2022-11-08T14:20:50Z | 63 | 0 |
transformers
|
[
"transformers",
"tf",
"whisper",
"automatic-speech-recognition",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-11-08T14:19:44Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: whisper_0010
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# whisper_0010
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.6371
- Train Accuracy: 0.0302
- Validation Loss: 0.7409
- Validation Accuracy: 0.0302
- Epoch: 9
## 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': 1e-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 | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 5.0856 | 0.0116 | 4.4440 | 0.0123 | 0 |
| 4.3149 | 0.0131 | 4.0521 | 0.0142 | 1 |
| 3.9260 | 0.0146 | 3.7264 | 0.0153 | 2 |
| 3.5418 | 0.0160 | 3.3026 | 0.0174 | 3 |
| 2.7510 | 0.0198 | 2.0157 | 0.0241 | 4 |
| 1.6782 | 0.0250 | 1.3567 | 0.0273 | 5 |
| 1.1705 | 0.0274 | 1.0678 | 0.0286 | 6 |
| 0.9126 | 0.0287 | 0.9152 | 0.0294 | 7 |
| 0.7514 | 0.0296 | 0.8057 | 0.0299 | 8 |
| 0.6371 | 0.0302 | 0.7409 | 0.0302 | 9 |
### Framework versions
- Transformers 4.25.0.dev0
- TensorFlow 2.9.2
- Tokenizers 0.13.2
|
google/ddpm-ema-cat-256
|
google
| 2022-11-08T13:42:16Z | 1,133 | 2 |
diffusers
|
[
"diffusers",
"pytorch",
"unconditional-image-generation",
"arxiv:2006.11239",
"license:apache-2.0",
"diffusers:DDPMPipeline",
"region:us"
] |
unconditional-image-generation
| 2022-07-19T10:45:53Z |
---
license: apache-2.0
tags:
- pytorch
- diffusers
- unconditional-image-generation
---
# Denoising Diffusion Probabilistic Models (DDPM)
**Paper**: [Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2006.11239)
**Authors**: Jonathan Ho, Ajay Jain, Pieter Abbeel
**Abstract**:
*We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best results are obtained by training on a weighted variational bound designed according to a novel connection between diffusion probabilistic models and denoising score matching with Langevin dynamics, and our models naturally admit a progressive lossy decompression scheme that can be interpreted as a generalization of autoregressive decoding. On the unconditional CIFAR10 dataset, we obtain an Inception score of 9.46 and a state-of-the-art FID score of 3.17. On 256x256 LSUN, we obtain sample quality similar to ProgressiveGAN.*
## Inference
**DDPM** models can use *discrete noise schedulers* such as:
- [scheduling_ddpm](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_ddpm.py)
- [scheduling_ddim](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_ddim.py)
- [scheduling_pndm](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_pndm.py)
for inference. Note that while the *ddpm* scheduler yields the highest quality, it also takes the longest.
For a good trade-off between quality and inference speed you might want to consider the *ddim* or *pndm* schedulers instead.
See the following code:
```python
# !pip install diffusers
from diffusers import DDPMPipeline, DDIMPipeline, PNDMPipeline
model_id = "google/ddpm-ema-cat-256"
# load model and scheduler
ddpm = DDPMPipeline.from_pretrained(model_id) # you can replace DDPMPipeline with DDIMPipeline or PNDMPipeline for faster inference
# run pipeline in inference (sample random noise and denoise)
image = ddpm().images[0]
# save image
image.save("ddpm_generated_image.png")
```
For more in-detail information, please have a look at the [official inference example](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/diffusers_intro.ipynb)
## Training
If you want to train your own model, please have a look at the [official training example](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/training_example.ipynb)
## Samples
1. 
2. 
3. 
4. 
|
google/ddpm-ema-bedroom-256
|
google
| 2022-11-08T13:41:41Z | 392 | 2 |
diffusers
|
[
"diffusers",
"pytorch",
"unconditional-image-generation",
"arxiv:2006.11239",
"license:apache-2.0",
"diffusers:DDPMPipeline",
"region:us"
] |
unconditional-image-generation
| 2022-07-18T19:49:13Z |
---
license: apache-2.0
tags:
- pytorch
- diffusers
- unconditional-image-generation
---
# Denoising Diffusion Probabilistic Models (DDPM)
**Paper**: [Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2006.11239)
**Authors**: Jonathan Ho, Ajay Jain, Pieter Abbeel
**Abstract**:
*We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best results are obtained by training on a weighted variational bound designed according to a novel connection between diffusion probabilistic models and denoising score matching with Langevin dynamics, and our models naturally admit a progressive lossy decompression scheme that can be interpreted as a generalization of autoregressive decoding. On the unconditional CIFAR10 dataset, we obtain an Inception score of 9.46 and a state-of-the-art FID score of 3.17. On 256x256 LSUN, we obtain sample quality similar to ProgressiveGAN.*
## Inference
**DDPM** models can use *discrete noise schedulers* such as:
- [scheduling_ddpm](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_ddpm.py)
- [scheduling_ddim](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_ddim.py)
- [scheduling_pndm](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_pndm.py)
for inference. Note that while the *ddpm* scheduler yields the highest quality, it also takes the longest.
For a good trade-off between quality and inference speed you might want to consider the *ddim* or *pndm* schedulers instead.
See the following code:
```python
# !pip install diffusers
from diffusers import DDPMPipeline, DDIMPipeline, PNDMPipeline
model_id = "google/ddpm-ema-bedroom-256"
# load model and scheduler
ddpm = DDPMPipeline.from_pretrained(model_id) # you can replace DDPMPipeline with DDIMPipeline or PNDMPipeline for faster inference
# run pipeline in inference (sample random noise and denoise)
image = ddpm().images[0]
# save image
image.save("ddpm_generated_image.png")
```
For more in-detail information, please have a look at the [official inference example](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/diffusers_intro.ipynb)
## Training
If you want to train your own model, please have a look at the [official training example](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/training_example.ipynb)
## Samples
1. 
2. 
3. 
4. 
|
google/ddpm-bedroom-256
|
google
| 2022-11-08T13:41:35Z | 626 | 4 |
diffusers
|
[
"diffusers",
"pytorch",
"unconditional-image-generation",
"arxiv:2006.11239",
"license:apache-2.0",
"diffusers:DDPMPipeline",
"region:us"
] |
unconditional-image-generation
| 2022-07-19T10:43:04Z |
---
license: apache-2.0
tags:
- pytorch
- diffusers
- unconditional-image-generation
---
# Denoising Diffusion Probabilistic Models (DDPM)
**Paper**: [Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2006.11239)
**Authors**: Jonathan Ho, Ajay Jain, Pieter Abbeel
**Abstract**:
*We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best results are obtained by training on a weighted variational bound designed according to a novel connection between diffusion probabilistic models and denoising score matching with Langevin dynamics, and our models naturally admit a progressive lossy decompression scheme that can be interpreted as a generalization of autoregressive decoding. On the unconditional CIFAR10 dataset, we obtain an Inception score of 9.46 and a state-of-the-art FID score of 3.17. On 256x256 LSUN, we obtain sample quality similar to ProgressiveGAN.*
## Inference
**DDPM** models can use *discrete noise schedulers* such as:
- [scheduling_ddpm](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_ddpm.py)
- [scheduling_ddim](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_ddim.py)
- [scheduling_pndm](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_pndm.py)
for inference. Note that while the *ddpm* scheduler yields the highest quality, it also takes the longest.
For a good trade-off between quality and inference speed you might want to consider the *ddim* or *pndm* schedulers instead.
See the following code:
```python
# !pip install diffusers
from diffusers import DDPMPipeline, DDIMPipeline, PNDMPipeline
model_id = "google/ddpm-bedroom-256"
# load model and scheduler
ddpm = DDPMPipeline.from_pretrained(model_id) # you can replace DDPMPipeline with DDIMPipeline or PNDMPipeline for faster inference
# run pipeline in inference (sample random noise and denoise)
image = ddpm().images[0]
# save image
image.save("ddpm_generated_image.png")
```
For more in-detail information, please have a look at the [official inference example](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/diffusers_intro.ipynb)
## Training
If you want to train your own model, please have a look at the [official training example](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/training_example.ipynb)
## Samples
1. 
2. 
3. 
4. 
|
google/ddpm-ema-celebahq-256
|
google
| 2022-11-08T13:41:29Z | 10,679 | 6 |
diffusers
|
[
"diffusers",
"pytorch",
"unconditional-image-generation",
"arxiv:2006.11239",
"license:apache-2.0",
"diffusers:DDPMPipeline",
"region:us"
] |
unconditional-image-generation
| 2022-07-19T10:42:32Z |
---
license: apache-2.0
tags:
- pytorch
- diffusers
- unconditional-image-generation
---
# Denoising Diffusion Probabilistic Models (DDPM)
**Paper**: [Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2006.11239)
**Authors**: Jonathan Ho, Ajay Jain, Pieter Abbeel
**Abstract**:
*We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best results are obtained by training on a weighted variational bound designed according to a novel connection between diffusion probabilistic models and denoising score matching with Langevin dynamics, and our models naturally admit a progressive lossy decompression scheme that can be interpreted as a generalization of autoregressive decoding. On the unconditional CIFAR10 dataset, we obtain an Inception score of 9.46 and a state-of-the-art FID score of 3.17. On 256x256 LSUN, we obtain sample quality similar to ProgressiveGAN.*
## Inference
**DDPM** models can use *discrete noise schedulers* such as:
- [scheduling_ddpm](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_ddpm.py)
- [scheduling_ddim](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_ddim.py)
- [scheduling_pndm](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_pndm.py)
for inference. Note that while the *ddpm* scheduler yields the highest quality, it also takes the longest.
For a good trade-off between quality and inference speed you might want to consider the *ddim* or *pndm* schedulers instead.
See the following code:
```python
# !pip install diffusers
from diffusers import DDPMPipeline, DDIMPipeline, PNDMPipeline
model_id = "google/ddpm-ema-celebahq-256"
# load model and scheduler
ddpm = DDPMPipeline.from_pretrained(model_id) # you can replace DDPMPipeline with DDIMPipeline or PNDMPipeline for faster inference
# run pipeline in inference (sample random noise and denoise)
image = ddpm().images[0]
# save image
image.save("ddpm_generated_image.png")
```
For more in-detail information, please have a look at the [official inference example](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/diffusers_intro.ipynb)
## Training
If you want to train your own model, please have a look at the [official training example](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/training_example.ipynb) # <- TODO(PVP) add link
## Samples
1. 
2. 
3. 
4. 
|
google/ddpm-ema-church-256
|
google
| 2022-11-08T13:41:12Z | 447 | 11 |
diffusers
|
[
"diffusers",
"pytorch",
"unconditional-image-generation",
"arxiv:2006.11239",
"license:apache-2.0",
"diffusers:DDPMPipeline",
"region:us"
] |
unconditional-image-generation
| 2022-07-19T10:43:19Z |
---
license: apache-2.0
tags:
- pytorch
- diffusers
- unconditional-image-generation
---
# Denoising Diffusion Probabilistic Models (DDPM)
**Paper**: [Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2006.11239)
**Authors**: Jonathan Ho, Ajay Jain, Pieter Abbeel
**Abstract**:
*We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best results are obtained by training on a weighted variational bound designed according to a novel connection between diffusion probabilistic models and denoising score matching with Langevin dynamics, and our models naturally admit a progressive lossy decompression scheme that can be interpreted as a generalization of autoregressive decoding. On the unconditional CIFAR10 dataset, we obtain an Inception score of 9.46 and a state-of-the-art FID score of 3.17. On 256x256 LSUN, we obtain sample quality similar to ProgressiveGAN.*
## Inference
**DDPM** models can use *discrete noise schedulers* such as:
- [scheduling_ddpm](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_ddpm.py)
- [scheduling_ddim](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_ddim.py)
- [scheduling_pndm](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_pndm.py)
for inference. Note that while the *ddpm* scheduler yields the highest quality, it also takes the longest.
For a good trade-off between quality and inference speed you might want to consider the *ddim* or *pndm* schedulers instead.
See the following code:
```python
# !pip install diffusers
from diffusers import DDPMPipeline, DDIMPipeline, PNDMPipeline
model_id = "google/ddpm-ema-church-256"
# load model and scheduler
ddpm = DDPMPipeline.from_pretrained(model_id) # you can replace DDPMPipeline with DDIMPipeline or PNDMPipeline for faster inference
# run pipeline in inference (sample random noise and denoise)
image = ddpm().images[0]
# save image
image.save("ddpm_generated_image.png")
```
For more in-detail information, please have a look at the [official inference example](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/diffusers_intro.ipynb)
## Training
If you want to train your own model, please have a look at the [official training example](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/training_example.ipynb)
## Samples
1. 
2. 
3. 
4. 
|
Prem11100/donut-base-Label-studio-707-invoices
|
Prem11100
| 2022-11-08T13:05:06Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vision-encoder-decoder",
"image-text-to-text",
"generated_from_trainer",
"dataset:imagefolder",
"license:mit",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2022-11-08T09:43:20Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- imagefolder
model-index:
- name: donut-base-Label-studio-707-invoices
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# donut-base-Label-studio-707-invoices
This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on the imagefolder dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 12
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.25.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2
|
troesy/hateBERT_3epoch
|
troesy
| 2022-11-08T10:21:20Z | 16 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-11-08T10:07:36Z |
---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: hateBERT_3epoch
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# hateBERT_3epoch
This model is a fine-tuned version of [GroNLP/hateBERT](https://huggingface.co/GroNLP/hateBERT) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2174
- Precision: 0.0
- Recall: 0.0
- F1: 0.0
- Accuracy: 0.9174
## 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: 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 | 174 | 0.2301 | 0.0 | 0.0 | 0.0 | 0.9112 |
| No log | 2.0 | 348 | 0.2192 | 0.0 | 0.0 | 0.0 | 0.9148 |
| 0.2311 | 3.0 | 522 | 0.2174 | 0.0 | 0.0 | 0.0 | 0.9174 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2
|
dreaming-tree/rl_class
|
dreaming-tree
| 2022-11-08T10:17:43Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-11-08T10:16:56Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 73.61 +/- 70.22
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
tkubotake/xlm-roberta-base-finetuned-panx-all
|
tkubotake
| 2022-11-08T09:07:09Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-11-07T03:46:39Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-all
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-all
This model is a fine-tuned version of [tkubotake/xlm-roberta-base-finetuned-panx-de](https://huggingface.co/tkubotake/xlm-roberta-base-finetuned-panx-de) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2290
- F1: 0.8629
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.1259 | 1.0 | 835 | 0.1879 | 0.8478 |
| 0.078 | 2.0 | 1670 | 0.2121 | 0.8582 |
| 0.0439 | 3.0 | 2505 | 0.2290 | 0.8629 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2
|
Enthusiastic/Stars
|
Enthusiastic
| 2022-11-08T08:44:02Z | 28 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-11-08T08:43:47Z |
---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: Stars
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.5135135054588318
---
# Stars
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### Andromeda
#### Cassiopeia
#### Hercules
#### Orion
#### Perseus
|
sd-concepts-library/kodakvision500t
|
sd-concepts-library
| 2022-11-08T08:19:22Z | 0 | 14 | null |
[
"license:mit",
"region:us"
] | null | 2022-11-08T07:57:07Z |
---
license: mit
---
### KodakVision500T on Stable Diffusion
This is the `<kodakvision_500T>` 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).
This concept was trained on **6** photographs taken with **Kodak Vision 3 500T**, through **1800** steps.
Here are some generated images from the concept that you will be able to use as a `style`:
|
Sushanti123/layoutxlm-finetuned-xfund-fr
|
Sushanti123
| 2022-11-08T07:26:56Z | 12 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"layoutlmv2",
"token-classification",
"generated_from_trainer",
"dataset:xfun",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-11-01T08:40:16Z |
---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
datasets:
- xfun
model-index:
- name: layoutxlm-finetuned-xfund-fr
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# layoutxlm-finetuned-xfund-fr
This model is a fine-tuned version of [microsoft/layoutxlm-base](https://huggingface.co/microsoft/layoutxlm-base) on the xfun 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: 1e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 1000
### Training results
### Framework versions
- Transformers 4.24.0
- Pytorch 1.10.0+cu111
- Datasets 2.6.1
- Tokenizers 0.13.2
|
bguan/Reinforce-Pixelcopter-PLE-v0
|
bguan
| 2022-11-08T07:20:11Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-11-08T07:20:03Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-PLE-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 16.00 +/- 11.92
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
|
GuiGel/beto-uncased-flert-context-we-lstm-crf-meddocan
|
GuiGel
| 2022-11-08T07:19:25Z | 6 | 0 |
flair
|
[
"flair",
"pytorch",
"token-classification",
"sequence-tagger-model",
"region:us"
] |
token-classification
| 2022-11-08T07:16:36Z |
---
tags:
- flair
- token-classification
- sequence-tagger-model
---
### Demo: How to use in Flair
Requires:
- **[Flair](https://github.com/flairNLP/flair/)** (`pip install flair`)
```python
from flair.data import Sentence
from flair.models import SequenceTagger
# load tagger
tagger = SequenceTagger.load("GuiGel/beto-uncased-flert-context-we-lstm-crf-meddocan")
# make example sentence
sentence = Sentence("On September 1st George won 1 dollar while watching Game of Thrones.")
# predict NER tags
tagger.predict(sentence)
# print sentence
print(sentence)
# print predicted NER spans
print('The following NER tags are found:')
# iterate over entities and print
for entity in sentence.get_spans('ner'):
print(entity)
```
|
Prem11100/donut-base-Label-studio-200-invoices
|
Prem11100
| 2022-11-08T06:49:59Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vision-encoder-decoder",
"image-text-to-text",
"generated_from_trainer",
"dataset:imagefolder",
"license:mit",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2022-11-08T05:45:03Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- imagefolder
model-index:
- name: donut-base-Label-studio-200-invoices
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# donut-base-Label-studio-200-invoices
This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on the imagefolder dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.25.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2
|
Meow412/finetuning-sentiment-BERTmodel-A3-allcontents
|
Meow412
| 2022-11-08T06:40:42Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-11-08T05:51:44Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-BERTmodel-A3-allcontents
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-BERTmodel-A3-allcontents
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2951
- Accuracy: 0.8814
- F1: 0.4138
## 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 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2
|
gaioNL/lesson2FrozenLake
|
gaioNL
| 2022-11-08T06:00:56Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-11-08T06:00:40Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: lesson2FrozenLake
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="gaioNL/lesson2FrozenLake", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
xu1998hz/sescore_english_mt
|
xu1998hz
| 2022-11-08T05:16:19Z | 0 | 1 | null |
[
"region:us"
] | null | 2022-11-05T01:44:33Z |
SEScore English checkpoint for Machine Translation
|
kit-nlp/bert-base-japanese-sentiment-irony
|
kit-nlp
| 2022-11-08T04:23:27Z | 481 | 4 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"ja",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-11-07T06:29:21Z |
---
language: ja
license: cc-by-sa-4.0
---
# BERT Base Japanese for Irony
This is a BERT Base model for sentiment analysis in Japanese additionally finetuned for automatic irony detection.
The model was based on [bert-base-japanese-sentiment](https://huggingface.co/daigo/bert-base-japanese-sentiment), and later finetuned on a dataset containing ironic and sarcastic 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{dan2022bert-base-irony02,
title={北見工業大学 テキスト情報処理研究室 ELECTRA Base 皮肉検出モデル (daigo ver.)},
author={団 俊輔 and プタシンスキ ミハウ and ジェプカ ラファウ and 桝井 文人},
publisher={HuggingFace},
year={2022},
url = "https://huggingface.co/kit-nlp/bert-base-japanese-sentiment-irony"
}
```
|
kit-nlp/yacis-electra-small-japanese-irony
|
kit-nlp
| 2022-11-08T04:16:30Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"electra",
"text-classification",
"ja",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-11-07T07:05:34Z |
---
language: ja
license: cc-by-sa-4.0
---
# YACIS ELECTRA Small Japanese for Irony
This is an [ELECTRA](https://github.com/google-research/electra) Base model for the Japanese language finetuned for automatic irony detection.
The model was based on [YACIS ELECTRA small Japanese](https://huggingface.co/ptaszynski/yacis-electra-small-japanese), and later finetuned on a dataset containing ironic and sarcastic 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{dan2022yaciselectra-small-irony,
title={北見工業大学 テキスト情報処理研究室 ELECTRA Base 皮肉検出モデル (Izumi Labs ver.)},
author={団 俊輔 and プタシンスキ ミハウ and ジェプカ ラファウ and 桝井 文人},
publisher={HuggingFace},
year={2022},
url = "https://huggingface.co/kit-nlp/yacis-electra-small-japanese-irony"
}
```
|
kit-nlp/electra-small-japanese-discriminator-irony
|
kit-nlp
| 2022-11-08T04:11:04Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"electra",
"text-classification",
"ja",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-11-07T07:14:32Z |
---
language: ja
license: cc-by-sa-4.0
---
# ELECTRA small Japanese discriminator for Irony
This is an [ELECTRA](https://github.com/google-research/electra) Base model for the Japanese language finetuned for automatic irony detection.
The model was based on [ELECTRA small Japanese discriminator](https://huggingface.co/izumi-lab/electra-small-japanese-discriminator), and later finetuned on a dataset containing ironic and sarcastic 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{dan2022electra-base-irony,
title={北見工業大学 テキスト情報処理研究室 ELECTRA Base 皮肉検出モデル (Izumi Labs ver.)},
author={団 俊輔 and プタシンスキ ミハウ and ジェプカ ラファウ and 桝井 文人},
publisher={HuggingFace},
year={2022},
url = "https://huggingface.co/kit-nlp/electra-small-japanese-discriminator-irony"
}
```
|
dhshin/ddpm-butterflies-128
|
dhshin
| 2022-11-08T03:16:58Z | 3 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"en",
"dataset:huggan/smithsonian_butterflies_subset",
"license:apache-2.0",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2022-10-25T01:06:18Z |
---
language: en
license: apache-2.0
library_name: diffusers
tags: []
datasets: huggan/smithsonian_butterflies_subset
metrics: []
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# ddpm-butterflies-128
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library
on the `huggan/smithsonian_butterflies_subset` dataset.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training data
[TODO: describe the data used to train the model]
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- gradient_accumulation_steps: 1
- optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None
- lr_scheduler: None
- lr_warmup_steps: 500
- ema_inv_gamma: None
- ema_inv_gamma: None
- ema_inv_gamma: None
- mixed_precision: fp16
### Training results
📈 [TensorBoard logs](https://huggingface.co/dhshin/ddpm-butterflies-128/tensorboard?#scalars)
|
QianMolloy/distilbert-base-uncased-finetuned-emotion
|
QianMolloy
| 2022-11-08T03:06:18Z | 108 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-11-07T02:51:02Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9285
- name: F1
type: f1
value: 0.928851862350588
---
<!-- 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: 0.2178
- Accuracy: 0.9285
- F1: 0.9289
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8227 | 1.0 | 250 | 0.3212 | 0.8985 | 0.8932 |
| 0.2463 | 2.0 | 500 | 0.2178 | 0.9285 | 0.9289 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.10.0
- Datasets 2.6.1
- Tokenizers 0.13.1
|
svjack/prompt-extend-chinese
|
svjack
| 2022-11-08T03:05:03Z | 106 | 3 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"MT5",
"text-to-text",
"zh",
"Chinese",
"license:other",
"autotrain_compatible",
"region:us"
] |
text2text-generation
| 2022-11-07T12:12:51Z |
---
language: zh
license: other
tags:
- MT5
- mt5
- text-to-text
- zh
- Chinese
inference: false
extra_gated_prompt: |-
The License specifies:
1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content
2. rinna Co., Ltd. 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
By clicking on "Access repository" below, you accept that your *contact information* (email address and username) can be shared with the model authors as well.
extra_gated_fields:
I have read the License and agree with its terms: checkbox
---
# Chinese Stable Diffusion Prompt Extend Model Card
<!--
-->
svjack/prompt-extend-chinese is a Chinese-specific latent text-to-text generator generating style cues given a short Chinese prompt input. This generator may make the Stable Diffusion model perform well with the help of some meaningful style cues.<br/>
The above idea is sourced from a project named [prompt-extend](https://github.com/daspartho/prompt-extend), it extending stable diffusion English prompts with suitable style cues using text generation.
And people can try it on [HuggingFace Space](https://huggingface.co/spaces/daspartho/prompt-extend).
```python
from transformers import T5Tokenizer, MT5ForConditionalGeneration
model = "svjack/prompt-extend-chinese"
device = "cpu"
tokenizer = T5Tokenizer.from_pretrained(model)
model = MT5ForConditionalGeneration.from_pretrained(model).to(device).eval()
prompt = "护国公克伦威尔"
encode = tokenizer(prompt, return_tensors='pt').to(device)
answer = model.generate(encode.input_ids)[0]
decoded = tokenizer.decode(answer, skip_special_tokens=True)
decoded
'''
的肖像,由,和,制作,在艺术站上趋势
'''
```
With the help of this generator, people can give some enhance to the stable diffusion model. Take [svjack/Stable-Diffusion-FineTuned-zh-v1](https://huggingface.co/svjack/Stable-Diffusion-FineTuned-zh-v1) for example. below image is the enhanced version of above.
第一次世界大战
第一次世界大战,在艺术站的潮流,8,高度详细,高质量,高分辨率,获
And below example is pivotal.
护国公克伦威尔
护国公克伦威尔,的肖像,由,和,制作,在艺术站上趋势
|
PrimeQA/DrDecr-large_XOR-TyDi_whitebox
|
PrimeQA
| 2022-11-08T02:57:16Z | 0 | 0 | null |
[
"arxiv:2112.08185",
"region:us"
] | null | 2022-11-08T01:05:48Z |
# Basic Information
This is the Dr. Decr-large model used in XOR-TyDi leaderboard task 1 whitebox submission.
https://nlp.cs.washington.edu/xorqa/
The detailed implementation of the model can be found in:
https://arxiv.org/pdf/2112.08185.pdf
Source code to train the model can be found via PrimeQA's IR component:
https://github.com/primeqa/primeqa/tree/main/examples/drdecr
It is a Neural IR model built on top of the ColBERTv2 api and not directly compatible with Huggingface API. The inference result on XOR Dev dataset is:
```
R@2kt R@5kt
ko 69.1 75.1
ar 68.0 75.7
bn 81.9 85.2
fi 68.2 73.6
ru 67.1 72.2
ja 63.1 69.7
te 82.8 86.1
Avg 71.4 76.8
```
# Limitations and Bias
This model used pre-trained XLMR-large model and fine tuned on 7 languages in XOR-TyDi leaderboard. The performance of other languages was not tested.
Since the model was fine-tuned on a large pre-trained language model XLM-Roberta, biases associated with the pre-existing XLM-Roberta model may be present in our fine-tuned model, Dr. Decr
# Citation
```
@article{Li2021_DrDecr,
doi = {10.48550/ARXIV.2112.08185},
url = {https://arxiv.org/abs/2112.08185},
author = {Li, Yulong and Franz, Martin and Sultan, Md Arafat and Iyer, Bhavani and Lee, Young-Suk and Sil, Avirup},
keywords = {Computation and Language (cs.CL), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Learning Cross-Lingual IR from an English Retriever},
publisher = {arXiv},
year = {2021}
}
```
|
gabrielgmendonca/bert-base-portuguese-cased-finetuned-chico-xavier
|
gabrielgmendonca
| 2022-11-08T01:25:38Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-09-25T18:00:16Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: bert-base-portuguese-cased-finetuned-chico-xavier
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-portuguese-cased-finetuned-chico-xavier
This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7196
## 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: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.0733 | 1.0 | 561 | 1.8147 |
| 1.8779 | 2.0 | 1122 | 1.7624 |
| 1.8345 | 3.0 | 1683 | 1.7206 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2
|
tomrb/bettercallbloom-3b
|
tomrb
| 2022-11-08T01:12:59Z | 9 | 5 |
transformers
|
[
"transformers",
"pytorch",
"bloom",
"text-generation",
"en",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-10-20T17:17:16Z |
---
language: en
license: mit
---
# BetterCallBloom-3b
Finetuned Bloom-3b model on the r/legaladvice subreddit from pileoflaw
## Model description
BLOOM-3B is a 3,002,557,440 parameters model pretrained by the BigScience initiative.
## Intended uses & limitations
### How to use
### Limitations and bias
## Training data
## Training procedure
### Preprocessing
## Evaluation results
### BibTeX entry and citation info
|
Asfesalas/ppo-LunarLander-v2
|
Asfesalas
| 2022-11-08T00:40:57Z | 6 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-11-08T00:40:18Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 234.84 +/- 22.67
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
rajistics/informal_formal_style_transfer
|
rajistics
| 2022-11-08T00:21:12Z | 6 | 6 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"en",
"arxiv:1804.06437",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-11-08T00:07:12Z |
---
license: apache-2.0
language: en
---
## Source
A Neural Language Style Transfer framework to transfer natural language text smoothly between fine-grained language styles like formal/casual. The original model is at [https://github.com/PrithivirajDamodaran/Styleformer](https://github.com/PrithivirajDamodaran/Styleformer).
## Examples:
```
[Casual] I am quitting my job
[Formal] I will be stepping down from my job.
----------------------------------------------------------------------------------------------------
[Casual] Jimmy is on crack and can't trust him
[Formal] Jimmy is a crack addict I cannot trust him
----------------------------------------------------------------------------------------------------
[Casual] What do guys do to show that they like a gal?
[Formal] What do guys do to demonstrate their affinity for women?
----------------------------------------------------------------------------------------------------
[Casual] i loooooooooooooooooooooooove going to the movies.
[Formal] I really like to go to the movies.
```
## References
- [Formality Style Transfer for Noisy Text: Leveraging Out-of-Domain
Parallel Data for In-Domain Training via POS Masking](https://www.aclweb.org/anthology/D19-5502.pdf)
- [Generative Text Style Transfer for Improved Language Sophistication](http://cs230.stanford.edu/projects_winter_2020/reports/32069807.pdf)
- [Delete, Retrieve, Generate: A Simple Approach to Sentiment and Style Transfer](https://arxiv.org/pdf/1804.06437.pdf)
|
yongauh/distilbert-base-uncased-finetuned-emotion
|
yongauh
| 2022-11-07T23:48:42Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-11-07T23:35:23Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.921
- name: F1
type: f1
value: 0.9211554013340549
---
<!-- 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: 0.2208
- Accuracy: 0.921
- F1: 0.9212
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8473 | 1.0 | 250 | 0.3167 | 0.908 | 0.9061 |
| 0.2561 | 2.0 | 500 | 0.2208 | 0.921 | 0.9212 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.12.1
- Datasets 2.6.1
- Tokenizers 0.11.0
|
Gr00t16/distilbert-imdb
|
Gr00t16
| 2022-11-07T23:24:30Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-11-07T22:53:28Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
model-index:
- name: distilbert-imdb
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: train
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.92916
---
<!-- 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-imdb
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: 0.1827
- Accuracy: 0.9292
## 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: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2182 | 1.0 | 1563 | 0.1827 | 0.9292 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2
|
Devarshi/Brain_Tumor_Detector_swin
|
Devarshi
| 2022-11-07T22:28:14Z | 50 | 4 |
transformers
|
[
"transformers",
"pytorch",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-11-07T06:37:51Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- f1
- recall
- precision
model-index:
- name: Brain_Tumor_Detector_swin
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9981308411214953
- name: F1
type: f1
value: 0.9985111662531018
- name: Recall
type: recall
value: 0.9990069513406157
- name: Precision
type: precision
value: 0.998015873015873
---
<!-- 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. -->
# Brain_Tumor_Detector_swin
This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224-in22k](https://huggingface.co/microsoft/swin-base-patch4-window7-224-in22k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0054
- Accuracy: 0.9981
- F1: 0.9985
- Recall: 0.9990
- Precision: 0.9980
## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Recall | Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.079 | 1.0 | 113 | 0.0283 | 0.9882 | 0.9906 | 0.9930 | 0.9881 |
| 0.0575 | 2.0 | 226 | 0.0121 | 0.9956 | 0.9965 | 0.9950 | 0.9980 |
| 0.0312 | 3.0 | 339 | 0.0054 | 0.9981 | 0.9985 | 0.9990 | 0.9980 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1
- Datasets 2.6.1
- Tokenizers 0.13.1
|
SiddharthaM/resnet-18-feature-extraction
|
SiddharthaM
| 2022-11-07T21:50:04Z | 114 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"resnet",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-11-07T17:47:48Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: resnet-18-feature-extraction
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.95
- name: Precision
type: precision
value: 0.9652777777777778
- name: Recall
type: recall
value: 0.9788732394366197
- name: F1
type: f1
value: 0.972027972027972
---
<!-- 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. -->
# resnet-18-feature-extraction
This model is a fine-tuned version of [microsoft/resnet-18](https://huggingface.co/microsoft/resnet-18) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1485
- Accuracy: 0.95
- Precision: 0.9653
- Recall: 0.9789
- F1: 0.9720
- Roc Auc: 0.8505
## 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
- 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: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Roc Auc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
| No log | 0.8 | 2 | 0.6232 | 0.75 | 0.9636 | 0.7465 | 0.8413 | 0.7621 |
| No log | 1.8 | 4 | 0.6971 | 0.4875 | 1.0 | 0.4225 | 0.5941 | 0.7113 |
| No log | 2.8 | 6 | 0.7915 | 0.2875 | 1.0 | 0.1972 | 0.3294 | 0.5986 |
| No log | 3.8 | 8 | 0.8480 | 0.2875 | 1.0 | 0.1972 | 0.3294 | 0.5986 |
| 0.8651 | 4.8 | 10 | 0.9094 | 0.2562 | 1.0 | 0.1620 | 0.2788 | 0.5810 |
| 0.8651 | 5.8 | 12 | 0.7470 | 0.5625 | 1.0 | 0.5070 | 0.6729 | 0.7535 |
| 0.8651 | 6.8 | 14 | 0.5915 | 0.85 | 1.0 | 0.8310 | 0.9077 | 0.9155 |
| 0.8651 | 7.8 | 16 | 0.4817 | 0.8875 | 0.9844 | 0.8873 | 0.9333 | 0.8881 |
| 0.8651 | 8.8 | 18 | 0.3455 | 0.9187 | 0.9778 | 0.9296 | 0.9531 | 0.8815 |
| 0.5349 | 9.8 | 20 | 0.2966 | 0.9187 | 0.9708 | 0.9366 | 0.9534 | 0.8572 |
| 0.5349 | 10.8 | 22 | 0.2347 | 0.95 | 0.9653 | 0.9789 | 0.9720 | 0.8505 |
| 0.5349 | 11.8 | 24 | 0.2468 | 0.9313 | 0.9645 | 0.9577 | 0.9611 | 0.8400 |
| 0.5349 | 12.8 | 26 | 0.2310 | 0.9563 | 0.9720 | 0.9789 | 0.9754 | 0.8783 |
| 0.5349 | 13.8 | 28 | 0.2083 | 0.9313 | 0.9580 | 0.9648 | 0.9614 | 0.8157 |
| 0.3593 | 14.8 | 30 | 0.1840 | 0.9375 | 0.9521 | 0.9789 | 0.9653 | 0.7950 |
| 0.3593 | 15.8 | 32 | 0.1947 | 0.9375 | 0.9648 | 0.9648 | 0.9648 | 0.8435 |
| 0.3593 | 16.8 | 34 | 0.1837 | 0.9313 | 0.9517 | 0.9718 | 0.9617 | 0.7915 |
| 0.3593 | 17.8 | 36 | 0.1819 | 0.9437 | 0.9524 | 0.9859 | 0.9689 | 0.7985 |
| 0.3593 | 18.8 | 38 | 0.1924 | 0.9437 | 0.9650 | 0.9718 | 0.9684 | 0.8470 |
| 0.2737 | 19.8 | 40 | 0.1990 | 0.95 | 0.9653 | 0.9789 | 0.9720 | 0.8505 |
| 0.2737 | 20.8 | 42 | 0.1759 | 0.95 | 0.9718 | 0.9718 | 0.9718 | 0.8748 |
| 0.2737 | 21.8 | 44 | 0.1804 | 0.9313 | 0.9517 | 0.9718 | 0.9617 | 0.7915 |
| 0.2737 | 22.8 | 46 | 0.1666 | 0.9313 | 0.9517 | 0.9718 | 0.9617 | 0.7915 |
| 0.2737 | 23.8 | 48 | 0.1534 | 0.9437 | 0.9524 | 0.9859 | 0.9689 | 0.7985 |
| 0.2278 | 24.8 | 50 | 0.1612 | 0.9375 | 0.9521 | 0.9789 | 0.9653 | 0.7950 |
| 0.2278 | 25.8 | 52 | 0.1535 | 0.9437 | 0.9586 | 0.9789 | 0.9686 | 0.8228 |
| 0.2278 | 26.8 | 54 | 0.1568 | 0.9437 | 0.9716 | 0.9648 | 0.9682 | 0.8713 |
| 0.2278 | 27.8 | 56 | 0.2107 | 0.9375 | 0.9714 | 0.9577 | 0.9645 | 0.8678 |
| 0.2278 | 28.8 | 58 | 0.1592 | 0.9313 | 0.9517 | 0.9718 | 0.9617 | 0.7915 |
| 0.2057 | 29.8 | 60 | 0.1557 | 0.9375 | 0.9648 | 0.9648 | 0.9648 | 0.8435 |
| 0.2057 | 30.8 | 62 | 0.1714 | 0.9437 | 0.9650 | 0.9718 | 0.9684 | 0.8470 |
| 0.2057 | 31.8 | 64 | 0.1571 | 0.95 | 0.9653 | 0.9789 | 0.9720 | 0.8505 |
| 0.2057 | 32.8 | 66 | 0.1574 | 0.9375 | 0.9583 | 0.9718 | 0.9650 | 0.8192 |
| 0.2057 | 33.8 | 68 | 0.1423 | 0.9563 | 0.9720 | 0.9789 | 0.9754 | 0.8783 |
| 0.2 | 34.8 | 70 | 0.1677 | 0.9437 | 0.9650 | 0.9718 | 0.9684 | 0.8470 |
| 0.2 | 35.8 | 72 | 0.1560 | 0.9375 | 0.9583 | 0.9718 | 0.9650 | 0.8192 |
| 0.2 | 36.8 | 74 | 0.1594 | 0.9375 | 0.9521 | 0.9789 | 0.9653 | 0.7950 |
| 0.2 | 37.8 | 76 | 0.1512 | 0.9437 | 0.9586 | 0.9789 | 0.9686 | 0.8228 |
| 0.2 | 38.8 | 78 | 0.1396 | 0.9563 | 0.9655 | 0.9859 | 0.9756 | 0.8541 |
| 0.1838 | 39.8 | 80 | 0.1509 | 0.9375 | 0.9583 | 0.9718 | 0.9650 | 0.8192 |
| 0.1838 | 40.8 | 82 | 0.1529 | 0.95 | 0.9718 | 0.9718 | 0.9718 | 0.8748 |
| 0.1838 | 41.8 | 84 | 0.1506 | 0.95 | 0.9653 | 0.9789 | 0.9720 | 0.8505 |
| 0.1838 | 42.8 | 86 | 0.1549 | 0.95 | 0.9653 | 0.9789 | 0.9720 | 0.8505 |
| 0.1838 | 43.8 | 88 | 0.1331 | 0.9563 | 0.9655 | 0.9859 | 0.9756 | 0.8541 |
| 0.1872 | 44.8 | 90 | 0.1409 | 0.9437 | 0.9524 | 0.9859 | 0.9689 | 0.7985 |
| 0.1872 | 45.8 | 92 | 0.1639 | 0.9375 | 0.9583 | 0.9718 | 0.9650 | 0.8192 |
| 0.1872 | 46.8 | 94 | 0.1391 | 0.95 | 0.9589 | 0.9859 | 0.9722 | 0.8263 |
| 0.1872 | 47.8 | 96 | 0.1436 | 0.9563 | 0.9655 | 0.9859 | 0.9756 | 0.8541 |
| 0.1872 | 48.8 | 98 | 0.1442 | 0.9437 | 0.9586 | 0.9789 | 0.9686 | 0.8228 |
| 0.185 | 49.8 | 100 | 0.1485 | 0.95 | 0.9653 | 0.9789 | 0.9720 | 0.8505 |
### Framework versions
- Transformers 4.24.0.dev0
- Pytorch 1.11.0+cu102
- Datasets 2.6.1
- Tokenizers 0.13.1
|
AlekseyKorshuk/amazon-reviews-input-output-6.7b
|
AlekseyKorshuk
| 2022-11-07T21:41:55Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"opt",
"text-generation",
"generated_from_trainer",
"dataset:AlekseyKorshuk/amazon-reviews-input-output",
"license:other",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-11-07T19:53:31Z |
---
license: other
tags:
- generated_from_trainer
datasets:
- AlekseyKorshuk/amazon-reviews-input-output
metrics:
- accuracy
model-index:
- name: amazon-reviews-input-output-6.7b
results:
- task:
name: Causal Language Modeling
type: text-generation
dataset:
name: AlekseyKorshuk/amazon-reviews-input-output
type: AlekseyKorshuk/amazon-reviews-input-output
metrics:
- name: Accuracy
type: accuracy
value: 0.03882113821138211
---
<!-- 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. -->
# amazon-reviews-input-output-6.7b
This model is a fine-tuned version of [facebook/opt-6.7b](https://huggingface.co/facebook/opt-6.7b) on the AlekseyKorshuk/amazon-reviews-input-output dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8574
- Accuracy: 0.0388
- Samples: 100
- Perplexity: 17.4166
- Table: <wandb.data_types.Table object at 0x7fd30eb4e940>
## 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
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 64
- total_eval_batch_size: 64
- 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 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.9912 | 0.06 | 1 | 2.7441 | 0.0404 |
| 2.9329 | 0.12 | 2 | 2.7441 | 0.0404 |
| 2.9138 | 0.19 | 3 | 2.8262 | 0.0389 |
| 2.9395 | 0.25 | 4 | 2.8262 | 0.0389 |
| 2.9109 | 0.31 | 5 | 2.7949 | 0.0399 |
| 2.8394 | 0.38 | 6 | 2.7461 | 0.0403 |
| 2.9365 | 0.44 | 7 | 2.7207 | 0.0399 |
| 2.7588 | 0.5 | 8 | 2.7070 | 0.0403 |
| 2.9751 | 0.56 | 9 | 2.6816 | 0.0407 |
| 2.844 | 0.62 | 10 | 2.6738 | 0.0404 |
| 2.731 | 0.69 | 11 | 2.6680 | 0.0406 |
| 2.7434 | 0.75 | 12 | 2.6699 | 0.0404 |
| 2.9043 | 0.81 | 13 | 2.6855 | 0.0400 |
| 2.8564 | 0.88 | 14 | 2.6855 | 0.0400 |
| 2.8716 | 0.94 | 15 | 2.6855 | 0.0400 |
| 2.896 | 1.0 | 16 | 2.6953 | 0.0398 |
| 1.9858 | 1.06 | 17 | 2.7070 | 0.0400 |
| 2.0563 | 1.12 | 18 | 2.7285 | 0.0400 |
| 2.04 | 1.19 | 19 | 2.7676 | 0.0398 |
| 1.9885 | 1.25 | 20 | 2.7910 | 0.0396 |
| 2.09 | 1.31 | 21 | 2.7969 | 0.0393 |
| 2.059 | 1.38 | 22 | 2.8105 | 0.0395 |
| 2.0498 | 1.44 | 23 | 2.7930 | 0.0398 |
| 1.9568 | 1.5 | 24 | 2.7910 | 0.0401 |
| 2.1418 | 1.56 | 25 | 2.7930 | 0.0398 |
| 1.975 | 1.62 | 26 | 2.7930 | 0.0397 |
| 1.996 | 1.69 | 27 | 2.7949 | 0.0393 |
| 1.9617 | 1.75 | 28 | 2.8047 | 0.0392 |
| 2.2062 | 1.81 | 29 | 2.8145 | 0.0388 |
| 1.9929 | 1.88 | 30 | 2.8145 | 0.0386 |
| 1.9235 | 1.94 | 31 | 2.8281 | 0.0390 |
| 1.9127 | 2.0 | 32 | 2.8574 | 0.0388 |
### Framework versions
- Transformers 4.25.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
understaters/ddpm-butterflies-128
|
understaters
| 2022-11-07T21:22:47Z | 3 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"en",
"dataset:huggan/smithsonian_butterflies_subset",
"license:apache-2.0",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2022-11-07T20:04:40Z |
---
language: en
license: apache-2.0
library_name: diffusers
tags: []
datasets: huggan/smithsonian_butterflies_subset
metrics: []
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# ddpm-butterflies-128
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library
on the `huggan/smithsonian_butterflies_subset` dataset.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training data
[TODO: describe the data used to train the model]
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- gradient_accumulation_steps: 1
- optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None
- lr_scheduler: None
- lr_warmup_steps: 500
- ema_inv_gamma: None
- ema_inv_gamma: None
- ema_inv_gamma: None
- mixed_precision: fp16
### Training results
📈 [TensorBoard logs](https://huggingface.co/understaters/ddpm-butterflies-128/tensorboard?#scalars)
|
bishalbaaniya/bishalbaaniya-finetuned-myaamia-to-english
|
bishalbaaniya
| 2022-11-07T21:15:33Z | 9 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-10-27T03:24:09Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: bishalbaaniya-finetuned-myaamia-to-english
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bishalbaaniya-finetuned-myaamia-to-english
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 4.0090
- Bleu: 0.1637
- Gen Len: 7.977
## 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 | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|
| 4.1712 | 1.0 | 1082 | 4.0090 | 0.1637 | 7.977 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2
|
ilevs/distilrubert-tiny-cased-conversational-finetuned
|
ilevs
| 2022-11-07T21:06:23Z | 109 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-11-06T14:54:50Z |
---
tags:
- generated_from_trainer
model-index:
- name: distilrubert-tiny-cased-conversational-finetuned
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilrubert-tiny-cased-conversational-finetuned
This model is a fine-tuned version of [DeepPavlov/distilrubert-tiny-cased-conversational](https://huggingface.co/DeepPavlov/distilrubert-tiny-cased-conversational) 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: 128
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
ntsema/wav2vec2-xlsr-53-espeak-cv-ft-tat-ntsema-colab
|
ntsema
| 2022-11-07T20:52:34Z | 125 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:audiofolder",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-11-07T08:05:09Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- audiofolder
metrics:
- wer
model-index:
- name: wav2vec2-xlsr-53-espeak-cv-ft-tat-ntsema-colab
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: audiofolder
type: audiofolder
config: default
split: train
args: default
metrics:
- name: Wer
type: wer
value: 0.28339140534262486
---
<!-- 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-xlsr-53-espeak-cv-ft-tat-ntsema-colab
This model is a fine-tuned version of [facebook/wav2vec2-xlsr-53-espeak-cv-ft](https://huggingface.co/facebook/wav2vec2-xlsr-53-espeak-cv-ft) on the audiofolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2976
- Wer: 0.2834
## 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: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.5013 | 3.57 | 400 | 0.4017 | 0.4837 |
| 0.3368 | 7.14 | 800 | 0.2774 | 0.3693 |
| 0.1942 | 10.71 | 1200 | 0.3054 | 0.3386 |
| 0.1449 | 14.28 | 1600 | 0.3085 | 0.3246 |
| 0.1147 | 17.85 | 2000 | 0.3134 | 0.3037 |
| 0.0944 | 21.43 | 2400 | 0.3046 | 0.2933 |
| 0.0778 | 24.99 | 2800 | 0.3057 | 0.2927 |
| 0.0643 | 28.57 | 3200 | 0.2976 | 0.2834 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.14.0.dev20221107+cu116
- Datasets 2.6.1
- Tokenizers 0.13.2
|
AlekseyKorshuk/amazon-reviews-input-output-1.3b
|
AlekseyKorshuk
| 2022-11-07T20:45:36Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"opt",
"text-generation",
"generated_from_trainer",
"dataset:AlekseyKorshuk/amazon-reviews-input-output",
"license:other",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-11-07T20:26:17Z |
---
license: other
tags:
- generated_from_trainer
datasets:
- AlekseyKorshuk/amazon-reviews-input-output
metrics:
- accuracy
model-index:
- name: amazon-reviews-input-output-1.3b
results:
- task:
name: Causal Language Modeling
type: text-generation
dataset:
name: AlekseyKorshuk/amazon-reviews-input-output
type: AlekseyKorshuk/amazon-reviews-input-output
metrics:
- name: Accuracy
type: accuracy
value: 0.03550813008130081
---
<!-- 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. -->
# amazon-reviews-input-output-1.3b
This model is a fine-tuned version of [facebook/opt-1.3b](https://huggingface.co/facebook/opt-1.3b) on the AlekseyKorshuk/amazon-reviews-input-output dataset.
It achieves the following results on the evaluation set:
- Loss: 3.5488
- Accuracy: 0.0355
- Samples: 100
- Perplexity: 34.7725
- Table: <wandb.data_types.Table object at 0x7ffa3c3fd700>
## 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
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 64
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 3.2024 | 0.06 | 1 | 2.9121 | 0.0385 |
| 3.1226 | 0.12 | 2 | 2.9121 | 0.0385 |
| 3.1321 | 0.19 | 3 | 2.8477 | 0.0394 |
| 2.9875 | 0.25 | 4 | 2.8477 | 0.0394 |
| 2.9717 | 0.31 | 5 | 2.8555 | 0.0391 |
| 2.9341 | 0.38 | 6 | 2.8438 | 0.0392 |
| 3.0376 | 0.44 | 7 | 2.8184 | 0.0396 |
| 2.8164 | 0.5 | 8 | 2.7988 | 0.0395 |
| 3.0857 | 0.56 | 9 | 2.7988 | 0.0394 |
| 2.9492 | 0.62 | 10 | 2.7969 | 0.0395 |
| 2.8633 | 0.69 | 11 | 2.7969 | 0.0395 |
| 2.8994 | 0.75 | 12 | 2.7910 | 0.0398 |
| 3.0024 | 0.81 | 13 | 2.7812 | 0.0401 |
| 2.937 | 0.88 | 14 | 2.7812 | 0.0399 |
| 2.9963 | 0.94 | 15 | 2.7812 | 0.0399 |
| 3.0168 | 1.0 | 16 | 2.7754 | 0.04 |
| 2.2589 | 1.06 | 17 | 2.7715 | 0.0397 |
| 2.2568 | 1.12 | 18 | 2.7793 | 0.0395 |
| 2.3138 | 1.19 | 19 | 2.8027 | 0.0393 |
| 2.2759 | 1.25 | 20 | 2.8184 | 0.0393 |
| 2.5137 | 1.31 | 21 | 2.8262 | 0.0390 |
| 2.2997 | 1.38 | 22 | 2.8320 | 0.0388 |
| 2.2693 | 1.44 | 23 | 2.8359 | 0.0392 |
| 2.204 | 1.5 | 24 | 2.8379 | 0.0387 |
| 2.3713 | 1.56 | 25 | 2.8359 | 0.0391 |
| 2.3448 | 1.62 | 26 | 2.8340 | 0.0391 |
| 2.217 | 1.69 | 27 | 2.8359 | 0.0391 |
| 2.3082 | 1.75 | 28 | 2.8379 | 0.0385 |
| 2.2878 | 1.81 | 29 | 2.8379 | 0.0386 |
| 2.2429 | 1.88 | 30 | 2.8379 | 0.0385 |
| 2.2838 | 1.94 | 31 | 2.8359 | 0.0385 |
| 2.4038 | 2.0 | 32 | 2.8379 | 0.0387 |
| 1.8481 | 2.06 | 33 | 2.8555 | 0.0384 |
| 1.657 | 2.12 | 34 | 2.8965 | 0.0382 |
| 1.6996 | 2.19 | 35 | 2.9590 | 0.0380 |
| 1.6741 | 2.25 | 36 | 3.0312 | 0.0379 |
| 1.594 | 2.31 | 37 | 3.0410 | 0.0380 |
| 1.5201 | 2.38 | 38 | 3.0156 | 0.0381 |
| 1.5149 | 2.44 | 39 | 3.0137 | 0.0380 |
| 1.5521 | 2.5 | 40 | 3.0176 | 0.0379 |
| 1.5364 | 2.56 | 41 | 3.0273 | 0.0378 |
| 1.5385 | 2.62 | 42 | 3.0391 | 0.0380 |
| 1.4794 | 2.69 | 43 | 3.0488 | 0.0380 |
| 1.4313 | 2.75 | 44 | 3.0527 | 0.0378 |
| 1.5071 | 2.81 | 45 | 3.0469 | 0.0378 |
| 1.4799 | 2.88 | 46 | 3.0449 | 0.0378 |
| 1.521 | 2.94 | 47 | 3.0371 | 0.0380 |
| 1.4603 | 3.0 | 48 | 3.0410 | 0.0379 |
| 1.25 | 3.06 | 49 | 3.0859 | 0.0381 |
| 1.0411 | 3.12 | 50 | 3.1797 | 0.0375 |
| 1.0385 | 3.19 | 51 | 3.2969 | 0.0371 |
| 1.0254 | 3.25 | 52 | 3.3613 | 0.0367 |
| 0.9656 | 3.31 | 53 | 3.3633 | 0.0368 |
| 1.036 | 3.38 | 54 | 3.3359 | 0.0366 |
| 0.9366 | 3.44 | 55 | 3.2949 | 0.0366 |
| 0.9712 | 3.5 | 56 | 3.2695 | 0.0367 |
| 1.0066 | 3.56 | 57 | 3.2676 | 0.0366 |
| 0.9952 | 3.62 | 58 | 3.2773 | 0.0368 |
| 1.0352 | 3.69 | 59 | 3.2891 | 0.0367 |
| 1.0212 | 3.75 | 60 | 3.3164 | 0.0362 |
| 0.9468 | 3.81 | 61 | 3.3203 | 0.0360 |
| 0.9155 | 3.88 | 62 | 3.3223 | 0.0366 |
| 0.8552 | 3.94 | 63 | 3.3262 | 0.0370 |
| 0.9575 | 4.0 | 64 | 3.3340 | 0.0370 |
| 0.6384 | 4.06 | 65 | 3.375 | 0.0370 |
| 0.6436 | 4.12 | 66 | 3.4453 | 0.0364 |
| 0.5752 | 4.19 | 67 | 3.5391 | 0.0358 |
| 0.6542 | 4.25 | 68 | 3.6016 | 0.0354 |
| 0.6724 | 4.31 | 69 | 3.6016 | 0.0354 |
| 0.591 | 4.38 | 70 | 3.5938 | 0.0359 |
| 0.5346 | 4.44 | 71 | 3.5801 | 0.0361 |
| 0.5112 | 4.5 | 72 | 3.5762 | 0.0361 |
| 0.5443 | 4.56 | 73 | 3.5840 | 0.0362 |
| 0.5689 | 4.62 | 74 | 3.6152 | 0.0358 |
| 0.5667 | 4.69 | 75 | 3.6328 | 0.0358 |
| 0.554 | 4.75 | 76 | 3.6348 | 0.0357 |
| 0.6087 | 4.81 | 77 | 3.625 | 0.0355 |
| 0.5236 | 4.88 | 78 | 3.6152 | 0.0355 |
| 0.5458 | 4.94 | 79 | 3.5781 | 0.0355 |
| 0.5702 | 5.0 | 80 | 3.5488 | 0.0355 |
### Framework versions
- Transformers 4.25.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
okho0653/distilbert-base-zero-shot
|
okho0653
| 2022-11-07T20:44:16Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-11-07T20:40:31Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-zero-shot
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-zero-shot
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.7147
- eval_accuracy: 0.0741
- eval_f1: 0.1379
- eval_runtime: 1.1794
- eval_samples_per_second: 22.894
- eval_steps_per_second: 1.696
- step: 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: 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
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2
|
okho0653/Bio_ClinicalBERT-zero-shot
|
okho0653
| 2022-11-07T20:40:03Z | 12 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-11-07T20:34:18Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: Bio_ClinicalBERT-zero-shot
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Bio_ClinicalBERT-zero-shot
This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.5417
- eval_accuracy: 1.0
- eval_f1: 1.0
- eval_runtime: 4.3261
- eval_samples_per_second: 6.241
- eval_steps_per_second: 0.462
- step: 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: 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
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2
|
Meow412/finetuning-sentiment-model-A3
|
Meow412
| 2022-11-07T20:39:27Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-11-07T20:30:47Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-A3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-model-A3
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: 0.3212
- Accuracy: 0.8760
- F1: 0.3516
## 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 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2
|
edbeeching/atari_zaxxon_3333
|
edbeeching
| 2022-11-07T20:31:59Z | 1 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-11-07T20:30:50Z |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_zaxxon
type: atari_zaxxon
metrics:
- type: mean_reward
value: 12600.00 +/- 0.00
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **atari_zaxxon** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
edbeeching/atari_wizardofwor_3333
|
edbeeching
| 2022-11-07T20:29:12Z | 4 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-11-07T20:28:16Z |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_wizardofwor
type: atari_wizardofwor
metrics:
- type: mean_reward
value: 25500.00 +/- 0.00
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **atari_wizardofwor** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
edbeeching/atari_videopinball_3333
|
edbeeching
| 2022-11-07T20:27:56Z | 1 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-11-07T20:26:42Z |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_videopinball
type: atari_videopinball
metrics:
- type: mean_reward
value: nan +/- nan
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **atari_videopinball** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
edbeeching/atari_tutankham_3333
|
edbeeching
| 2022-11-07T20:23:10Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-11-07T20:22:04Z |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_tutankham
type: atari_tutankham
metrics:
- type: mean_reward
value: nan +/- nan
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **atari_tutankham** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
edbeeching/atari_tennis_3333
|
edbeeching
| 2022-11-07T20:20:35Z | 3 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-11-07T20:19:22Z |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_tennis
type: atari_tennis
metrics:
- type: mean_reward
value: 24.00 +/- 0.00
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **atari_tennis** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
edbeeching/atari_spaceinvaders_3333
|
edbeeching
| 2022-11-07T20:17:41Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-11-07T20:16:43Z |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_spaceinvaders
type: atari_spaceinvaders
metrics:
- type: mean_reward
value: 2212.50 +/- 2.50
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **atari_spaceinvaders** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
edbeeching/atari_roadrunner_3333
|
edbeeching
| 2022-11-07T20:10:27Z | 1 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-11-07T20:09:13Z |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_roadrunner
type: atari_roadrunner
metrics:
- type: mean_reward
value: 84000.00 +/- 0.00
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **atari_roadrunner** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
edbeeching/atari_riverraid_3333
|
edbeeching
| 2022-11-07T20:08:53Z | 2 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-11-07T20:07:42Z |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_riverraid
type: atari_riverraid
metrics:
- type: mean_reward
value: 15935.00 +/- 755.00
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **atari_riverraid** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
Ananjas/AwooAI
|
Ananjas
| 2022-11-07T20:08:29Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-11-07T19:37:57Z |
---
tags:
- conversational
---
|
edbeeching/atari_pong_3333
|
edbeeching
| 2022-11-07T20:04:26Z | 3 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-11-07T20:03:30Z |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_pong
type: atari_pong
metrics:
- type: mean_reward
value: 21.00 +/- 0.00
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **atari_pong** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
edbeeching/atari_phoenix_3333
|
edbeeching
| 2022-11-07T20:01:38Z | 2 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-11-07T20:00:35Z |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: atari_phoenix
type: atari_phoenix
metrics:
- type: mean_reward
value: nan +/- nan
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **atari_phoenix** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
artemnech/dialoT5-base
|
artemnech
| 2022-11-07T18:58:36Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-08-29T10:37:48Z |
How to use:
```
from collections import deque
from bs4 import BeautifulSoup
import requests
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, T5Tokenizer
import torch
model_name = 'artemnech/dialoT5-base'
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
def generate(text, **kwargs):
model.eval()
inputs = tokenizer(text, return_tensors='pt').to(model.device)
with torch.no_grad():
hypotheses = model.generate(**inputs, **kwargs)
return tokenizer.decode(hypotheses[0], skip_special_tokens=True)
def dialog(context):
keyword = generate('keyword: ' + ' '.join(context), num_beams=2,)
knowlege = ''
if keyword != 'no_keywords':
resp = requests.get(f"https://en.wikipedia.org/wiki/{keyword}")
root = BeautifulSoup(resp.content, "html.parser")
knowlege ="knowlege: " + " ".join([_.text.strip() for _ in root.find("div", class_="mw-body-content mw-content-ltr").find_all("p", limit=2)])
answ = generate(f'dialog: ' + knowlege + ' '.join(context), num_beams=3,
do_sample=True, temperature=1.1, encoder_no_repeat_ngram_size=5,
no_repeat_ngram_size=5,
max_new_tokens = 30)
return answ
context =deque([], maxlen=4)
while True:
text = input()
text = 'user1>>: ' + text
context.append(text)
answ = dialog(context)
context.append('user2>>: ' + answ)
print('bot: ', answ)
```
|
azuresonance/bert-finetuned-ner
|
azuresonance
| 2022-11-07T18:08:45Z | 15 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-11-07T17:58:09Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: train
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9351422898742554
- name: Recall
type: recall
value: 0.9511948838774823
- name: F1
type: f1
value: 0.943100283664275
- name: Accuracy
type: accuracy
value: 0.9867251427562254
---
<!-- 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-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0604
- Precision: 0.9351
- Recall: 0.9512
- F1: 0.9431
- Accuracy: 0.9867
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0861 | 1.0 | 1756 | 0.0691 | 0.9094 | 0.9322 | 0.9206 | 0.9809 |
| 0.034 | 2.0 | 3512 | 0.0605 | 0.9303 | 0.9482 | 0.9392 | 0.9861 |
| 0.0162 | 3.0 | 5268 | 0.0604 | 0.9351 | 0.9512 | 0.9431 | 0.9867 |
### Framework versions
- Transformers 4.22.2
- Pytorch 1.11.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
versae/stt_nn-NO_conformer_transducer_large
|
versae
| 2022-11-07T17:57:43Z | 4 | 0 |
nemo
|
[
"nemo",
"region:us"
] | null | 2022-11-07T17:51:45Z |
Colab → https://colab.research.google.com/drive/1ggqsd5tu6cKf22EiKckbUNTJOwMMqKAh?usp=sharing
|
GuiGel/beto-uncased-flert-lstm-crf-meddocan
|
GuiGel
| 2022-11-07T17:09:39Z | 3 | 0 |
flair
|
[
"flair",
"pytorch",
"token-classification",
"sequence-tagger-model",
"region:us"
] |
token-classification
| 2022-11-07T17:08:40Z |
---
tags:
- flair
- token-classification
- sequence-tagger-model
---
### Demo: How to use in Flair
Requires:
- **[Flair](https://github.com/flairNLP/flair/)** (`pip install flair`)
```python
from flair.data import Sentence
from flair.models import SequenceTagger
# load tagger
tagger = SequenceTagger.load("GuiGel/beto-uncased-flert-lstm-crf-meddocan")
# make example sentence
sentence = Sentence("On September 1st George won 1 dollar while watching Game of Thrones.")
# predict NER tags
tagger.predict(sentence)
# print sentence
print(sentence)
# print predicted NER spans
print('The following NER tags are found:')
# iterate over entities and print
for entity in sentence.get_spans('ner'):
print(entity)
```
|
mqymmayy/mt5-small-finetuned-amazon-en-es
|
mqymmayy
| 2022-11-07T16:44:48Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"summarization",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
summarization
| 2022-11-07T14:21:59Z |
---
license: apache-2.0
tags:
- summarization
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mt5-small-finetuned-amazon-en-es
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-small-finetuned-amazon-en-es
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0294
- Rouge1: 16.5993
- Rouge2: 8.0138
- Rougel: 16.1315
- Rougelsum: 16.2931
## 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: 5.6e-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: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|
| 6.5928 | 1.0 | 1209 | 3.3005 | 14.7775 | 6.4604 | 14.2574 | 14.3422 |
| 3.9024 | 2.0 | 2418 | 3.1399 | 16.8632 | 8.6474 | 16.065 | 16.2114 |
| 3.5806 | 3.0 | 3627 | 3.0869 | 18.2422 | 9.2647 | 17.6227 | 17.7649 |
| 3.4201 | 4.0 | 4836 | 3.0590 | 17.7826 | 8.9742 | 16.9951 | 17.1804 |
| 3.3202 | 5.0 | 6045 | 3.0598 | 17.7808 | 8.6038 | 17.2243 | 17.4125 |
| 3.2436 | 6.0 | 7254 | 3.0409 | 16.8469 | 8.2339 | 16.3935 | 16.5818 |
| 3.2079 | 7.0 | 8463 | 3.0332 | 16.8148 | 8.2115 | 16.3166 | 16.4832 |
| 3.1801 | 8.0 | 9672 | 3.0294 | 16.5993 | 8.0138 | 16.1315 | 16.2931 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2
|
danduh/test-model
|
danduh
| 2022-11-07T16:34:50Z | 0 | 0 | null |
[
"tf",
"exbert",
"danielTheBest",
"TensorFlow",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"license:apache-2.0",
"region:us"
] | null | 2022-11-07T16:15:52Z |
---
language: en
tags:
- exbert
- danielTheBest
- TensorFlow
license: apache-2.0
datasets:
- bookcorpus
- wikipedia
---
Some cool text
and in TensorFlow:
```python
from transformers import GPT2Tokenizer, TFGPT2Model
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
model = TFGPT2Model.from_pretrained('gpt2')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
|
Rundstedtz/distilbert-base-uncased-letters-from-jenny
|
Rundstedtz
| 2022-11-07T15:35:42Z | 5 | 1 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-11-07T15:27:32Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Rundstedtz/distilbert-base-uncased-letters-from-jenny
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Rundstedtz/distilbert-base-uncased-letters-from-jenny
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 3.5319
- Validation Loss: 2.9614
- 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': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -988, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 3.5319 | 2.9614 | 0 |
### Framework versions
- Transformers 4.24.0
- TensorFlow 2.9.2
- Datasets 2.6.1
- Tokenizers 0.13.2
|
Maxter825/2
|
Maxter825
| 2022-11-07T14:16:05Z | 0 | 0 | null |
[
"license:bigscience-openrail-m",
"region:us"
] | null | 2022-11-07T14:16:05Z |
---
license: bigscience-openrail-m
---
|
cyburn/midjourney_v4_finetune
|
cyburn
| 2022-11-07T14:06:25Z | 0 | 7 | null |
[
"region:us"
] | null | 2022-11-06T23:53:48Z |
# midjourney v4 finetune
This model is based on SD1.5 with MSE VAE, finetuned on roughly 300 images created by midjourney v4 engine
Prompt: `midjourney v4, <your prompt>`
## models
- midjourney_v4-khoya-r12-e2-sd15.ckpt : epoch 2
- midjourney_v4-khoya-r12-e3-sd15.ckpt : epoch 3
|
jacquesle/bert-base-cased-NER-favsbot
|
jacquesle
| 2022-11-07T11:25:44Z | 13 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:favsbot",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-10-20T06:52:15Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- favsbot
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-cased-NER-favsbot
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: favsbot
type: favsbot
config: default
split: train
args: default
metrics:
- name: Precision
type: precision
value: 0.8571428571428571
- name: Recall
type: recall
value: 0.96
- name: F1
type: f1
value: 0.9056603773584904
- name: Accuracy
type: accuracy
value: 0.9583333333333334
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-cased-NER-favsbot
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the favsbot dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0992
- Precision: 0.8571
- Recall: 0.96
- F1: 0.9057
- Accuracy: 0.9583
## 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: 1.5e-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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 10 | 1.7643 | 0.0 | 0.0 | 0.0 | 0.5694 |
| No log | 2.0 | 20 | 1.1420 | 0.0 | 0.0 | 0.0 | 0.5833 |
| No log | 3.0 | 30 | 0.7946 | 0.9375 | 0.6 | 0.7317 | 0.8056 |
| No log | 4.0 | 40 | 0.5625 | 0.8182 | 0.72 | 0.7660 | 0.8611 |
| No log | 5.0 | 50 | 0.4217 | 0.8148 | 0.88 | 0.8462 | 0.9306 |
| No log | 6.0 | 60 | 0.3082 | 0.8519 | 0.92 | 0.8846 | 0.9444 |
| No log | 7.0 | 70 | 0.2386 | 0.8148 | 0.88 | 0.8462 | 0.9444 |
| No log | 8.0 | 80 | 0.1965 | 0.8148 | 0.88 | 0.8462 | 0.9444 |
| No log | 9.0 | 90 | 0.1626 | 0.8148 | 0.88 | 0.8462 | 0.9444 |
| No log | 10.0 | 100 | 0.1465 | 0.8571 | 0.96 | 0.9057 | 0.9583 |
| No log | 11.0 | 110 | 0.1314 | 0.8571 | 0.96 | 0.9057 | 0.9583 |
| No log | 12.0 | 120 | 0.1215 | 0.8571 | 0.96 | 0.9057 | 0.9583 |
| No log | 13.0 | 130 | 0.1160 | 0.8571 | 0.96 | 0.9057 | 0.9583 |
| No log | 14.0 | 140 | 0.1104 | 0.8571 | 0.96 | 0.9057 | 0.9583 |
| No log | 15.0 | 150 | 0.1050 | 0.8571 | 0.96 | 0.9057 | 0.9583 |
| No log | 16.0 | 160 | 0.1012 | 0.8571 | 0.96 | 0.9057 | 0.9583 |
| No log | 17.0 | 170 | 0.0997 | 0.8571 | 0.96 | 0.9057 | 0.9583 |
| No log | 18.0 | 180 | 0.0997 | 0.8571 | 0.96 | 0.9057 | 0.9583 |
| No log | 19.0 | 190 | 0.0995 | 0.8571 | 0.96 | 0.9057 | 0.9583 |
| No log | 20.0 | 200 | 0.0992 | 0.8571 | 0.96 | 0.9057 | 0.9583 |
### Framework versions
- Transformers 4.21.1
- Pytorch 1.12.1
- Datasets 2.6.1
- Tokenizers 0.12.1
|
FacVain/turkish-sentiment-XMLRoBERTa
|
FacVain
| 2022-11-07T11:19:48Z | 0 | 0 | null |
[
"tr",
"region:us"
] | null | 2022-11-07T09:57:24Z |
---
language: tr
tag: text-classification
widget:
- text: "Oldukça kullanışlı bir ürün."
---
This repository contains two models that has been finetuned on twitter-XMLRoBERTa https://huggingface.co/cardiffnlp/twitter-xlm-roberta-base.
3_Label model can classify text as positive, neutral and negative.
2_Label_Twitter is finetuned with tweets and can predict tweets as positive and negative.
|
tatakof/testpyramidsrnd
|
tatakof
| 2022-11-07T11:00:36Z | 4 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] |
reinforcement-learning
| 2022-11-07T11:00:28Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
library_name: ml-agents
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Write your model_id: franfram/testpyramidsrnd
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Odiljon/Tanjo
|
Odiljon
| 2022-11-07T10:53:44Z | 0 | 0 | null |
[
"license:bigscience-openrail-m",
"region:us"
] | null | 2022-11-07T10:53:44Z |
---
license: bigscience-openrail-m
---
|
silveto/distilbert-base-uncased-finetuned-squad
|
silveto
| 2022-11-07T10:44:30Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-11-02T17:43:38Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: distilbert-base-uncased-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-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: 1.1531
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.2297 | 1.0 | 5533 | 1.1547 |
| 0.9688 | 2.0 | 11066 | 1.1278 |
| 0.763 | 3.0 | 16599 | 1.1531 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.13.0
- Datasets 2.6.1
- Tokenizers 0.10.3
|
Shyam-311/distilroberta-base-finetuned-wikitext2
|
Shyam-311
| 2022-11-07T10:34:57Z | 164 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-11-07T10:01:02Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilroberta-base-finetuned-wikitext2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilroberta-base-finetuned-wikitext2
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8340
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 2.0843 | 1.0 | 2406 | 1.9226 |
| 1.9913 | 2.0 | 4812 | 1.8820 |
| 1.9597 | 3.0 | 7218 | 1.8214 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2
|
julien-c/avocado-prices
|
julien-c
| 2022-11-07T10:31:46Z | 0 | 1 |
mlconsole
|
[
"mlconsole",
"tabular-regression",
"dataset:nateraw/avocado-prices",
"license:apache-2.0",
"model-index",
"region:us"
] |
tabular-regression
| 2022-10-13T08:34:56Z |
---
license: apache-2.0
inference: false
tags:
- mlconsole
- tabular-regression
library_name: mlconsole
metrics:
- mae
- loss
datasets:
- nateraw/avocado-prices
model-index:
- name: avocado-prices
results:
- task:
type: tabular-regression
name: tabular-regression
dataset:
type: nateraw/avocado-prices
name: avocado.csv
metrics:
- type: mae
name: Mean absolute error
value: 0.22897861897945404
- type: loss
name: Model loss
value: 0.08849651366472244
---
# regression model trained on "nateraw/avocado-prices"
🤖 [Load and use this model](https://mlconsole.com/model/hf/julien-c/avocado-prices) in one click.
🧑💻 [Train your own model](https://mlconsole.com) on ML Console.
### Screenshots
|
nguyenkhoa2407/bert-base-cased-NER-favsbot-no-apostrophe-2022-11-07
|
nguyenkhoa2407
| 2022-11-07T10:30:30Z | 9 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:favsbot",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-11-07T10:23:28Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- favsbot
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-cased-NER-favsbot-no-apostrophe-2022-11-07
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: favsbot
type: favsbot
config: default
split: train
args: default
metrics:
- name: Precision
type: precision
value: 0.8275862068965517
- name: Recall
type: recall
value: 0.96
- name: F1
type: f1
value: 0.888888888888889
- name: Accuracy
type: accuracy
value: 0.9444444444444444
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-cased-NER-favsbot-no-apostrophe-2022-11-07
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the favsbot dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1169
- Precision: 0.8276
- Recall: 0.96
- F1: 0.8889
- Accuracy: 0.9444
## 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: 1.5e-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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 10 | 1.6302 | 0.0 | 0.0 | 0.0 | 0.5972 |
| No log | 2.0 | 20 | 1.0453 | 0.6667 | 0.08 | 0.1429 | 0.6389 |
| No log | 3.0 | 30 | 0.7286 | 0.8421 | 0.64 | 0.7273 | 0.8472 |
| No log | 4.0 | 40 | 0.5296 | 0.8 | 0.8 | 0.8000 | 0.8889 |
| No log | 5.0 | 50 | 0.3960 | 0.8214 | 0.92 | 0.8679 | 0.9306 |
| No log | 6.0 | 60 | 0.2987 | 0.8214 | 0.92 | 0.8679 | 0.9306 |
| No log | 7.0 | 70 | 0.2424 | 0.8276 | 0.96 | 0.8889 | 0.9444 |
| No log | 8.0 | 80 | 0.2151 | 0.8276 | 0.96 | 0.8889 | 0.9444 |
| No log | 9.0 | 90 | 0.1815 | 0.8276 | 0.96 | 0.8889 | 0.9444 |
| No log | 10.0 | 100 | 0.1675 | 0.8276 | 0.96 | 0.8889 | 0.9444 |
| No log | 11.0 | 110 | 0.1504 | 0.8276 | 0.96 | 0.8889 | 0.9444 |
| No log | 12.0 | 120 | 0.1410 | 0.8276 | 0.96 | 0.8889 | 0.9444 |
| No log | 13.0 | 130 | 0.1350 | 0.8276 | 0.96 | 0.8889 | 0.9444 |
| No log | 14.0 | 140 | 0.1281 | 0.8276 | 0.96 | 0.8889 | 0.9444 |
| No log | 15.0 | 150 | 0.1239 | 0.8276 | 0.96 | 0.8889 | 0.9444 |
| No log | 16.0 | 160 | 0.1190 | 0.8276 | 0.96 | 0.8889 | 0.9444 |
| No log | 17.0 | 170 | 0.1187 | 0.8276 | 0.96 | 0.8889 | 0.9444 |
| No log | 18.0 | 180 | 0.1180 | 0.8276 | 0.96 | 0.8889 | 0.9444 |
| No log | 19.0 | 190 | 0.1170 | 0.8276 | 0.96 | 0.8889 | 0.9444 |
| No log | 20.0 | 200 | 0.1169 | 0.8276 | 0.96 | 0.8889 | 0.9444 |
### Framework versions
- Transformers 4.21.1
- Pytorch 1.12.1
- Datasets 2.4.0
- Tokenizers 0.12.1
|
Shyam-311/distilgpt2-finetuned-wikitext2
|
Shyam-311
| 2022-11-07T09:55:19Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-11-07T09:08:03Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilgpt2-finetuned-wikitext2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilgpt2-finetuned-wikitext2
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.6421
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 3.7602 | 1.0 | 2334 | 3.6669 |
| 3.653 | 2.0 | 4668 | 3.6472 |
| 3.6006 | 3.0 | 7002 | 3.6421 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2
|
freepina/musika-hyperpop
|
freepina
| 2022-11-07T09:46:51Z | 0 | 0 | null |
[
"audio",
"music",
"generation",
"tensorflow",
"arxiv:2208.08706",
"license:mit",
"region:us"
] | null | 2022-11-07T09:46:18Z |
---
license: mit
tags:
- audio
- music
- generation
- tensorflow
---
# Musika Model: musika_hyperpop
## Model provided by: freepina
Pretrained musika_hyperpop model for the [Musika system](https://github.com/marcoppasini/musika) for fast infinite waveform music generation.
Introduced in [this paper](https://arxiv.org/abs/2208.08706).
## How to use
You can generate music from this pretrained musika_hyperpop model using the notebook available [here](https://colab.research.google.com/drive/1HJWliBXPi-Xlx3gY8cjFI5-xaZgrTD7r).
### Model description
This pretrained GAN system consists of a ResNet-style generator and discriminator. During training, stability is controlled by adapting the strength of gradient penalty regularization on-the-fly. The gradient penalty weighting term is contained in *switch.npy*. The generator is conditioned on a latent coordinate system to produce samples of arbitrary length. The latent representations produced by the generator are then passed to a decoder which converts them into waveform audio.
The generator has a context window of about 12 seconds of audio.
|
pig4431/Sentiment140_BERT_5E
|
pig4431
| 2022-11-07T08:46:38Z | 10 | 1 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:sentiment140",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-11-07T08:39:06Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- sentiment140
metrics:
- accuracy
model-index:
- name: Sentiment140_BERT_5E
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: sentiment140
type: sentiment140
config: sentiment140
split: train
args: sentiment140
metrics:
- name: Accuracy
type: accuracy
value: 0.82
---
<!-- 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. -->
# Sentiment140_BERT_5E
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the sentiment140 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7061
- Accuracy: 0.82
## 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
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6882 | 0.08 | 50 | 0.6047 | 0.7 |
| 0.6223 | 0.16 | 100 | 0.5137 | 0.8067 |
| 0.5463 | 0.24 | 150 | 0.4573 | 0.8067 |
| 0.4922 | 0.32 | 200 | 0.4790 | 0.8 |
| 0.4821 | 0.4 | 250 | 0.4207 | 0.8267 |
| 0.4985 | 0.48 | 300 | 0.4267 | 0.8067 |
| 0.4455 | 0.56 | 350 | 0.4301 | 0.8133 |
| 0.469 | 0.64 | 400 | 0.4294 | 0.82 |
| 0.4906 | 0.72 | 450 | 0.4059 | 0.8067 |
| 0.4006 | 0.8 | 500 | 0.4181 | 0.8133 |
| 0.445 | 0.88 | 550 | 0.3948 | 0.8267 |
| 0.4302 | 0.96 | 600 | 0.3976 | 0.84 |
| 0.4442 | 1.04 | 650 | 0.3887 | 0.8533 |
| 0.3424 | 1.12 | 700 | 0.4119 | 0.8267 |
| 0.3589 | 1.2 | 750 | 0.4083 | 0.8533 |
| 0.3737 | 1.28 | 800 | 0.4253 | 0.8333 |
| 0.334 | 1.36 | 850 | 0.4147 | 0.86 |
| 0.3637 | 1.44 | 900 | 0.3926 | 0.8533 |
| 0.3388 | 1.52 | 950 | 0.4084 | 0.8267 |
| 0.3375 | 1.6 | 1000 | 0.4132 | 0.8467 |
| 0.3725 | 1.68 | 1050 | 0.3965 | 0.8467 |
| 0.3649 | 1.76 | 1100 | 0.3956 | 0.8333 |
| 0.3799 | 1.84 | 1150 | 0.3923 | 0.8333 |
| 0.3695 | 1.92 | 1200 | 0.4266 | 0.84 |
| 0.3233 | 2.0 | 1250 | 0.4225 | 0.8333 |
| 0.2313 | 2.08 | 1300 | 0.4672 | 0.8333 |
| 0.231 | 2.16 | 1350 | 0.5212 | 0.8133 |
| 0.2526 | 2.24 | 1400 | 0.5392 | 0.8067 |
| 0.2721 | 2.32 | 1450 | 0.4895 | 0.82 |
| 0.2141 | 2.4 | 1500 | 0.5258 | 0.8133 |
| 0.2658 | 2.48 | 1550 | 0.5046 | 0.8267 |
| 0.2386 | 2.56 | 1600 | 0.4873 | 0.8267 |
| 0.2493 | 2.64 | 1650 | 0.4950 | 0.8333 |
| 0.2692 | 2.72 | 1700 | 0.5080 | 0.8267 |
| 0.2226 | 2.8 | 1750 | 0.5016 | 0.8467 |
| 0.2522 | 2.88 | 1800 | 0.5068 | 0.8267 |
| 0.2556 | 2.96 | 1850 | 0.4937 | 0.8267 |
| 0.2311 | 3.04 | 1900 | 0.5103 | 0.8267 |
| 0.1703 | 3.12 | 1950 | 0.5680 | 0.82 |
| 0.1744 | 3.2 | 2000 | 0.5501 | 0.82 |
| 0.1667 | 3.28 | 2050 | 0.6142 | 0.82 |
| 0.1863 | 3.36 | 2100 | 0.6355 | 0.82 |
| 0.2543 | 3.44 | 2150 | 0.6000 | 0.8133 |
| 0.1565 | 3.52 | 2200 | 0.6618 | 0.8267 |
| 0.1531 | 3.6 | 2250 | 0.6595 | 0.8133 |
| 0.1915 | 3.68 | 2300 | 0.6647 | 0.8267 |
| 0.1601 | 3.76 | 2350 | 0.6729 | 0.8267 |
| 0.176 | 3.84 | 2400 | 0.6699 | 0.82 |
| 0.1815 | 3.92 | 2450 | 0.6819 | 0.8067 |
| 0.1987 | 4.0 | 2500 | 0.6543 | 0.8333 |
| 0.1236 | 4.08 | 2550 | 0.6686 | 0.8333 |
| 0.1599 | 4.16 | 2600 | 0.6583 | 0.8267 |
| 0.1256 | 4.24 | 2650 | 0.6871 | 0.8267 |
| 0.1291 | 4.32 | 2700 | 0.6855 | 0.82 |
| 0.1198 | 4.4 | 2750 | 0.6901 | 0.82 |
| 0.1245 | 4.48 | 2800 | 0.7152 | 0.8267 |
| 0.1784 | 4.56 | 2850 | 0.7053 | 0.82 |
| 0.1705 | 4.64 | 2900 | 0.7016 | 0.82 |
| 0.1265 | 4.72 | 2950 | 0.7013 | 0.82 |
| 0.1192 | 4.8 | 3000 | 0.7084 | 0.82 |
| 0.174 | 4.88 | 3050 | 0.7062 | 0.82 |
| 0.1328 | 4.96 | 3100 | 0.7061 | 0.82 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
nsadeq/InformBERT
|
nsadeq
| 2022-11-07T08:42:44Z | 5 | 1 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"fill-mask",
"arxiv:2210.11771",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-11-05T23:06:20Z |
---
license: apache-2.0
---
# InformBERT
## Introduction
InformBERT is pretrained using variable masking strategy, where informative tokens are masked more frequently compared to other tokens. InformBERT outperforms random masking based pretrained models on the factual recall benchmark LAMA and extractive question answering benchmark SQuAD.
More detail: https://arxiv.org/abs/2210.11771
## How to load
```Python
from transformers import BertTokenizer, AutoModel
tokenizer = BertTokenizer.from_pretrained("nsadeq/InformBERT")
model = AutoModel.from_pretrained("nsadeq/InformBERT")
from transformers import pipeline
unmasker = pipeline('fill-mask', model='nsadeq/InformBERT',tokenizer=tokenizer)
unmasker("SpeedWeek is an American television program on [MASK].")
```
## Citation
```bibtex
@misc{https://doi.org/10.48550/arxiv.2210.11771,
doi = {10.48550/ARXIV.2210.11771},
url = {https://arxiv.org/abs/2210.11771},
author = {Sadeq, Nafis and Xu, Canwen and McAuley, Julian},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {InforMask: Unsupervised Informative Masking for Language Model Pretraining},
publisher = {arXiv},
year = {2022},
copyright = {arXiv.org perpetual, non-exclusive license}
}
```
|
bofenghuang/wav2vec2-xls-r-1b-voxpopuli-fr
|
bofenghuang
| 2022-11-07T08:40:09Z | 19 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"polinaeterna/voxpopuli",
"generated_from_trainer",
"hf-asr-leaderboard",
"robust-speech-event",
"fr",
"dataset:polinaeterna/voxpopuli",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-09-29T08:19:39Z |
---
language:
- fr
license: apache-2.0
tags:
- automatic-speech-recognition
- polinaeterna/voxpopuli
- generated_from_trainer
- hf-asr-leaderboard
- robust-speech-event
datasets:
- polinaeterna/voxpopuli
model-index:
- name: Fine-tuned Wav2Vec2 XLS-R 1B model for ASR in French
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Voxpopuli
type: polinaeterna/voxpopuli
args: fr
metrics:
- name: Test WER
type: wer
value: 11.70
- name: Test WER (+LM)
type: wer
value: 10.01
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 9
type: mozilla-foundation/common_voice_9_0
args: fr
metrics:
- name: Test WER
type: wer
value: 45.74
- name: Test WER (+LM)
type: wer
value: 38.81
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: fr
metrics:
- name: Test WER
type: wer
value: 27.86
- name: Test WER (+LM)
type: wer
value: 22.53
---
# Fine-tuned Wav2Vec2 XLS-R 1B model for ASR in French
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the POLINAETERNA/VOXPOPULI - FR dataset.
## Usage
1. To use on a local audio file without the language model
```python
import torch
import torchaudio
from transformers import AutoModelForCTC, Wav2Vec2Processor
processor = Wav2Vec2Processor.from_pretrained("bhuang/wav2vec2-xls-r-1b-voxpopuli-fr")
model = AutoModelForCTC.from_pretrained("bhuang/wav2vec2-xls-r-1b-voxpopuli-fr").cuda()
# path to your audio file
wav_path = "example.wav"
waveform, sample_rate = torchaudio.load(wav_path)
waveform = waveform.squeeze(axis=0) # mono
# resample
if sample_rate != 16_000:
resampler = torchaudio.transforms.Resample(sample_rate, 16_000)
waveform = resampler(waveform)
# normalize
input_dict = processor(waveform, sampling_rate=16_000, return_tensors="pt")
with torch.inference_mode():
logits = model(input_dict.input_values.to("cuda")).logits
# decode
predicted_ids = torch.argmax(logits, dim=-1)
predicted_sentence = processor.batch_decode(predicted_ids)[0]
```
2. To use on a local audio file with the language model
```python
import torch
import torchaudio
from transformers import AutoModelForCTC, Wav2Vec2ProcessorWithLM
processor_with_lm = Wav2Vec2ProcessorWithLM.from_pretrained("bhuang/wav2vec2-xls-r-1b-voxpopuli-fr")
model = AutoModelForCTC.from_pretrained("bhuang/wav2vec2-xls-r-1b-voxpopuli-fr").cuda()
model_sampling_rate = processor_with_lm.feature_extractor.sampling_rate
# path to your audio file
wav_path = "example.wav"
waveform, sample_rate = torchaudio.load(wav_path)
waveform = waveform.squeeze(axis=0) # mono
# resample
if sample_rate != 16_000:
resampler = torchaudio.transforms.Resample(sample_rate, 16_000)
waveform = resampler(waveform)
# normalize
input_dict = processor_with_lm(waveform, sampling_rate=16_000, return_tensors="pt")
with torch.inference_mode():
logits = model(input_dict.input_values.to("cuda")).logits
predicted_sentence = processor_with_lm.batch_decode(logits.cpu().numpy()).text[0]
```
## Evaluation
1. To evaluate on `polinaeterna/voxpopuli`
```bash
python eval.py \
--model_id "bhuang/wav2vec2-xls-r-1b-voxpopuli-fr" \
--dataset "polinaeterna/voxpopuli" \
--config "fr" \
--split "test" \
--log_outputs \
--outdir "outputs/results_polinaeterna_voxpopuli_with_lm"
```
2. To evaluate on `mozilla-foundation/common_voice_9_0`
```bash
python eval.py \
--model_id "bhuang/wav2vec2-xls-r-1b-voxpopuli-fr" \
--dataset "mozilla-foundation/common_voice_9_0" \
--config "fr" \
--split "test" \
--log_outputs \
--outdir "outputs/results_mozilla-foundatio_common_voice_9_0_with_lm"
```
3. To evaluate on `speech-recognition-community-v2/dev_data`
```bash
python eval.py \
--model_id "bhuang/wav2vec2-xls-r-1b-voxpopuli-fr" \
--dataset "speech-recognition-community-v2/dev_data" \
--config "fr" \
--split "validation" \
--chunk_length_s 5.0 \
--stride_length_s 1.0 \
--log_outputs \
--outdir "outputs/results_speech-recognition-community-v2_dev_data_with_lm"
```
|
bofenghuang/wav2vec2-xls-r-1b-cv9-fr
|
bofenghuang
| 2022-11-07T08:37:59Z | 8 | 1 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_9_0",
"generated_from_trainer",
"hf-asr-leaderboard",
"robust-speech-event",
"fr",
"dataset:common_voice",
"dataset:mozilla-foundation/common_voice_9_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-09-12T13:09:54Z |
---
language:
- fr
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_9_0
- generated_from_trainer
- hf-asr-leaderboard
- robust-speech-event
datasets:
- common_voice
- mozilla-foundation/common_voice_9_0
model-index:
- name: Fine-tuned Wav2Vec2 XLS-R 1B model for ASR in French
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 9
type: mozilla-foundation/common_voice_9_0
args: fr
metrics:
- name: Test WER
type: wer
value: 12.72
- name: Test WER (+LM)
type: wer
value: 10.60
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: fr
metrics:
- name: Test WER
type: wer
value: 24.28
- name: Test WER (+LM)
type: wer
value: 20.85
---
# Fine-tuned Wav2Vec2 XLS-R 1B model for ASR in French
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MOZILLA-FOUNDATION/COMMON_VOICE_9_0 - FR dataset.
## Usage
1. To use on a local audio file without the language model
```python
import torch
import torchaudio
from transformers import AutoModelForCTC, Wav2Vec2Processor
processor = Wav2Vec2Processor.from_pretrained("bhuang/wav2vec2-xls-r-1b-cv9-fr")
model = AutoModelForCTC.from_pretrained("bhuang/wav2vec2-xls-r-1b-cv9-fr").cuda()
# path to your audio file
wav_path = "example.wav"
waveform, sample_rate = torchaudio.load(wav_path)
waveform = waveform.squeeze(axis=0) # mono
# resample
if sample_rate != 16_000:
resampler = torchaudio.transforms.Resample(sample_rate, 16_000)
waveform = resampler(waveform)
# normalize
input_dict = processor(waveform, sampling_rate=16_000, return_tensors="pt")
with torch.inference_mode():
logits = model(input_dict.input_values.to("cuda")).logits
# decode
predicted_ids = torch.argmax(logits, dim=-1)
predicted_sentence = processor.batch_decode(predicted_ids)[0]
```
2. To use on a local audio file with the language model
```python
import torch
import torchaudio
from transformers import AutoModelForCTC, Wav2Vec2ProcessorWithLM
processor_with_lm = Wav2Vec2ProcessorWithLM.from_pretrained("bhuang/wav2vec2-xls-r-1b-cv9-fr")
model = AutoModelForCTC.from_pretrained("bhuang/wav2vec2-xls-r-1b-cv9-fr").cuda()
model_sampling_rate = processor_with_lm.feature_extractor.sampling_rate
# path to your audio file
wav_path = "example.wav"
waveform, sample_rate = torchaudio.load(wav_path)
waveform = waveform.squeeze(axis=0) # mono
# resample
if sample_rate != 16_000:
resampler = torchaudio.transforms.Resample(sample_rate, 16_000)
waveform = resampler(waveform)
# normalize
input_dict = processor_with_lm(waveform, sampling_rate=16_000, return_tensors="pt")
with torch.inference_mode():
logits = model(input_dict.input_values.to("cuda")).logits
predicted_sentence = processor_with_lm.batch_decode(logits.cpu().numpy()).text[0]
```
## Evaluation
1. To evaluate on `mozilla-foundation/common_voice_9_0`
```bash
python eval.py \
--model_id "bhuang/wav2vec2-xls-r-1b-cv9-fr" \
--dataset "mozilla-foundation/common_voice_9_0" \
--config "fr" \
--split "test" \
--log_outputs \
--outdir "outputs/results_mozilla-foundatio_common_voice_9_0_with_lm"
```
2. To evaluate on `speech-recognition-community-v2/dev_data`
```bash
python eval.py \
--model_id "bhuang/wav2vec2-xls-r-1b-cv9-fr" \
--dataset "speech-recognition-community-v2/dev_data" \
--config "fr" \
--split "validation" \
--chunk_length_s 5.0 \
--stride_length_s 1.0 \
--log_outputs \
--outdir "outputs/results_speech-recognition-community-v2_dev_data_with_lm"
```
|
ahmadRa/q-FrozenLake-v1-4x4-noSlippery-try1
|
ahmadRa
| 2022-11-07T08:04:08Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-11-07T08:04:02Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery-try1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="ahmadRa/q-FrozenLake-v1-4x4-noSlippery-try1", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
pig4431/Sentiment140_ALBERT_5E
|
pig4431
| 2022-11-07T07:45:04Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"albert",
"text-classification",
"generated_from_trainer",
"dataset:sentiment140",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-11-07T07:44:38Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- sentiment140
metrics:
- accuracy
model-index:
- name: Sentiment140_ALBERT_5E
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: sentiment140
type: sentiment140
config: sentiment140
split: train
args: sentiment140
metrics:
- name: Accuracy
type: accuracy
value: 0.8533333333333334
---
<!-- 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. -->
# Sentiment140_ALBERT_5E
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the sentiment140 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6103
- Accuracy: 0.8533
## 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
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6713 | 0.08 | 50 | 0.5704 | 0.7333 |
| 0.5742 | 0.16 | 100 | 0.4620 | 0.8 |
| 0.5104 | 0.24 | 150 | 0.5536 | 0.74 |
| 0.5313 | 0.32 | 200 | 0.5198 | 0.76 |
| 0.5023 | 0.4 | 250 | 0.4286 | 0.8 |
| 0.4871 | 0.48 | 300 | 0.4294 | 0.8267 |
| 0.4513 | 0.56 | 350 | 0.4349 | 0.8133 |
| 0.4647 | 0.64 | 400 | 0.4046 | 0.8333 |
| 0.4827 | 0.72 | 450 | 0.4218 | 0.8333 |
| 0.4517 | 0.8 | 500 | 0.4093 | 0.82 |
| 0.4417 | 0.88 | 550 | 0.3999 | 0.84 |
| 0.4701 | 0.96 | 600 | 0.3779 | 0.8867 |
| 0.397 | 1.04 | 650 | 0.3730 | 0.8667 |
| 0.3377 | 1.12 | 700 | 0.3833 | 0.8333 |
| 0.411 | 1.2 | 750 | 0.3704 | 0.84 |
| 0.3796 | 1.28 | 800 | 0.3472 | 0.86 |
| 0.3523 | 1.36 | 850 | 0.3512 | 0.8733 |
| 0.3992 | 1.44 | 900 | 0.3712 | 0.84 |
| 0.3641 | 1.52 | 950 | 0.3718 | 0.82 |
| 0.3973 | 1.6 | 1000 | 0.3508 | 0.84 |
| 0.3576 | 1.68 | 1050 | 0.3600 | 0.86 |
| 0.3701 | 1.76 | 1100 | 0.3287 | 0.8667 |
| 0.3721 | 1.84 | 1150 | 0.3794 | 0.82 |
| 0.3673 | 1.92 | 1200 | 0.3378 | 0.8733 |
| 0.4223 | 2.0 | 1250 | 0.3508 | 0.86 |
| 0.2745 | 2.08 | 1300 | 0.3835 | 0.86 |
| 0.283 | 2.16 | 1350 | 0.3500 | 0.8533 |
| 0.2769 | 2.24 | 1400 | 0.3334 | 0.8733 |
| 0.2491 | 2.32 | 1450 | 0.3519 | 0.8867 |
| 0.3237 | 2.4 | 1500 | 0.3438 | 0.86 |
| 0.2662 | 2.48 | 1550 | 0.3513 | 0.8667 |
| 0.2423 | 2.56 | 1600 | 0.3413 | 0.8867 |
| 0.2655 | 2.64 | 1650 | 0.3126 | 0.8933 |
| 0.2516 | 2.72 | 1700 | 0.3333 | 0.8733 |
| 0.252 | 2.8 | 1750 | 0.3316 | 0.88 |
| 0.2872 | 2.88 | 1800 | 0.3227 | 0.9 |
| 0.306 | 2.96 | 1850 | 0.3383 | 0.8733 |
| 0.248 | 3.04 | 1900 | 0.3474 | 0.8733 |
| 0.1507 | 3.12 | 1950 | 0.4140 | 0.8667 |
| 0.1994 | 3.2 | 2000 | 0.3729 | 0.8533 |
| 0.167 | 3.28 | 2050 | 0.3782 | 0.8867 |
| 0.1872 | 3.36 | 2100 | 0.4352 | 0.8867 |
| 0.1611 | 3.44 | 2150 | 0.4511 | 0.8667 |
| 0.2338 | 3.52 | 2200 | 0.4244 | 0.8533 |
| 0.1538 | 3.6 | 2250 | 0.4226 | 0.8733 |
| 0.1561 | 3.68 | 2300 | 0.4126 | 0.88 |
| 0.2156 | 3.76 | 2350 | 0.4382 | 0.86 |
| 0.1684 | 3.84 | 2400 | 0.4969 | 0.86 |
| 0.1917 | 3.92 | 2450 | 0.4439 | 0.8667 |
| 0.1584 | 4.0 | 2500 | 0.4759 | 0.86 |
| 0.1038 | 4.08 | 2550 | 0.5042 | 0.8667 |
| 0.0983 | 4.16 | 2600 | 0.5527 | 0.8533 |
| 0.1404 | 4.24 | 2650 | 0.5801 | 0.84 |
| 0.0844 | 4.32 | 2700 | 0.5884 | 0.86 |
| 0.1347 | 4.4 | 2750 | 0.5865 | 0.8467 |
| 0.1373 | 4.48 | 2800 | 0.5915 | 0.8533 |
| 0.1506 | 4.56 | 2850 | 0.5976 | 0.8467 |
| 0.1007 | 4.64 | 2900 | 0.6678 | 0.82 |
| 0.1311 | 4.72 | 2950 | 0.6082 | 0.8533 |
| 0.1402 | 4.8 | 3000 | 0.6180 | 0.8467 |
| 0.1363 | 4.88 | 3050 | 0.6107 | 0.8533 |
| 0.0995 | 4.96 | 3100 | 0.6103 | 0.8533 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.13.0
- Datasets 2.3.2
- Tokenizers 0.13.1
|
ntsema/wav2vec2-xlsr-53-espeak-cv-ft-sah-ntsema-colab
|
ntsema
| 2022-11-07T07:24:16Z | 132 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:audiofolder",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-11-07T04:24:31Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- audiofolder
metrics:
- wer
model-index:
- name: wav2vec2-xlsr-53-espeak-cv-ft-sah-ntsema-colab
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: audiofolder
type: audiofolder
config: default
split: train
args: default
metrics:
- name: Wer
type: wer
value: 0.2246858832224686
---
<!-- 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-xlsr-53-espeak-cv-ft-sah-ntsema-colab
This model is a fine-tuned version of [facebook/wav2vec2-xlsr-53-espeak-cv-ft](https://huggingface.co/facebook/wav2vec2-xlsr-53-espeak-cv-ft) on the audiofolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2143
- Wer: 0.2247
## 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: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 2.7431 | 5.71 | 400 | 0.2879 | 0.4054 |
| 0.1876 | 11.42 | 800 | 0.2349 | 0.3023 |
| 0.0986 | 17.14 | 1200 | 0.2248 | 0.2701 |
| 0.0737 | 22.85 | 1600 | 0.2242 | 0.2428 |
| 0.0546 | 28.57 | 2000 | 0.2143 | 0.2247 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.14.0.dev20221105+cu116
- Datasets 2.6.1
- Tokenizers 0.13.1
|
pig4431/amazonPolarity_fewshot
|
pig4431
| 2022-11-07T07:23:26Z | 2 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-11-07T07:23:13Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
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.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
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('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# 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, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 160 with parameters:
```
{'batch_size': 8, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 10,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 160,
"warmup_steps": 16,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel
(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
<!--- Describe where people can find more information -->
|
pig4431/IMDB_fewshot
|
pig4431
| 2022-11-07T06:51:38Z | 1 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-11-06T21:07:06Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
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.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
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('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# 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, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 160 with parameters:
```
{'batch_size': 8, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 10,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 160,
"warmup_steps": 16,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel
(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
<!--- Describe where people can find more information -->
|
pig4431/Sentiment140_XLNET_5E
|
pig4431
| 2022-11-07T06:22:19Z | 89 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlnet",
"text-classification",
"generated_from_trainer",
"dataset:sentiment140",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-11-07T06:20:23Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- sentiment140
metrics:
- accuracy
model-index:
- name: Sentiment140_XLNET_5E
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: sentiment140
type: sentiment140
config: sentiment140
split: train
args: sentiment140
metrics:
- name: Accuracy
type: accuracy
value: 0.84
---
<!-- 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. -->
# Sentiment140_XLNET_5E
This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on the sentiment140 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3797
- Accuracy: 0.84
## 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: 8
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6687 | 0.08 | 50 | 0.5194 | 0.76 |
| 0.5754 | 0.16 | 100 | 0.4500 | 0.7867 |
| 0.5338 | 0.24 | 150 | 0.3725 | 0.8333 |
| 0.5065 | 0.32 | 200 | 0.4093 | 0.8133 |
| 0.4552 | 0.4 | 250 | 0.3910 | 0.8267 |
| 0.5352 | 0.48 | 300 | 0.3888 | 0.82 |
| 0.415 | 0.56 | 350 | 0.3887 | 0.8267 |
| 0.4716 | 0.64 | 400 | 0.3888 | 0.84 |
| 0.4565 | 0.72 | 450 | 0.3619 | 0.84 |
| 0.4447 | 0.8 | 500 | 0.3758 | 0.8333 |
| 0.4407 | 0.88 | 550 | 0.3664 | 0.8133 |
| 0.46 | 0.96 | 600 | 0.3797 | 0.84 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.13.0
- Datasets 2.3.2
- Tokenizers 0.13.1
|
fimu-docproc-research/master_0.0.1_DoctrOcrEngine
|
fimu-docproc-research
| 2022-11-07T06:00:27Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"cz",
"endpoints_compatible",
"region:us"
] | null | 2022-11-06T20:56:46Z |
---
language: cz
---
**Optical Character Recognition made seamless & accessible to anyone, powered by PyTorch**
## Task: recognition
### Example usage:
```python
>>> from doctr.io import DocumentFile
>>> from doctr.models import ocr_predictor, from_hub
>>> img = DocumentFile.from_images(['<image_path>'])
>>> # Load your model from the hub
>>> model = from_hub('mindee/my-model')
>>> # Pass it to the predictor
>>> # If your model is a recognition model:
>>> predictor = ocr_predictor(det_arch='db_resnet50',
>>> reco_arch=model,
>>> pretrained=True)
>>> # Get your predictions
>>> res = predictor(img)
```
Training configuration and logs: https://wandb.ai/xbankov/text-recognition
### Run Configuration
{
"hf_dataset_name": "fimu-docproc-research/born_digital",
"name": "master_20221106-223158",
"epochs": 50,
"lr": 0.001,
"weight_decay": 0,
"batch_size": 512,
"input_size": 32,
"sched": "cosine",
"sample": null,
"workers": 16,
"wb": true,
"push_to_hub": "fimu-docproc-research/master_0.0.1",
"test_only": false,
"arch": "master"
}
|
tkubotake/xlm-roberta-base-finetuned-panx-fr
|
tkubotake
| 2022-11-07T04:39:39Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-11-07T02:57:03Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-fr
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
config: PAN-X.fr
split: train
args: PAN-X.fr
metrics:
- name: F1
type: f1
value: 0.8635672020287405
---
<!-- 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-fr
This model is a fine-tuned version of [tkubotake/xlm-roberta-base-finetuned-panx-de](https://huggingface.co/tkubotake/xlm-roberta-base-finetuned-panx-de) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4157
- F1: 0.8636
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.0847 | 1.0 | 191 | 0.4066 | 0.8524 |
| 0.0574 | 2.0 | 382 | 0.4025 | 0.8570 |
| 0.0333 | 3.0 | 573 | 0.4157 | 0.8636 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
jrtec/jrtec-gpt2-text-generation-quotes-jonathan-vargas
|
jrtec
| 2022-11-07T04:26:10Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"quotes",
"quote",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-11-06T03:21:37Z |
---
license: mit
tags:
- text-generation
- quotes
- quote
- generated_from_trainer
model-index:
- name: jrtec-gpt2-text-generation-quotes-jonathan-vargas
results: []
widget:
- text: "life: "
example_title: "Life quote"
- text: "death: "
example_title: "Death quote"
---
<!-- 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. -->
# jrtec-gpt2-text-generation-quotes-jonathan-vargas
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the datasetX dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7033
## 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: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.7463 | 1.71 | 500 | 0.7033 |
| 0.4281 | 3.41 | 1000 | 0.7084 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
Marve271/BartConditionalGeneration-bart-large-finetuned-insult
|
Marve271
| 2022-11-07T04:05:25Z | 182 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-11-06T19:15:18Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: BartConditionalGeneration-bart-large-finetuned-insult
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# BartConditionalGeneration-bart-large-finetuned-insult
This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.7901
## 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: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 5.6217 | 1.0 | 568 | 4.5864 |
| 4.7444 | 2.0 | 1136 | nan |
| 4.2308 | 3.0 | 1704 | 3.7590 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
salascorp/categorizacion_comercios_v_0.0.7
|
salascorp
| 2022-11-07T03:24:01Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-11-07T02:51:40Z |
---
license: apache-2.0
tags:
- text-classification
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: categorizacion_comercios_v_0.0.7
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# categorizacion_comercios_v_0.0.7
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the datasetX dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4673
- Accuracy: 0.9125
## 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: 3
### Training results
### Framework versions
- Transformers 4.23.1
- Pytorch 1.13.0+cpu
- Datasets 2.6.1
- Tokenizers 0.13.1
|
Formzu/bart-large-japanese
|
Formzu
| 2022-11-07T03:06:32Z | 6 | 1 |
transformers
|
[
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"bart",
"ja",
"dataset:wikipedia",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-10-31T06:53:19Z |
---
language:
- ja
license: mit
tags:
- bart
- pytorch
datasets:
- wikipedia
---
# bart-large-japanese
This model is converted from the original [Japanese BART Pretrained model](https://nlp.ist.i.kyoto-u.ac.jp/?BART%E6%97%A5%E6%9C%AC%E8%AA%9EPretrained%E3%83%A2%E3%83%87%E3%83%AB) released by Kyoto University.
Both the encoder and decoder outputs are identical to the original Fairseq model.
### How to use the model
The input text should be tokenized by [BartJapaneseTokenizer](https://huggingface.co/Formzu/bart-large-japanese/blob/main/tokenization_bart_japanese.py).
Tokenizer requirements:
* [Juman++](https://github.com/ku-nlp/jumanpp)
* [zenhan](https://pypi.org/project/zenhan/)
* [pyknp](https://pypi.org/project/pyknp/)
* [sentencepiece](https://pypi.org/project/sentencepiece/)
#### Simple FillMaskPipeline
```python
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, pipeline
model_name = "Formzu/bart-large-japanese"
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
masked_text = "天気が<mask>から散歩しましょう。"
fill_mask = pipeline("fill-mask", model=model, tokenizer=tokenizer)
out = fill_mask(masked_text)
print(out)
# [{'score': 0.03228279948234558, 'token': 2566, 'token_str': 'いい', 'sequence': '天気 が いい から 散歩 し ましょう 。'},
# {'score': 0.023878807201981544, 'token': 27365, 'token_str': '晴れ', 'sequence': '天気 が 晴れ から 散歩 し ましょう 。'},
# {'score': 0.020059829577803612, 'token': 267, 'token_str': '南', 'sequence': '天気 が 南 から 散歩 し ましょう 。'},
# {'score': 0.013921134173870087, 'token': 17, 'token_str': 'な', 'sequence': '天気 が な から 散歩 し ましょう 。'},
# {'score': 0.013069136068224907, 'token': 1718, 'token_str': 'よく', 'sequence': '天気 が よく から 散歩 し ましょう 。'}]
```
#### Text Generation
```python
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
import torch
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
model_name = "Formzu/bart-large-japanese"
model = AutoModelForSeq2SeqLM.from_pretrained(model_name).to(device)
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
masked_text = "天気が<mask>から散歩しましょう。"
inp = tokenizer(masked_text, return_tensors='pt').to(device)
out = model.generate(**inp, num_beams=1, min_length=0, max_length=20, early_stopping=True, no_repeat_ngram_size=2)
res = "".join(tokenizer.decode(out.squeeze(0).tolist(), skip_special_tokens=True).split(" "))
print(res)
# 天気がいいから散歩しましょう。天気のいいへやから、ここから
```
### Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1+cu116
- Tokenizers 0.12.1
|
huggingtweets/h3xenbrenner2-s4m31p4n-tallbart
|
huggingtweets
| 2022-11-07T00:22:34Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-11-07T00:22:25Z |
---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1396839225249734657/GG6ve7Qv_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1542608466077855744/a0q2rR-P_400x400.png')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1529675700772302848/uXtYNx_v_400x400.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">h b & very tall bart & ppigg</div>
<div style="text-align: center; font-size: 14px;">@h3xenbrenner2-s4m31p4n-tallbart</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from h b & very tall bart & ppigg.
| Data | h b | very tall bart | ppigg |
| --- | --- | --- | --- |
| Tweets downloaded | 1230 | 3194 | 3008 |
| Retweets | 75 | 381 | 957 |
| Short tweets | 155 | 569 | 643 |
| Tweets kept | 1000 | 2244 | 1408 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/34qe4a18/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @h3xenbrenner2-s4m31p4n-tallbart's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/kg3j88xz) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/kg3j88xz/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/h3xenbrenner2-s4m31p4n-tallbart')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/gleampt2-h3xenbrenner2-kidddozer
|
huggingtweets
| 2022-11-06T23:40:24Z | 97 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-11-06T23:39:09Z |
---
language: en
thumbnail: http://www.huggingtweets.com/gleampt2-h3xenbrenner2-kidddozer/1667778020169/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1396839225249734657/GG6ve7Qv_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1509747695795118080/Vz0be-8x_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1380052646178996227/fmYX0h3D_400x400.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">h b & Pepper Boy & gleam</div>
<div style="text-align: center; font-size: 14px;">@gleampt2-h3xenbrenner2-kidddozer</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from h b & Pepper Boy & gleam.
| Data | h b | Pepper Boy | gleam |
| --- | --- | --- | --- |
| Tweets downloaded | 1231 | 2848 | 2305 |
| Retweets | 75 | 690 | 196 |
| Short tweets | 155 | 442 | 170 |
| Tweets kept | 1001 | 1716 | 1939 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/336sqi28/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @gleampt2-h3xenbrenner2-kidddozer's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/hhg4q0io) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/hhg4q0io/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/gleampt2-h3xenbrenner2-kidddozer')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
lewtun/distilhubert-finetuned-gtzan
|
lewtun
| 2022-11-06T21:05:59Z | 31 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"hubert",
"audio-classification",
"hf-course",
"generated_from_trainer",
"dataset:marsyas/gtzan",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2022-03-14T17:10:18Z |
---
license: apache-2.0
tags:
- hf-course
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: distilhubert-finetuned-gtzan
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilhubert-finetuned-gtzan
This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6694
- Accuracy: 0.82
## 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
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.99 | 56 | 1.9426 | 0.5 |
| No log | 1.99 | 112 | 1.4157 | 0.63 |
| No log | 2.99 | 168 | 1.1351 | 0.69 |
| No log | 3.99 | 224 | 1.0285 | 0.72 |
| No log | 4.99 | 280 | 0.8538 | 0.79 |
| No log | 5.99 | 336 | 0.8015 | 0.74 |
| No log | 6.99 | 392 | 0.6694 | 0.82 |
| No log | 7.99 | 448 | 0.6779 | 0.79 |
| 1.0811 | 8.99 | 504 | 0.6414 | 0.81 |
| 1.0811 | 9.99 | 560 | 0.6443 | 0.82 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.11.0
- Datasets 2.6.1
- Tokenizers 0.11.6
|
pig4431/amazonPolarity_DistilBERT_5E
|
pig4431
| 2022-11-06T20:58:38Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:amazon_polarity",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-11-06T20:54:32Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- amazon_polarity
metrics:
- accuracy
model-index:
- name: amazonPolarity_DistilBERT_5EE
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: amazon_polarity
type: amazon_polarity
config: amazon_polarity
split: train
args: amazon_polarity
metrics:
- name: Accuracy
type: accuracy
value: 0.94
---
<!-- 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. -->
# amazonPolarity_DistilBERT_5EE
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the amazon_polarity dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2899
- Accuracy: 0.94
## 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
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6581 | 0.03 | 50 | 0.5315 | 0.84 |
| 0.4321 | 0.05 | 100 | 0.2897 | 0.8933 |
| 0.298 | 0.08 | 150 | 0.3165 | 0.8667 |
| 0.2902 | 0.11 | 200 | 0.2552 | 0.9067 |
| 0.2824 | 0.13 | 250 | 0.2277 | 0.9133 |
| 0.2522 | 0.16 | 300 | 0.1998 | 0.94 |
| 0.2781 | 0.19 | 350 | 0.1933 | 0.94 |
| 0.2668 | 0.21 | 400 | 0.2316 | 0.92 |
| 0.2619 | 0.24 | 450 | 0.1968 | 0.9333 |
| 0.2446 | 0.27 | 500 | 0.1846 | 0.9467 |
| 0.2677 | 0.29 | 550 | 0.1818 | 0.94 |
| 0.2026 | 0.32 | 600 | 0.2348 | 0.9133 |
| 0.2351 | 0.35 | 650 | 0.2127 | 0.92 |
| 0.2685 | 0.37 | 700 | 0.1792 | 0.94 |
| 0.2141 | 0.4 | 750 | 0.2252 | 0.9133 |
| 0.2193 | 0.43 | 800 | 0.2131 | 0.9267 |
| 0.2456 | 0.45 | 850 | 0.2205 | 0.9133 |
| 0.2548 | 0.48 | 900 | 0.1788 | 0.94 |
| 0.2353 | 0.51 | 950 | 0.1954 | 0.9267 |
| 0.2546 | 0.53 | 1000 | 0.1815 | 0.9333 |
| 0.2583 | 0.56 | 1050 | 0.1654 | 0.9333 |
| 0.219 | 0.59 | 1100 | 0.1760 | 0.9467 |
| 0.2241 | 0.61 | 1150 | 0.2107 | 0.92 |
| 0.2201 | 0.64 | 1200 | 0.2381 | 0.8933 |
| 0.1745 | 0.67 | 1250 | 0.1944 | 0.92 |
| 0.2698 | 0.69 | 1300 | 0.1971 | 0.9267 |
| 0.214 | 0.72 | 1350 | 0.1944 | 0.9333 |
| 0.2436 | 0.75 | 1400 | 0.2079 | 0.92 |
| 0.2318 | 0.77 | 1450 | 0.2088 | 0.9333 |
| 0.2206 | 0.8 | 1500 | 0.1875 | 0.94 |
| 0.2593 | 0.83 | 1550 | 0.1797 | 0.9267 |
| 0.1908 | 0.85 | 1600 | 0.1924 | 0.9333 |
| 0.2378 | 0.88 | 1650 | 0.1649 | 0.9267 |
| 0.2332 | 0.91 | 1700 | 0.1768 | 0.94 |
| 0.2125 | 0.93 | 1750 | 0.2276 | 0.92 |
| 0.2174 | 0.96 | 1800 | 0.2035 | 0.9333 |
| 0.19 | 0.99 | 1850 | 0.1805 | 0.94 |
| 0.1515 | 1.01 | 1900 | 0.1832 | 0.94 |
| 0.1671 | 1.04 | 1950 | 0.1902 | 0.94 |
| 0.171 | 1.07 | 2000 | 0.2468 | 0.9267 |
| 0.1495 | 1.09 | 2050 | 0.2276 | 0.9267 |
| 0.1535 | 1.12 | 2100 | 0.1926 | 0.94 |
| 0.2085 | 1.15 | 2150 | 0.1878 | 0.94 |
| 0.1395 | 1.17 | 2200 | 0.1795 | 0.9467 |
| 0.1556 | 1.2 | 2250 | 0.1554 | 0.9467 |
| 0.1273 | 1.23 | 2300 | 0.1707 | 0.94 |
| 0.1873 | 1.25 | 2350 | 0.1867 | 0.9467 |
| 0.1589 | 1.28 | 2400 | 0.2089 | 0.9333 |
| 0.1426 | 1.31 | 2450 | 0.1797 | 0.9467 |
| 0.149 | 1.33 | 2500 | 0.1991 | 0.9333 |
| 0.1535 | 1.36 | 2550 | 0.2116 | 0.94 |
| 0.1671 | 1.39 | 2600 | 0.1704 | 0.9467 |
| 0.1582 | 1.41 | 2650 | 0.1843 | 0.94 |
| 0.1393 | 1.44 | 2700 | 0.1831 | 0.94 |
| 0.1474 | 1.47 | 2750 | 0.1895 | 0.94 |
| 0.203 | 1.49 | 2800 | 0.1843 | 0.9467 |
| 0.1562 | 1.52 | 2850 | 0.2060 | 0.9467 |
| 0.1886 | 1.55 | 2900 | 0.1837 | 0.94 |
| 0.1332 | 1.57 | 2950 | 0.1920 | 0.9467 |
| 0.1519 | 1.6 | 3000 | 0.1789 | 0.9533 |
| 0.1354 | 1.63 | 3050 | 0.1974 | 0.9467 |
| 0.125 | 1.65 | 3100 | 0.1890 | 0.9533 |
| 0.2044 | 1.68 | 3150 | 0.1755 | 0.9533 |
| 0.1746 | 1.71 | 3200 | 0.1607 | 0.9467 |
| 0.1981 | 1.73 | 3250 | 0.1613 | 0.9533 |
| 0.1276 | 1.76 | 3300 | 0.1825 | 0.96 |
| 0.1935 | 1.79 | 3350 | 0.1707 | 0.9533 |
| 0.1848 | 1.81 | 3400 | 0.1697 | 0.96 |
| 0.1596 | 1.84 | 3450 | 0.1581 | 0.9667 |
| 0.1797 | 1.87 | 3500 | 0.1634 | 0.96 |
| 0.1493 | 1.89 | 3550 | 0.1614 | 0.9533 |
| 0.1703 | 1.92 | 3600 | 0.1673 | 0.9467 |
| 0.1951 | 1.95 | 3650 | 0.1589 | 0.9533 |
| 0.1582 | 1.97 | 3700 | 0.1761 | 0.9467 |
| 0.1974 | 2.0 | 3750 | 0.1918 | 0.94 |
| 0.1056 | 2.03 | 3800 | 0.2063 | 0.94 |
| 0.1109 | 2.05 | 3850 | 0.2031 | 0.9467 |
| 0.113 | 2.08 | 3900 | 0.2118 | 0.9467 |
| 0.0834 | 2.11 | 3950 | 0.1974 | 0.9533 |
| 0.1434 | 2.13 | 4000 | 0.2075 | 0.9533 |
| 0.0691 | 2.16 | 4050 | 0.2178 | 0.9533 |
| 0.1144 | 2.19 | 4100 | 0.2383 | 0.9467 |
| 0.1446 | 2.21 | 4150 | 0.2207 | 0.9533 |
| 0.172 | 2.24 | 4200 | 0.2034 | 0.9467 |
| 0.1026 | 2.27 | 4250 | 0.2048 | 0.9467 |
| 0.1131 | 2.29 | 4300 | 0.2334 | 0.9467 |
| 0.121 | 2.32 | 4350 | 0.2367 | 0.9333 |
| 0.1144 | 2.35 | 4400 | 0.2313 | 0.9467 |
| 0.1089 | 2.37 | 4450 | 0.2352 | 0.9533 |
| 0.1193 | 2.4 | 4500 | 0.2440 | 0.94 |
| 0.0689 | 2.43 | 4550 | 0.2379 | 0.9333 |
| 0.1799 | 2.45 | 4600 | 0.2354 | 0.9467 |
| 0.1068 | 2.48 | 4650 | 0.2158 | 0.9533 |
| 0.0974 | 2.51 | 4700 | 0.2456 | 0.94 |
| 0.0637 | 2.53 | 4750 | 0.2191 | 0.9333 |
| 0.1125 | 2.56 | 4800 | 0.2390 | 0.9467 |
| 0.1706 | 2.59 | 4850 | 0.2407 | 0.94 |
| 0.1533 | 2.61 | 4900 | 0.2242 | 0.9533 |
| 0.1357 | 2.64 | 4950 | 0.2119 | 0.9533 |
| 0.1342 | 2.67 | 5000 | 0.2268 | 0.9467 |
| 0.0796 | 2.69 | 5050 | 0.2450 | 0.9467 |
| 0.1351 | 2.72 | 5100 | 0.2499 | 0.94 |
| 0.1285 | 2.75 | 5150 | 0.2252 | 0.94 |
| 0.1563 | 2.77 | 5200 | 0.2191 | 0.94 |
| 0.1022 | 2.8 | 5250 | 0.2256 | 0.9533 |
| 0.11 | 2.83 | 5300 | 0.2365 | 0.9467 |
| 0.0926 | 2.85 | 5350 | 0.2206 | 0.9467 |
| 0.1043 | 2.88 | 5400 | 0.2018 | 0.9533 |
| 0.1041 | 2.91 | 5450 | 0.2268 | 0.9467 |
| 0.1232 | 2.93 | 5500 | 0.2164 | 0.9467 |
| 0.1537 | 2.96 | 5550 | 0.1956 | 0.9533 |
| 0.1188 | 2.99 | 5600 | 0.2126 | 0.9467 |
| 0.0749 | 3.01 | 5650 | 0.2249 | 0.9467 |
| 0.062 | 3.04 | 5700 | 0.2254 | 0.9467 |
| 0.0755 | 3.07 | 5750 | 0.2472 | 0.94 |
| 0.0866 | 3.09 | 5800 | 0.2569 | 0.94 |
| 0.0502 | 3.12 | 5850 | 0.2481 | 0.9467 |
| 0.1158 | 3.15 | 5900 | 0.2457 | 0.94 |
| 0.0413 | 3.17 | 5950 | 0.2500 | 0.94 |
| 0.0966 | 3.2 | 6000 | 0.2851 | 0.9333 |
| 0.0613 | 3.23 | 6050 | 0.2717 | 0.9467 |
| 0.1029 | 3.25 | 6100 | 0.2714 | 0.94 |
| 0.0833 | 3.28 | 6150 | 0.2683 | 0.94 |
| 0.0928 | 3.31 | 6200 | 0.2490 | 0.9467 |
| 0.0571 | 3.33 | 6250 | 0.2575 | 0.9533 |
| 0.1252 | 3.36 | 6300 | 0.2599 | 0.9467 |
| 0.0788 | 3.39 | 6350 | 0.2522 | 0.9467 |
| 0.0862 | 3.41 | 6400 | 0.2489 | 0.9533 |
| 0.112 | 3.44 | 6450 | 0.2452 | 0.9533 |
| 0.0868 | 3.47 | 6500 | 0.2438 | 0.9533 |
| 0.0979 | 3.49 | 6550 | 0.2474 | 0.94 |
| 0.0739 | 3.52 | 6600 | 0.2508 | 0.94 |
| 0.0786 | 3.55 | 6650 | 0.2621 | 0.94 |
| 0.0872 | 3.57 | 6700 | 0.2543 | 0.9333 |
| 0.0962 | 3.6 | 6750 | 0.2347 | 0.9467 |
| 0.124 | 3.63 | 6800 | 0.2319 | 0.9533 |
| 0.0747 | 3.65 | 6850 | 0.2448 | 0.9533 |
| 0.0591 | 3.68 | 6900 | 0.2379 | 0.94 |
| 0.1049 | 3.71 | 6950 | 0.2493 | 0.9333 |
| 0.0772 | 3.73 | 7000 | 0.2429 | 0.94 |
| 0.071 | 3.76 | 7050 | 0.2558 | 0.94 |
| 0.1116 | 3.79 | 7100 | 0.2600 | 0.94 |
| 0.1199 | 3.81 | 7150 | 0.2480 | 0.94 |
| 0.0819 | 3.84 | 7200 | 0.2506 | 0.94 |
| 0.1054 | 3.87 | 7250 | 0.2431 | 0.94 |
| 0.09 | 3.89 | 7300 | 0.2582 | 0.9333 |
| 0.0936 | 3.92 | 7350 | 0.2460 | 0.94 |
| 0.0469 | 3.95 | 7400 | 0.2509 | 0.94 |
| 0.1101 | 3.97 | 7450 | 0.2545 | 0.9467 |
| 0.1077 | 4.0 | 7500 | 0.2640 | 0.9467 |
| 0.0777 | 4.03 | 7550 | 0.2709 | 0.94 |
| 0.0777 | 4.05 | 7600 | 0.2842 | 0.94 |
| 0.0847 | 4.08 | 7650 | 0.2649 | 0.94 |
| 0.0462 | 4.11 | 7700 | 0.2702 | 0.9467 |
| 0.0572 | 4.13 | 7750 | 0.2628 | 0.94 |
| 0.0435 | 4.16 | 7800 | 0.2689 | 0.9467 |
| 0.0566 | 4.19 | 7850 | 0.2727 | 0.9467 |
| 0.1149 | 4.21 | 7900 | 0.2635 | 0.9467 |
| 0.0557 | 4.24 | 7950 | 0.2665 | 0.9467 |
| 0.061 | 4.27 | 8000 | 0.2680 | 0.9467 |
| 0.0664 | 4.29 | 8050 | 0.2767 | 0.9467 |
| 0.0481 | 4.32 | 8100 | 0.2662 | 0.9467 |
| 0.0893 | 4.35 | 8150 | 0.2677 | 0.9467 |
| 0.0855 | 4.37 | 8200 | 0.2733 | 0.9467 |
| 0.0552 | 4.4 | 8250 | 0.2589 | 0.94 |
| 0.0469 | 4.43 | 8300 | 0.2733 | 0.94 |
| 0.0633 | 4.45 | 8350 | 0.2799 | 0.94 |
| 0.0629 | 4.48 | 8400 | 0.2838 | 0.94 |
| 0.0854 | 4.51 | 8450 | 0.2837 | 0.94 |
| 0.0596 | 4.53 | 8500 | 0.2808 | 0.94 |
| 0.0579 | 4.56 | 8550 | 0.2839 | 0.94 |
| 0.0508 | 4.59 | 8600 | 0.2844 | 0.94 |
| 0.0557 | 4.61 | 8650 | 0.2833 | 0.94 |
| 0.0383 | 4.64 | 8700 | 0.2878 | 0.94 |
| 0.0554 | 4.67 | 8750 | 0.2924 | 0.94 |
| 0.0681 | 4.69 | 8800 | 0.2868 | 0.94 |
| 0.065 | 4.72 | 8850 | 0.2888 | 0.94 |
| 0.0731 | 4.75 | 8900 | 0.2946 | 0.94 |
| 0.0638 | 4.77 | 8950 | 0.2886 | 0.94 |
| 0.043 | 4.8 | 9000 | 0.2867 | 0.94 |
| 0.0658 | 4.83 | 9050 | 0.2872 | 0.94 |
| 0.0249 | 4.85 | 9100 | 0.2882 | 0.94 |
| 0.0612 | 4.88 | 9150 | 0.2902 | 0.94 |
| 0.0271 | 4.91 | 9200 | 0.2890 | 0.94 |
| 0.0308 | 4.93 | 9250 | 0.2897 | 0.94 |
| 0.0896 | 4.96 | 9300 | 0.2898 | 0.94 |
| 0.1172 | 4.99 | 9350 | 0.2899 | 0.94 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
keith97/bert-small2bert-small-finetuned-cnn_daily_mail-summarization-newsroom-filtered
|
keith97
| 2022-11-06T20:26:08Z | 113 | 0 |
transformers
|
[
"transformers",
"pytorch",
"encoder-decoder",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-11-06T20:10:36Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: bert-small2bert-small-finetuned-cnn_daily_mail-summarization-newsroom-filtered
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-small2bert-small-finetuned-cnn_daily_mail-summarization-newsroom-filtered
This model is a fine-tuned version of [mrm8488/bert-small2bert-small-finetuned-cnn_daily_mail-summarization](https://huggingface.co/mrm8488/bert-small2bert-small-finetuned-cnn_daily_mail-summarization) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.5413
- Rouge1: 32.3232
- Rouge2: 20.9203
- Rougel: 27.232
- Rougelsum: 29.345
- Gen Len: 72.2217
## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
- mixed_precision_training: Native AMP
- label_smoothing_factor: 0.1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 3.796 | 0.89 | 405 | 3.6945 | 29.7168 | 17.6705 | 24.4204 | 26.484 | 69.6847 |
| 3.6426 | 1.78 | 810 | 3.5532 | 32.3051 | 20.8789 | 27.1724 | 29.384 | 72.3695 |
| 3.2645 | 2.66 | 1215 | 3.5437 | 32.2016 | 20.758 | 27.083 | 29.0954 | 73.3892 |
| 3.1719 | 3.55 | 1620 | 3.5377 | 32.5493 | 21.083 | 27.0881 | 29.4691 | 71.5222 |
| 2.9763 | 4.44 | 2025 | 3.5413 | 32.3232 | 20.9203 | 27.232 | 29.345 | 72.2217 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
andrewkroening/GalaxyFarAway-DialoGPT-LeiaOrgana
|
andrewkroening
| 2022-11-06T20:13:52Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"en",
"license:cc",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-11-06T20:12:57Z |
---
language: en
tags:
- conversational
license: cc
---
# GPT-2
This model is based on a GPT-2 model which was fine-tuned on a Hugging Face dataset. It is intended largely as an illustrative example and is not intended to be used for any serious purpose. It's trained on a movie script for goodness' sake.
Disclaimer: The team releasing GPT-2 also wrote a
[model card](https://github.com/openai/gpt-2/blob/master/model_card.md) for their model. Content from this model card
has been written by the Hugging Face team to complete the information they provided and give specific examples of bias.
## Acknowledgements
There are several sources of inspiration and insight for the project that spawned this model. I'd like to recognize them up front:
* The [Microsoft DialoGPT-Medium](https://huggingface.co/microsoft/DialoGPT-medium?text=Hi.) model page was very insightful for getting stated.
* Lynn Zheng [r3dhummingbird](https://huggingface.co/r3dhummingbird/DialoGPT-medium-joshua?text=Hey+my+name+is+Thomas%21+How+are+you%3F) put together one heck of an awesome tutorial on how to fine-tune GPT-2 for conversational purposes. I used her tutorial as a starting point for this project. Check out the [Github repo here.](https://github.com/RuolinZheng08/twewy-discord-chatbot)
* [This article](https://towardsdatascience.com/make-your-own-rick-sanchez-bot-with-transformers-and-dialogpt-fine-tuning-f85e6d1f4e30) was also very insightful. Written by Rostyslav Neskorozhenyi.
* From a lineage standpoint, it looks like Nathan Cooper kicked this whole thing off with this [notebook.](https://github.com/ncoop57/i-am-a-nerd/blob/master/_notebooks/2020-05-12-chatbot-part-1.ipynb)
* Noah Gift figured out a few of the big pieces in [this repository.](https://github.com/nogibjj/hugging-face-tutorial-practice)
* I'd be remiss if I also didn't mention Hugging Face's own support [documentation](https://huggingface.co/transformers/v2.0.0/examples.html#gpt-2-gpt-and-causal-language-modeling) and team. All around great.
## Model description
This model uses GPT-2 Medium as a base model and was fine-tuned using scripts from the original (and best) Star Wars Trilogy. In this particular case, it was fine-tuned on Leia Organa's 220-some lines. This is not a lot, and thus the model should not be assumed to have serious integrity. It's just a fun little project.
## Intended uses & limitations
This model is intended to be used for fun and entertainment. Don't take it too seriously.
### Ways to use
You can always chat with the model directly on the Hugging Face website. Just click the "Chat" button on the right side of the model page.
If you want to use the model in your own project, I recommend you train it better using much more data.
To access the GitHub repository I used to train this model, click [here](https://github.com/nogibjj/hugging-face-gpt-trainer/tree/gpt-fine-tune)
## Fine-tuning data
The script to generate this model takes a Hugging Face data set in this approximate format:
| Speaker | Text |
| --- | --- |
| Luke | Hello there. |
| Han | General Kenobi. |
| Luke | You are a bold one. |
The script then asks the user to define parameters for making the dataset and proceeding to fine-tuning. The actual dataset for this model can be found [here.](andrewkroening/Star-wars-scripts-dialogue-IV-VI)
|
andrewkroening/GalaxyFarAway-DialoGPT-LukeSkywalker
|
andrewkroening
| 2022-11-06T19:50:52Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"en",
"license:cc",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-11-06T19:48:55Z |
---
language: en
tags:
- conversational
license: cc
---
# GPT-2
This model is based on a GPT-2 model which was fine-tuned on a Hugging Face dataset. It is intended largely as an illustrative example and is not intended to be used for any serious purpose. It's trained on a movie script for goodness' sake.
Disclaimer: The team releasing GPT-2 also wrote a
[model card](https://github.com/openai/gpt-2/blob/master/model_card.md) for their model. Content from this model card
has been written by the Hugging Face team to complete the information they provided and give specific examples of bias.
## Acknowledgements
There are several sources of inspiration and insight for the project that spawned this model. I'd like to recognize them up front:
* The [Microsoft DialoGPT-Medium](https://huggingface.co/microsoft/DialoGPT-medium?text=Hi.) model page was very insightful for getting stated.
* Lynn Zheng [r3dhummingbird](https://huggingface.co/r3dhummingbird/DialoGPT-medium-joshua?text=Hey+my+name+is+Thomas%21+How+are+you%3F) put together one heck of an awesome tutorial on how to fine-tune GPT-2 for conversational purposes. I used her tutorial as a starting point for this project. Check out the [Github repo here.](https://github.com/RuolinZheng08/twewy-discord-chatbot)
* [This article](https://towardsdatascience.com/make-your-own-rick-sanchez-bot-with-transformers-and-dialogpt-fine-tuning-f85e6d1f4e30) was also very insightful. Written by Rostyslav Neskorozhenyi.
* From a lineage standpoint, it looks like Nathan Cooper kicked this whole thing off with this [notebook.](https://github.com/ncoop57/i-am-a-nerd/blob/master/_notebooks/2020-05-12-chatbot-part-1.ipynb)
* Noah Gift figured out a few of the big pieces in [this repository.](https://github.com/nogibjj/hugging-face-tutorial-practice)
* I'd be remiss if I also didn't mention Hugging Face's own support [documentation](https://huggingface.co/transformers/v2.0.0/examples.html#gpt-2-gpt-and-causal-language-modeling) and team. All around great.
## Model description
This model uses GPT-2 Medium as a base model and was fine-tuned using scripts from the original (and best) Star Wars Trilogy. In this particular case, it was fine-tuned on Luke Skywalker's 490-some lines. This is not a lot, and thus the model should not be assumed to have serious integrity. It's just a fun little project.
## Intended uses & limitations
This model is intended to be used for fun and entertainment. Don't take it too seriously.
### Ways to use
You can always chat with the model directly on the Hugging Face website. Just click the "Chat" button on the right side of the model page.
If you want to use the model in your own project, I recommend you train it better using much more data.
To access the GitHub repository I used to train this model, click [here](https://github.com/nogibjj/hugging-face-gpt-trainer/tree/gpt-fine-tune)
## Fine-tuning data
The script to generate this model takes a Hugging Face data set in this approximate format:
| Speaker | Text |
| --- | --- |
| Luke | Hello there. |
| Han | General Kenobi. |
| Luke | You are a bold one. |
The script then asks the user to define parameters for making the dataset and proceeding to fine-tuning. The actual dataset for this model can be found [here.](andrewkroening/Star-wars-scripts-dialogue-IV-VI)
|
sd-concepts-library/terraria-style
|
sd-concepts-library
| 2022-11-06T18:59:29Z | 0 | 12 | null |
[
"license:mit",
"region:us"
] | null | 2022-11-06T18:59:25Z |
---
license: mit
---
### terraria style on Stable Diffusion
This is the `<terr-sty>` 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 a `style`:
|
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