modelId
string | author
string | last_modified
timestamp[us, tz=UTC] | downloads
int64 | likes
int64 | library_name
string | tags
sequence | pipeline_tag
string | createdAt
timestamp[us, tz=UTC] | card
string |
|---|---|---|---|---|---|---|---|---|---|
kornosk/bert-election2020-twitter-stance-biden | kornosk | 2022-05-02T22:59:23Z | 135 | 2 | transformers | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"twitter",
"stance-detection",
"election2020",
"politics",
"en",
"license:gpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-02T23:29:05Z | ---
language: "en"
tags:
- twitter
- stance-detection
- election2020
- politics
license: "gpl-3.0"
---
# Pre-trained BERT on Twitter US Election 2020 for Stance Detection towards Joe Biden (f-BERT)
Pre-trained weights for **f-BERT** in [Knowledge Enhance Masked Language Model for Stance Detection](https://www.aclweb.org/anthology/2021.naacl-main.376), NAACL 2021.
# Training Data
This model is pre-trained on over 5 million English tweets about the 2020 US Presidential Election. Then fine-tuned using our [stance-labeled data](https://github.com/GU-DataLab/stance-detection-KE-MLM) for stance detection towards Joe Biden.
# Training Objective
This model is initialized with BERT-base and trained with normal MLM objective with classification layer fine-tuned for stance detection towards Joe Biden.
# Usage
This pre-trained language model is fine-tuned to the stance detection task specifically for Joe Biden.
Please see the [official repository](https://github.com/GU-DataLab/stance-detection-KE-MLM) for more detail.
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
import numpy as np
# choose GPU if available
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# select mode path here
pretrained_LM_path = "kornosk/bert-election2020-twitter-stance-biden"
# load model
tokenizer = AutoTokenizer.from_pretrained(pretrained_LM_path)
model = AutoModelForSequenceClassification.from_pretrained(pretrained_LM_path)
id2label = {
0: "AGAINST",
1: "FAVOR",
2: "NONE"
}
##### Prediction Neutral #####
sentence = "Hello World."
inputs = tokenizer(sentence.lower(), return_tensors="pt")
outputs = model(**inputs)
predicted_probability = torch.softmax(outputs[0], dim=1)[0].tolist()
print("Sentence:", sentence)
print("Prediction:", id2label[np.argmax(predicted_probability)])
print("Against:", predicted_probability[0])
print("Favor:", predicted_probability[1])
print("Neutral:", predicted_probability[2])
##### Prediction Favor #####
sentence = "Go Go Biden!!!"
inputs = tokenizer(sentence.lower(), return_tensors="pt")
outputs = model(**inputs)
predicted_probability = torch.softmax(outputs[0], dim=1)[0].tolist()
print("Sentence:", sentence)
print("Prediction:", id2label[np.argmax(predicted_probability)])
print("Against:", predicted_probability[0])
print("Favor:", predicted_probability[1])
print("Neutral:", predicted_probability[2])
##### Prediction Against #####
sentence = "Biden is the worst."
inputs = tokenizer(sentence.lower(), return_tensors="pt")
outputs = model(**inputs)
predicted_probability = torch.softmax(outputs[0], dim=1)[0].tolist()
print("Sentence:", sentence)
print("Prediction:", id2label[np.argmax(predicted_probability)])
print("Against:", predicted_probability[0])
print("Favor:", predicted_probability[1])
print("Neutral:", predicted_probability[2])
# please consider citing our paper if you feel this is useful :)
```
# Reference
- [Knowledge Enhance Masked Language Model for Stance Detection](https://www.aclweb.org/anthology/2021.naacl-main.376), NAACL 2021.
# Citation
```bibtex
@inproceedings{kawintiranon2021knowledge,
title={Knowledge Enhanced Masked Language Model for Stance Detection},
author={Kawintiranon, Kornraphop and Singh, Lisa},
booktitle={Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies},
year={2021},
publisher={Association for Computational Linguistics},
url={https://www.aclweb.org/anthology/2021.naacl-main.376}
}
``` |
kornosk/bert-election2020-twitter-stance-trump | kornosk | 2022-05-02T22:59:13Z | 64 | 3 | transformers | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"twitter",
"stance-detection",
"election2020",
"politics",
"en",
"license:gpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-02T23:29:05Z | ---
language: "en"
tags:
- twitter
- stance-detection
- election2020
- politics
license: "gpl-3.0"
---
# Pre-trained BERT on Twitter US Election 2020 for Stance Detection towards Donald Trump (f-BERT)
Pre-trained weights for **f-BERT** in [Knowledge Enhance Masked Language Model for Stance Detection](https://www.aclweb.org/anthology/2021.naacl-main.376), NAACL 2021.
# Training Data
This model is pre-trained on over 5 million English tweets about the 2020 US Presidential Election. Then fine-tuned using our [stance-labeled data](https://github.com/GU-DataLab/stance-detection-KE-MLM) for stance detection towards Donald Trump.
# Training Objective
This model is initialized with BERT-base and trained with normal MLM objective with classification layer fine-tuned for stance detection towards Donald Trump.
# Usage
This pre-trained language model is fine-tuned to the stance detection task specifically for Donald Trump.
Please see the [official repository](https://github.com/GU-DataLab/stance-detection-KE-MLM) for more detail.
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
import numpy as np
# choose GPU if available
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# select mode path here
pretrained_LM_path = "kornosk/bert-election2020-twitter-stance-trump"
# load model
tokenizer = AutoTokenizer.from_pretrained(pretrained_LM_path)
model = AutoModelForSequenceClassification.from_pretrained(pretrained_LM_path)
id2label = {
0: "AGAINST",
1: "FAVOR",
2: "NONE"
}
##### Prediction Neutral #####
sentence = "Hello World."
inputs = tokenizer(sentence.lower(), return_tensors="pt")
outputs = model(**inputs)
predicted_probability = torch.softmax(outputs[0], dim=1)[0].tolist()
print("Sentence:", sentence)
print("Prediction:", id2label[np.argmax(predicted_probability)])
print("Against:", predicted_probability[0])
print("Favor:", predicted_probability[1])
print("Neutral:", predicted_probability[2])
##### Prediction Favor #####
sentence = "Go Go Trump!!!"
inputs = tokenizer(sentence.lower(), return_tensors="pt")
outputs = model(**inputs)
predicted_probability = torch.softmax(outputs[0], dim=1)[0].tolist()
print("Sentence:", sentence)
print("Prediction:", id2label[np.argmax(predicted_probability)])
print("Against:", predicted_probability[0])
print("Favor:", predicted_probability[1])
print("Neutral:", predicted_probability[2])
##### Prediction Against #####
sentence = "Trump is the worst."
inputs = tokenizer(sentence.lower(), return_tensors="pt")
outputs = model(**inputs)
predicted_probability = torch.softmax(outputs[0], dim=1)[0].tolist()
print("Sentence:", sentence)
print("Prediction:", id2label[np.argmax(predicted_probability)])
print("Against:", predicted_probability[0])
print("Favor:", predicted_probability[1])
print("Neutral:", predicted_probability[2])
# please consider citing our paper if you feel this is useful :)
```
# Reference
- [Knowledge Enhance Masked Language Model for Stance Detection](https://www.aclweb.org/anthology/2021.naacl-main.376), NAACL 2021.
# Citation
```bibtex
@inproceedings{kawintiranon2021knowledge,
title={Knowledge Enhanced Masked Language Model for Stance Detection},
author={Kawintiranon, Kornraphop and Singh, Lisa},
booktitle={Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies},
year={2021},
publisher={Association for Computational Linguistics},
url={https://www.aclweb.org/anthology/2021.naacl-main.376}
}
``` |
kornosk/bert-election2020-twitter-stance-biden-KE-MLM | kornosk | 2022-05-02T22:58:37Z | 26 | 3 | transformers | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"twitter",
"stance-detection",
"election2020",
"politics",
"en",
"license:gpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-02T23:29:05Z | ---
language: "en"
tags:
- twitter
- stance-detection
- election2020
- politics
license: "gpl-3.0"
---
# Pre-trained BERT on Twitter US Election 2020 for Stance Detection towards Joe Biden (KE-MLM)
Pre-trained weights for **KE-MLM model** in [Knowledge Enhance Masked Language Model for Stance Detection](https://www.aclweb.org/anthology/2021.naacl-main.376), NAACL 2021.
# Training Data
This model is pre-trained on over 5 million English tweets about the 2020 US Presidential Election. Then fine-tuned using our [stance-labeled data](https://github.com/GU-DataLab/stance-detection-KE-MLM) for stance detection towards Joe Biden.
# Training Objective
This model is initialized with BERT-base and trained with normal MLM objective with classification layer fine-tuned for stance detection towards Joe Biden.
# Usage
This pre-trained language model is fine-tuned to the stance detection task specifically for Joe Biden.
Please see the [official repository](https://github.com/GU-DataLab/stance-detection-KE-MLM) for more detail.
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
import numpy as np
# choose GPU if available
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# select mode path here
pretrained_LM_path = "kornosk/bert-election2020-twitter-stance-biden-KE-MLM"
# load model
tokenizer = AutoTokenizer.from_pretrained(pretrained_LM_path)
model = AutoModelForSequenceClassification.from_pretrained(pretrained_LM_path)
id2label = {
0: "AGAINST",
1: "FAVOR",
2: "NONE"
}
##### Prediction Neutral #####
sentence = "Hello World."
inputs = tokenizer(sentence.lower(), return_tensors="pt")
outputs = model(**inputs)
predicted_probability = torch.softmax(outputs[0], dim=1)[0].tolist()
print("Sentence:", sentence)
print("Prediction:", id2label[np.argmax(predicted_probability)])
print("Against:", predicted_probability[0])
print("Favor:", predicted_probability[1])
print("Neutral:", predicted_probability[2])
##### Prediction Favor #####
sentence = "Go Go Biden!!!"
inputs = tokenizer(sentence.lower(), return_tensors="pt")
outputs = model(**inputs)
predicted_probability = torch.softmax(outputs[0], dim=1)[0].tolist()
print("Sentence:", sentence)
print("Prediction:", id2label[np.argmax(predicted_probability)])
print("Against:", predicted_probability[0])
print("Favor:", predicted_probability[1])
print("Neutral:", predicted_probability[2])
##### Prediction Against #####
sentence = "Biden is the worst."
inputs = tokenizer(sentence.lower(), return_tensors="pt")
outputs = model(**inputs)
predicted_probability = torch.softmax(outputs[0], dim=1)[0].tolist()
print("Sentence:", sentence)
print("Prediction:", id2label[np.argmax(predicted_probability)])
print("Against:", predicted_probability[0])
print("Favor:", predicted_probability[1])
print("Neutral:", predicted_probability[2])
# please consider citing our paper if you feel this is useful :)
```
# Reference
- [Knowledge Enhance Masked Language Model for Stance Detection](https://www.aclweb.org/anthology/2021.naacl-main.376), NAACL 2021.
# Citation
```bibtex
@inproceedings{kawintiranon2021knowledge,
title={Knowledge Enhanced Masked Language Model for Stance Detection},
author={Kawintiranon, Kornraphop and Singh, Lisa},
booktitle={Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies},
year={2021},
publisher={Association for Computational Linguistics},
url={https://www.aclweb.org/anthology/2021.naacl-main.376}
}
``` |
huggingtweets/usrsistakenhelp | huggingtweets | 2022-05-02T22:26:31Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-05-02T22:25:02Z | ---
language: en
thumbnail: http://www.huggingtweets.com/usrsistakenhelp/1651530363067/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/1520487753896665088/lO1PwH2q_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Rosa - I miss tgamm</div>
<div style="text-align: center; font-size: 14px;">@usrsistakenhelp</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 Rosa - I miss tgamm.
| Data | Rosa - I miss tgamm |
| --- | --- |
| Tweets downloaded | 3244 |
| Retweets | 507 |
| Short tweets | 1160 |
| Tweets kept | 1577 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/jxrwgo01/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 @usrsistakenhelp's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1z4w7mpe) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1z4w7mpe/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/usrsistakenhelp')
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)
|
caush/Clickbait4 | caush | 2022-05-02T20:39:40Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-05-02T20:24:42Z | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: Clickbait1
results: []
---
This model is a fine-tuned version of microsoft/Multilingual-MiniLM-L12-H384 on the Webis-Clickbait-17 dataset. It achieves the following results on the evaluation set:
Loss: 0.0261
The following list presents the current performances achieved by the participants. As primary evaluation measure, Mean Squared Error (MSE) with respect to the mean judgments of the annotators is used. Our result is 0,0261 for the MSE metric. We do not compute the other metrics. We try not to cheat using unknown data at the time of the challenge. We do not use k-fold cross validation techniques.
| team | MSE | F1 | Precision | Recall| Accuracy| Runtime |
|----- |----- |--- |-----------|-------|---------|-------- |
|goldfish | 0.024 | 0.741 | 0.739 | 0.742 | 0.876 | 16:20:21|
|caush | 0.026 | | | | | 00:11:00|
|monkfish | 0.026 | 0.694 | 0.785 | 0.622 | 0.870 | 03:41:35|
|dartfish | 0.027 | 0.706 | 0.733 | 0.681 | 0.865 | 00:47:07|
|torpedo19 | 0.03 | 0.677 | 0.755 | 0.614 | 0.861 | 00:52:44|
|albacore | 0.031 | 0.67 | 0.731 | 0.62 | 0.855 | 00:01:10|
|blobfish | 0.032 | 0.646 | 0.738 | 0.574 | 0.85 | 00:03:22|
|zingel | 0.033 | 0.683 | 0.719 | 0.65 | 0.856 | 00:03:27|
|anchovy | 0.034 | 0.68 | 0.717 | 0.645 | 0.855 | 00:07:20|
|ray | 0.034 | 0.684 | 0.691 | 0.677 | 0.851 | 00:29:28|
|icarfish | 0.035 | 0.621 | 0.768 | 0.522 | 0.849 | 01:02:57|
|emperor | 0.036 | 0.641 | 0.714 | 0.581 | 0.845 | 00:04:03|
|carpetshark | 0.036 | 0.638 | 0.728 | 0.568 | 0.847 | 00:08:05|
|electriceel | 0.038 | 0.588 | 0.727 | 0.493 | 0.835 | 01:04:54|
|arowana | 0.039 | 0.656 | 0.659 | 0.654 | 0.837 | 00:35:24|
|pineapplefish | 0.041 | 0.631 | 0.642 | 0.621 | 0.827 | 00:54:28|
|whitebait | 0.043 | 0.565 | 0.7 | 0.474 | 0.826 | 00:04:31| |
caush/Clickbait1 | caush | 2022-05-02T20:36:10Z | 110 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-04-26T18:25:39Z | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: Clickbait1
results: []
---
# Clickbait1
This model is a fine-tuned version of [microsoft/Multilingual-MiniLM-L12-H384](https://huggingface.co/microsoft/Multilingual-MiniLM-L12-H384) on the [Webis-Clickbait-17](https://zenodo.org/record/5530410) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0257
## Model description
MiniLM is a distilled model from the paper "MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers".
We fine tune this model to evaluate (regression) the clickbait level of title news.
## Intended uses & limitations
Model looks like the model described in the paper [Predicting Clickbait Strength in Online Social Media](https://aclanthology.org/2020.coling-main.425/) by Indurthi Vijayasaradhi, Syed Bakhtiyar, Gupta Manish, Varma Vasudeva.
The model was trained with english titles.
## Training and evaluation data
We trained the model with the official training data for the chalenge (clickbait17-train-170630.zip (894 MiB, 19538 posts), plus another set that was just available after the end of the challenge (clickbait17-train-170331.zip (157 MiB, 2459 posts).
## Training procedure
Code can be find in [Github](https://github.com/caush/Clickbait).
### 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: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 0.05 | 50 | 0.0571 |
| No log | 0.09 | 100 | 0.0448 |
| No log | 0.14 | 150 | 0.0391 |
| No log | 0.18 | 200 | 0.0326 |
| No log | 0.23 | 250 | 0.0343 |
| No log | 0.27 | 300 | 0.0343 |
| No log | 0.32 | 350 | 0.0343 |
| No log | 0.36 | 400 | 0.0346 |
| No log | 0.41 | 450 | 0.0343 |
| 0.0388 | 0.46 | 500 | 0.0297 |
| 0.0388 | 0.5 | 550 | 0.0293 |
| 0.0388 | 0.55 | 600 | 0.0301 |
| 0.0388 | 0.59 | 650 | 0.0290 |
| 0.0388 | 0.64 | 700 | 0.0326 |
| 0.0388 | 0.68 | 750 | 0.0285 |
| 0.0388 | 0.73 | 800 | 0.0285 |
| 0.0388 | 0.77 | 850 | 0.0275 |
| 0.0388 | 0.82 | 900 | 0.0314 |
| 0.0388 | 0.87 | 950 | 0.0309 |
| 0.0297 | 0.91 | 1000 | 0.0277 |
| 0.0297 | 0.96 | 1050 | 0.0281 |
| 0.0297 | 1.0 | 1100 | 0.0273 |
| 0.0297 | 1.05 | 1150 | 0.0270 |
| 0.0297 | 1.09 | 1200 | 0.0291 |
| 0.0297 | 1.14 | 1250 | 0.0293 |
| 0.0297 | 1.18 | 1300 | 0.0269 |
| 0.0297 | 1.23 | 1350 | 0.0276 |
| 0.0297 | 1.28 | 1400 | 0.0279 |
| 0.0297 | 1.32 | 1450 | 0.0267 |
| 0.0265 | 1.37 | 1500 | 0.0270 |
| 0.0265 | 1.41 | 1550 | 0.0300 |
| 0.0265 | 1.46 | 1600 | 0.0274 |
| 0.0265 | 1.5 | 1650 | 0.0274 |
| 0.0265 | 1.55 | 1700 | 0.0266 |
| 0.0265 | 1.59 | 1750 | 0.0267 |
| 0.0265 | 1.64 | 1800 | 0.0267 |
| 0.0265 | 1.68 | 1850 | 0.0280 |
| 0.0265 | 1.73 | 1900 | 0.0274 |
| 0.0265 | 1.78 | 1950 | 0.0272 |
| 0.025 | 1.82 | 2000 | 0.0261 |
| 0.025 | 1.87 | 2050 | 0.0268 |
| 0.025 | 1.91 | 2100 | 0.0268 |
| 0.025 | 1.96 | 2150 | 0.0259 |
| 0.025 | 2.0 | 2200 | 0.0257 |
| 0.025 | 2.05 | 2250 | 0.0260 |
| 0.025 | 2.09 | 2300 | 0.0263 |
| 0.025 | 2.14 | 2350 | 0.0262 |
| 0.025 | 2.19 | 2400 | 0.0269 |
| 0.025 | 2.23 | 2450 | 0.0262 |
| 0.0223 | 2.28 | 2500 | 0.0262 |
| 0.0223 | 2.32 | 2550 | 0.0267 |
| 0.0223 | 2.37 | 2600 | 0.0260 |
| 0.0223 | 2.41 | 2650 | 0.0260 |
| 0.0223 | 2.46 | 2700 | 0.0259 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0a0+17540c5
- Datasets 2.1.0
- Tokenizers 0.12.1
|
ali2066/DistilBERT_FINAL_ctxSentence_TRAIN_editorials_TEST_NULL_second_train_set_null_False | ali2066 | 2022-05-02T18:29:59Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-05-02T18:27:39Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: DistilBERT_FINAL_ctxSentence_TRAIN_editorials_TEST_NULL_second_train_set_null_False
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_FINAL_ctxSentence_TRAIN_editorials_TEST_NULL_second_train_set_null_False
This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 4.8119
- Precision: 0.2752
- Recall: 0.9522
- F1: 0.4270
- Accuracy: 0.2849
## 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: 32
- eval_batch_size: 32
- 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 | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 166 | 0.0726 | 0.9827 | 1.0 | 0.9913 | 0.9828 |
| No log | 2.0 | 332 | 0.0569 | 0.9827 | 1.0 | 0.9913 | 0.9828 |
| No log | 3.0 | 498 | 0.0434 | 0.9884 | 1.0 | 0.9942 | 0.9885 |
| 0.1021 | 4.0 | 664 | 0.0505 | 0.9884 | 1.0 | 0.9942 | 0.9885 |
| 0.1021 | 5.0 | 830 | 0.0472 | 0.9884 | 1.0 | 0.9942 | 0.9885 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1+cu113
- Datasets 1.18.0
- Tokenizers 0.10.3
|
LACAI/roberta-large-adapted-PFG-progression | LACAI | 2022-05-02T18:28:47Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-05-02T18:09:17Z | ---
license: mit
---
Base model: [lacai/roberta-large-dialog-narrative](https://huggingface.co/lacai/roberta-large-dialog-narrative)
Fine tuned as a progression model (to predict the acceptability of a dialogue) on the [Persuasion For Good Dataset](https://gitlab.com/ucdavisnlp/persuasionforgood) (Wang et al., 2019):
Given a complete dialogue from (or in the style of) Persuasion For Good, the task is to predict a numeric score typically in the range (-3, 3) where a higher score means a more acceptable dialogue in context of the donation solicitation task.
This model inherits a special dialogue token `<d>` from its base model, which indicates the start of a dialogue utterance.
**Example input**: `<d>How are you?</s><d>Good! how about yourself?</s><d>Great. Would you like to donate today to help the children?</s>`
For more context and usage information see [https://github.rpi.edu/LACAI/dialogue-progression](https://github.rpi.edu/LACAI/dialogue-progression). |
ali2066/DistilBERT_FINAL_ctxSentence_TRAIN_essays_TEST_NULL_second_train_set_null_False | ali2066 | 2022-05-02T18:23:52Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-05-02T18:22:28Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: DistilBERT_FINAL_ctxSentence_TRAIN_essays_TEST_NULL_second_train_set_null_False
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_FINAL_ctxSentence_TRAIN_essays_TEST_NULL_second_train_set_null_False
This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7321
- Precision: 0.9795
- Recall: 0.7277
- F1: 0.835
- Accuracy: 0.7208
## 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: 32
- eval_batch_size: 32
- 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 | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 130 | 0.3755 | 0.8521 | 0.9910 | 0.9163 | 0.8529 |
| No log | 2.0 | 260 | 0.3352 | 0.8875 | 0.9638 | 0.9241 | 0.8713 |
| No log | 3.0 | 390 | 0.3370 | 0.8918 | 0.9321 | 0.9115 | 0.8529 |
| 0.4338 | 4.0 | 520 | 0.3415 | 0.8957 | 0.9321 | 0.9135 | 0.8566 |
| 0.4338 | 5.0 | 650 | 0.3416 | 0.8918 | 0.9321 | 0.9115 | 0.8529 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1+cu113
- Datasets 1.18.0
- Tokenizers 0.10.3
|
espnet/tamil_slu | espnet | 2022-05-02T18:09:16Z | 1 | 0 | espnet | [
"espnet",
"audio",
"automatic-speech-recognition",
"dataset:tamil",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | automatic-speech-recognition | 2022-05-02T18:00:45Z | ---
tags:
- espnet
- audio
- automatic-speech-recognition
language: noinfo
datasets:
- tamil
license: cc-by-4.0
---
## ESPnet2 ASR model
### `espnet/tamil_slu`
This model was trained by Sujay S Kumar using tamil recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 395bda6123ae268f991e5ef1dab887b6e677974a
pip install -e .
cd egs2/tamil/asr1
./run.sh --skip_data_prep false --skip_train true --download_model espnet/tamil_slu
```
<!-- Generated by scripts/utils/show_asr_result.sh -->
# RESULTS
## Environments
- date: `Sun Oct 3 20:59:46 EDT 2021`
- python version: `3.9.5 (default, Jun 4 2021, 12:28:51) [GCC 7.5.0]`
- espnet version: `espnet 0.10.3a3`
- pytorch version: `pytorch 1.8.1+cu102`
- Git hash: `b41391336042a4876e30d9fe5c66afb4e4be404c`
- Commit date: `Wed Sep 22 10:02:03 2021 -0400`
## asr_train_asr_wav2vec2_xlsr_raw_word
### WER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|inference_asr_model_valid.acc.ave_5best/test|80|372|70.4|22.6|7.0|3.2|32.8|56.3|
|inference_asr_model_valid.acc.ave_5best/valid|80|372|70.4|22.6|7.0|3.2|32.8|56.3|
### CER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|inference_asr_model_valid.acc.ave_5best/test|80|3234|85.9|8.2|5.9|5.5|19.6|56.3|
|inference_asr_model_valid.acc.ave_5best/valid|80|3234|85.9|8.2|5.9|5.5|19.6|56.3|
### TER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
## ASR config
<details><summary>expand</summary>
```
config: conf/train_asr_wav2vec2_xlsr.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp_train_asr_wav2vec2_xlsr/asr_train_asr_wav2vec2_xlsr_raw_word
ngpu: 1
seed: 0
num_workers: 1
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: null
dist_rank: null
local_rank: 0
dist_master_addr: null
dist_master_port: null
dist_launcher: null
multiprocessing_distributed: false
unused_parameters: false
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
collect_stats: false
write_collected_feats: false
max_epoch: 250
patience: null
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - train
- loss
- min
- - valid
- loss
- min
- - train
- acc
- max
- - valid
- acc
- max
keep_nbest_models: 5
grad_clip: 5.0
grad_clip_type: 2.0
grad_noise: false
accum_grad: 1
no_forward_run: false
resume: true
train_dtype: float32
use_amp: false
log_interval: null
use_tensorboard: true
use_wandb: false
wandb_project: null
wandb_id: null
wandb_entity: null
wandb_name: null
wandb_model_log_interval: -1
detect_anomaly: false
pretrain_path: null
init_param: []
ignore_init_mismatch: false
freeze_param:
- frontend.upstream
num_iters_per_epoch: null
batch_size: 20
valid_batch_size: null
batch_bins: 1000000
valid_batch_bins: null
train_shape_file:
- exp_train_asr_wav2vec2_xlsr/asr_stats_raw_word/train/speech_shape
- exp_train_asr_wav2vec2_xlsr/asr_stats_raw_word/train/text_shape.word
valid_shape_file:
- exp_train_asr_wav2vec2_xlsr/asr_stats_raw_word/valid/speech_shape
- exp_train_asr_wav2vec2_xlsr/asr_stats_raw_word/valid/text_shape.word
batch_type: folded
valid_batch_type: null
fold_length:
- 80000
- 150
sort_in_batch: descending
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
train_data_path_and_name_and_type:
- - dump/raw/train/wav.scp
- speech
- sound
- - dump/raw/train/text
- text
- text
valid_data_path_and_name_and_type:
- - dump/raw/valid/wav.scp
- speech
- sound
- - dump/raw/valid/text
- text
- text
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
optim: adam
optim_conf:
lr: 0.0001
scheduler: warmuplr
scheduler_conf:
warmup_steps: 5000
token_list:
- <blank>
- <unk>
- காசு
- வேணும்
- Request_Acc_balance
- Account
- Money_deposit
- Money_withdraw
- Credit_card_payments
- card
- மீதி
- Money_transfer
- எவ்வளோ
- Bill_payments
- Credit
- கட்ட
- எவ்வளவு
- காச
- கட்டவேணும்
- இந்த
- Balance
- வேண்டும்
- போடோணும்
- கணக்கு
- செய்ய
- Bill
- போட
- account
- மாத்த
- credit
- pay
- பண்ணோணும்
- Deposit
- மீளெடுக்க
- வைப்பு
- எடுக்கவேணும்
- ல
- இருக்கிற
- எடுக்கணும்
- இல
- இருந்து
- மற்ற
- accountக்கு
- balance
- என்ன
- bill
- அ
- ஒருக்கா
- ஏலுமோ
- deposit
- பண்ண
- payment
- Account-la
- காசெடுக்கோணும்
- அனுப்பவேணும்
- காசெடுக்க
- இன்னொரு
- கு
- Cash
- <sos/eos>
init: null
input_size: null
ctc_conf:
dropout_rate: 0.0
ctc_type: builtin
reduce: true
ignore_nan_grad: true
model_conf:
ctc_weight: 0.3
lsm_weight: 0.1
length_normalized_loss: false
extract_feats_in_collect_stats: false
use_preprocessor: true
token_type: word
bpemodel: null
non_linguistic_symbols: null
cleaner: null
g2p: null
speech_volume_normalize: null
rir_scp: null
rir_apply_prob: 1.0
noise_scp: null
noise_apply_prob: 1.0
noise_db_range: '13_15'
frontend: s3prl
frontend_conf:
frontend_conf:
upstream: wav2vec2_xlsr
download_dir: ./hub
multilayer_feature: true
fs: 16k
specaug: specaug
specaug_conf:
apply_time_warp: true
time_warp_window: 5
time_warp_mode: bicubic
apply_freq_mask: true
freq_mask_width_range:
- 0
- 30
num_freq_mask: 2
apply_time_mask: true
time_mask_width_range:
- 0
- 40
num_time_mask: 2
normalize: utterance_mvn
normalize_conf: {}
preencoder: linear
preencoder_conf:
input_size: 1024
output_size: 80
encoder: conformer
encoder_conf:
output_size: 512
attention_heads: 8
linear_units: 2048
num_blocks: 12
dropout_rate: 0.1
positional_dropout_rate: 0.1
attention_dropout_rate: 0.1
input_layer: conv2d
normalize_before: true
macaron_style: true
pos_enc_layer_type: rel_pos
selfattention_layer_type: rel_selfattn
activation_type: swish
use_cnn_module: true
cnn_module_kernel: 31
postencoder: null
postencoder_conf: {}
decoder: transformer
decoder_conf:
attention_heads: 8
linear_units: 2048
num_blocks: 4
dropout_rate: 0.1
positional_dropout_rate: 0.1
self_attention_dropout_rate: 0.1
src_attention_dropout_rate: 0.1
required:
- output_dir
- token_list
version: 0.10.3a3
distributed: false
```
</details>
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
wpatena/PB-Chlamy | wpatena | 2022-05-02T16:34:01Z | 0 | 0 | null | [
"region:us"
] | null | 2022-04-12T22:35:19Z | These are files for the trained protein localization prediction model PB-Chlamy, created for the paper **"A Chloroplast Protein Atlas Reveals Novel Structures and Spatial Organization of Biosynthetic Pathways"** by Lianyong Wang, Weronika Patena, Kelly A. Van Baalen, Yihua Xie, Emily R. Singer, Sophia Gavrilenko, Michelle Warren-Williams, Linqu Han, Henry Harrigan, Vivian Chen, Vinh Ton, Saw Kyin, Henry H. Shwe, Matthew H. Cahn, Alexandra Wilson, Jianping Hu, Christoph Benning, Danny J. Schnell, Claire D. McWhite, Martin Jonikas (submitted for publication in May 2022). |
espnet/thai_commonvoice_blstm | espnet | 2022-05-02T15:53:53Z | 4 | 0 | espnet | [
"espnet",
"audio",
"automatic-speech-recognition",
"th",
"dataset:commonvoice",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | automatic-speech-recognition | 2022-05-02T15:16:52Z | ---
tags:
- espnet
- audio
- automatic-speech-recognition
language: th
datasets:
- commonvoice
license: cc-by-4.0
---
## ESPnet2 ASR model
### `espnet/thai_commonvoice_blstm`
This model was trained by dzeinali using commonvoice recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 716eb8f92e19708acfd08ba3bd39d40890d3a84b
pip install -e .
cd egs2/commonvoice/asr1
./run.sh --skip_data_prep false --skip_train true --download_model espnet/thai_commonvoice_blstm
```
<!-- Generated by scripts/utils/show_asr_result.sh -->
# RESULTS
## Environments
- date: `Mon Apr 18 11:05:12 EDT 2022`
- python version: `3.9.5 (default, Jun 4 2021, 12:28:51) [GCC 7.5.0]`
- espnet version: `espnet 0.10.6a1`
- pytorch version: `pytorch 1.8.1+cu102`
- Git hash: `5e6e95d087af8a7a4c33c4248b75114237eae64b`
- Commit date: `Mon Apr 4 21:04:45 2022 -0400`
## asr_train_asr_rnn_raw_th_bpe150_sp
### WER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_rnn_asr_model_valid.acc.ave/test_th|10769|14356|49.0|43.1|7.9|5.1|56.0|53.5|
### CER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_rnn_asr_model_valid.acc.ave/test_th|10769|348793|95.2|3.0|1.8|1.8|6.6|53.5|
### TER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_rnn_asr_model_valid.acc.ave/test_th|10769|278454|95.0|2.8|2.2|1.1|6.1|41.2|
## ASR config
<details><summary>expand</summary>
```
config: conf/tuning/train_asr_rnn.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/asr_train_asr_rnn_raw_th_bpe150_sp
ngpu: 1
seed: 0
num_workers: 1
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: null
dist_rank: null
local_rank: 0
dist_master_addr: null
dist_master_port: null
dist_launcher: null
multiprocessing_distributed: false
unused_parameters: false
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
collect_stats: false
write_collected_feats: false
max_epoch: 15
patience: 3
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - train
- loss
- min
- - valid
- loss
- min
- - train
- acc
- max
- - valid
- acc
- max
keep_nbest_models:
- 10
nbest_averaging_interval: 0
grad_clip: 5.0
grad_clip_type: 2.0
grad_noise: false
accum_grad: 1
no_forward_run: false
resume: true
train_dtype: float32
use_amp: false
log_interval: null
use_matplotlib: true
use_tensorboard: true
use_wandb: false
wandb_project: null
wandb_id: null
wandb_entity: null
wandb_name: null
wandb_model_log_interval: -1
detect_anomaly: false
pretrain_path: null
init_param: []
ignore_init_mismatch: false
freeze_param: []
num_iters_per_epoch: null
batch_size: 30
valid_batch_size: null
batch_bins: 1000000
valid_batch_bins: null
train_shape_file:
- exp/asr_stats_raw_th_bpe150_sp/train/speech_shape
- exp/asr_stats_raw_th_bpe150_sp/train/text_shape.bpe
valid_shape_file:
- exp/asr_stats_raw_th_bpe150_sp/valid/speech_shape
- exp/asr_stats_raw_th_bpe150_sp/valid/text_shape.bpe
batch_type: folded
valid_batch_type: null
fold_length:
- 80000
- 150
sort_in_batch: descending
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
train_data_path_and_name_and_type:
- - dump/raw/train_th_sp/wav.scp
- speech
- sound
- - dump/raw/train_th_sp/text
- text
- text
valid_data_path_and_name_and_type:
- - dump/raw/dev_th/wav.scp
- speech
- sound
- - dump/raw/dev_th/text
- text
- text
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
optim: adadelta
optim_conf:
lr: 0.1
scheduler: null
scheduler_conf: {}
token_list:
- <blank>
- <unk>
- ▁
- น
- ร
- ก
- า
- เ
- อ
- ง
- ย
- ม
- ั
- ส
- ด
- บ
- ว
- ิ
- ล
- ค
- ต
- ห
- ่
- ท
- ้
- พ
- ช
- แ
- ี
- จ
- ะ
- ที่
- ุ
- ้า
- ู
- ์
- ป
- ข
- ไ
- การ
- โ
- ไม่
- ่อ
- ่า
- ็
- ื
- ํา
- ือ
- จะ
- มา
- ของ
- ได้
- เป็น
- ถ
- ีย
- มี
- ่ง
- ว่า
- ้อ
- ัน
- ใน
- ไป
- คุณ
- ▁ฉัน
- ัง
- เขา
- ความ
- ใ
- ผ
- หน
- ให้
- ทํา
- ศ
- ซ
- ึ
- นี้
- ฉัน
- มัน
- ี่
- ญ
- และ
- ประ
- ิน
- หล
- ษ
- ภ
- ธ
- ณ
- ฟ
- อย่าง
- เธอ
- '?'
- '"'
- ฐ
- '!'
- ฝ
- ฉ
- ฮ
- ๊
- ''''
- '-'
- ฒ
- ๆ
- ๋
- ฎ
- ฤ
- ฏ
- ฬ
- ฑ
- .
- ”
- ':'
- “
- ','
- ’
- ;
- ฌ
- E
- R
- O
- T
- N
- A
- I
- S
- F
- C
- '~'
- B
- K
- X
- L
- H
- M
- Y
- —
- J
- W
- ฃ
- _
- ฯ
- ํ
- U
- ๅ
- ‘
- G
- '|'
- P
- ฆ
- <sos/eos>
init: null
input_size: null
ctc_conf:
dropout_rate: 0.0
ctc_type: builtin
reduce: true
ignore_nan_grad: true
joint_net_conf: null
model_conf:
ctc_weight: 0.5
use_preprocessor: true
token_type: bpe
bpemodel: data/th_token_list/bpe_unigram150/bpe.model
non_linguistic_symbols: null
cleaner: null
g2p: null
speech_volume_normalize: null
rir_scp: null
rir_apply_prob: 1.0
noise_scp: null
noise_apply_prob: 1.0
noise_db_range: '13_15'
frontend: default
frontend_conf:
fs: 16k
specaug: specaug
specaug_conf:
apply_time_warp: true
time_warp_window: 5
time_warp_mode: bicubic
apply_freq_mask: true
freq_mask_width_range:
- 0
- 27
num_freq_mask: 2
apply_time_mask: true
time_mask_width_ratio_range:
- 0.0
- 0.05
num_time_mask: 2
normalize: global_mvn
normalize_conf:
stats_file: exp/asr_stats_raw_th_bpe150_sp/train/feats_stats.npz
preencoder: null
preencoder_conf: {}
encoder: vgg_rnn
encoder_conf:
rnn_type: lstm
bidirectional: true
use_projection: true
num_layers: 4
hidden_size: 1024
output_size: 1024
postencoder: null
postencoder_conf: {}
decoder: rnn
decoder_conf:
num_layers: 2
hidden_size: 1024
sampling_probability: 0
att_conf:
atype: location
adim: 1024
aconv_chans: 10
aconv_filts: 100
required:
- output_dir
- token_list
version: 0.10.6a1
distributed: false
```
</details>
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
espnet/farsi_commonvoice_blstm | espnet | 2022-05-02T15:50:24Z | 5 | 3 | espnet | [
"espnet",
"audio",
"automatic-speech-recognition",
"fa",
"dataset:commonvoice",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | automatic-speech-recognition | 2022-05-02T15:49:22Z | ---
tags:
- espnet
- audio
- automatic-speech-recognition
language: fa
datasets:
- commonvoice
license: cc-by-4.0
---
## ESPnet2 ASR model
### `espnet/farsi_commonvoice_blstm`
This model was trained by dzeinali using commonvoice recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 716eb8f92e19708acfd08ba3bd39d40890d3a84b
pip install -e .
cd egs2/commonvoice/asr1
./run.sh --skip_data_prep false --skip_train true --download_model espnet/farsi_commonvoice_blstm
```
<!-- Generated by scripts/utils/show_asr_result.sh -->
# RESULTS
## Environments
- date: `Mon May 2 11:48:56 EDT 2022`
- python version: `3.9.5 (default, Jun 4 2021, 12:28:51) [GCC 7.5.0]`
- espnet version: `espnet 0.10.6a1`
- pytorch version: `pytorch 1.8.1+cu102`
- Git hash: `716eb8f92e19708acfd08ba3bd39d40890d3a84b`
- Commit date: `Thu Apr 28 19:50:59 2022 -0400`
## asr_train_asr_rnn_raw_fa_bpe150_sp
### WER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_rnn_asr_model_valid.acc.ave/test_fa|9728|68904|0.0|0.0|100.0|0.0|100.0|100.0|
|decode_rnn_asr_model_valid.acc.best/test_fa|9728|68904|91.4|7.2|1.4|1.0|9.5|30.1|
### CER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_rnn_asr_model_valid.acc.ave/test_fa|9728|331506|0.0|0.0|100.0|0.0|100.0|100.0|
|decode_rnn_asr_model_valid.acc.best/test_fa|9728|331506|97.2|1.3|1.5|0.7|3.6|30.1|
### TER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_rnn_asr_model_valid.acc.ave/test_fa|9728|230963|0.0|0.0|100.0|0.0|100.0|100.0|
|decode_rnn_asr_model_valid.acc.best/test_fa|9728|230963|95.9|2.4|1.6|0.7|4.7|30.1|
## ASR config
<details><summary>expand</summary>
```
config: conf/tuning/train_asr_rnn.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/asr_train_asr_rnn_raw_fa_bpe150_sp
ngpu: 1
seed: 0
num_workers: 1
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: null
dist_rank: null
local_rank: 0
dist_master_addr: null
dist_master_port: null
dist_launcher: null
multiprocessing_distributed: false
unused_parameters: false
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
collect_stats: false
write_collected_feats: false
max_epoch: 15
patience: 3
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - train
- loss
- min
- - valid
- loss
- min
- - train
- acc
- max
- - valid
- acc
- max
keep_nbest_models:
- 10
nbest_averaging_interval: 0
grad_clip: 5.0
grad_clip_type: 2.0
grad_noise: false
accum_grad: 1
no_forward_run: false
resume: true
train_dtype: float32
use_amp: false
log_interval: null
use_matplotlib: true
use_tensorboard: true
use_wandb: false
wandb_project: null
wandb_id: null
wandb_entity: null
wandb_name: null
wandb_model_log_interval: -1
detect_anomaly: false
pretrain_path: null
init_param: []
ignore_init_mismatch: false
freeze_param: []
num_iters_per_epoch: null
batch_size: 30
valid_batch_size: null
batch_bins: 1000000
valid_batch_bins: null
train_shape_file:
- exp/asr_stats_raw_fa_bpe150_sp/train/speech_shape
- exp/asr_stats_raw_fa_bpe150_sp/train/text_shape.bpe
valid_shape_file:
- exp/asr_stats_raw_fa_bpe150_sp/valid/speech_shape
- exp/asr_stats_raw_fa_bpe150_sp/valid/text_shape.bpe
batch_type: folded
valid_batch_type: null
fold_length:
- 80000
- 150
sort_in_batch: descending
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
train_data_path_and_name_and_type:
- - dump/raw/train_fa_sp/wav.scp
- speech
- sound
- - dump/raw/train_fa_sp/text
- text
- text
valid_data_path_and_name_and_type:
- - dump/raw/dev_fa/wav.scp
- speech
- sound
- - dump/raw/dev_fa/text
- text
- text
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
optim: adadelta
optim_conf:
lr: 0.1
scheduler: null
scheduler_conf: {}
token_list:
- <blank>
- <unk>
- ی
- ا
- ه
- ▁
- ر
- م
- و
- د
- ت
- ش
- ن
- ل
- ▁ب
- ز
- ب
- .
- ▁م
- ان
- ▁ا
- س
- ک
- ▁می
- گ
- ف
- ▁د
- ؟
- ق
- ▁و
- ید
- ▁ن
- ند
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- ار
- ▁چ
- ع
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- ▁ت
- ▁ک
- ▁با
- خ
- ون
- ▁پ
- ▁به
- ▁من
- ▁س
- ▁را
- ،
- ▁خ
- ▁این
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- ▁آ
- ▁در
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- اد
- ▁است
- ح
- ص
- ▁ش
- ط
- ▁تو
- ین
- ▁دار
- ▁که
- ال
- ▁رو
- ▁گ
- ▁ج
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- ام
- ▁هم
- ▁ح
- فت
- رد
- یم
- پ
- غ
- چ
- ذ
- ض
- ظ
- '!'
- ث
- ً
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- '"'
- ژ
- ك
- آ
- ي
- ':'
- ى
- '-'
- ِ
- أ
- َ
- »
- ـ
- ','
- ُ
- (
- )
- ء
- ٔ
- ٬
- ّ
- ؛
- B
- C
- A
- E
- G
- M
- S
- ؤ
- I
- ;
- T
- H
- _
- F
- D
- ۀ
- Y
- N
- K
- U
- –
- ٌ
- P
- O
- Q
- Z
- '&'
- L
- R
- ة
- X
- ā
- '#'
- “
- '='
- «
- š
- ْ
- ے
- ”
- <sos/eos>
init: null
input_size: null
ctc_conf:
dropout_rate: 0.0
ctc_type: builtin
reduce: true
ignore_nan_grad: true
joint_net_conf: null
model_conf:
ctc_weight: 0.5
use_preprocessor: true
token_type: bpe
bpemodel: data/fa_token_list/bpe_unigram150/bpe.model
non_linguistic_symbols: null
cleaner: null
g2p: null
speech_volume_normalize: null
rir_scp: null
rir_apply_prob: 1.0
noise_scp: null
noise_apply_prob: 1.0
noise_db_range: '13_15'
frontend: default
frontend_conf:
fs: 16k
specaug: specaug
specaug_conf:
apply_time_warp: true
time_warp_window: 5
time_warp_mode: bicubic
apply_freq_mask: true
freq_mask_width_range:
- 0
- 27
num_freq_mask: 2
apply_time_mask: true
time_mask_width_ratio_range:
- 0.0
- 0.05
num_time_mask: 2
normalize: global_mvn
normalize_conf:
stats_file: exp/asr_stats_raw_fa_bpe150_sp/train/feats_stats.npz
preencoder: null
preencoder_conf: {}
encoder: vgg_rnn
encoder_conf:
rnn_type: lstm
bidirectional: true
use_projection: true
num_layers: 4
hidden_size: 1024
output_size: 1024
postencoder: null
postencoder_conf: {}
decoder: rnn
decoder_conf:
num_layers: 2
hidden_size: 1024
sampling_probability: 0
att_conf:
atype: location
adim: 1024
aconv_chans: 10
aconv_filts: 100
required:
- output_dir
- token_list
version: 0.10.6a1
distributed: false
```
</details>
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
espnet/pt_commonvoice_blstm | espnet | 2022-05-02T15:39:16Z | 3 | 1 | espnet | [
"espnet",
"audio",
"automatic-speech-recognition",
"pt",
"dataset:commonvoice",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | automatic-speech-recognition | 2022-05-02T15:37:14Z | ---
tags:
- espnet
- audio
- automatic-speech-recognition
language: pt
datasets:
- commonvoice
license: cc-by-4.0
---
## ESPnet2 ASR model
### `espnet/pt_commonvoice_blstm`
This model was trained by dzeinali using commonvoice recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 716eb8f92e19708acfd08ba3bd39d40890d3a84b
pip install -e .
cd egs2/commonvoice/asr1
./run.sh --skip_data_prep false --skip_train true --download_model espnet/pt_commonvoice_blstm
```
<!-- Generated by scripts/utils/show_asr_result.sh -->
# RESULTS
## Environments
- date: `Mon Apr 11 18:55:23 EDT 2022`
- python version: `3.9.5 (default, Jun 4 2021, 12:28:51) [GCC 7.5.0]`
- espnet version: `espnet 0.10.6a1`
- pytorch version: `pytorch 1.8.1+cu102`
- Git hash: `5e6e95d087af8a7a4c33c4248b75114237eae64b`
- Commit date: `Mon Apr 4 21:04:45 2022 -0400`
## asr_train_asr_rnn_raw_pt_bpe150_sp
### WER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_rnn_asr_model_valid.acc.best/test_pt|4334|33716|84.7|12.4|2.9|1.3|16.6|46.8|
### CER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_rnn_asr_model_valid.acc.best/test_pt|4334|191499|93.4|3.0|3.6|1.2|7.8|46.9|
### TER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_rnn_asr_model_valid.acc.best/test_pt|4334|116003|90.4|5.7|3.9|1.5|11.1|46.9|
## ASR config
<details><summary>expand</summary>
```
config: conf/tuning/train_asr_rnn.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/asr_train_asr_rnn_raw_pt_bpe150_sp
ngpu: 1
seed: 0
num_workers: 1
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: null
dist_rank: null
local_rank: 0
dist_master_addr: null
dist_master_port: null
dist_launcher: null
multiprocessing_distributed: false
unused_parameters: false
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
collect_stats: false
write_collected_feats: false
max_epoch: 15
patience: 3
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - train
- loss
- min
- - valid
- loss
- min
- - train
- acc
- max
- - valid
- acc
- max
keep_nbest_models:
- 10
nbest_averaging_interval: 0
grad_clip: 5.0
grad_clip_type: 2.0
grad_noise: false
accum_grad: 1
no_forward_run: false
resume: true
train_dtype: float32
use_amp: false
log_interval: null
use_matplotlib: true
use_tensorboard: true
use_wandb: false
wandb_project: null
wandb_id: null
wandb_entity: null
wandb_name: null
wandb_model_log_interval: -1
detect_anomaly: false
pretrain_path: null
init_param: []
ignore_init_mismatch: false
freeze_param: []
num_iters_per_epoch: null
batch_size: 30
valid_batch_size: null
batch_bins: 1000000
valid_batch_bins: null
train_shape_file:
- exp/asr_stats_raw_pt_bpe150_sp/train/speech_shape
- exp/asr_stats_raw_pt_bpe150_sp/train/text_shape.bpe
valid_shape_file:
- exp/asr_stats_raw_pt_bpe150_sp/valid/speech_shape
- exp/asr_stats_raw_pt_bpe150_sp/valid/text_shape.bpe
batch_type: folded
valid_batch_type: null
fold_length:
- 80000
- 150
sort_in_batch: descending
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
train_data_path_and_name_and_type:
- - dump/raw/train_pt_sp/wav.scp
- speech
- sound
- - dump/raw/train_pt_sp/text
- text
- text
valid_data_path_and_name_and_type:
- - dump/raw/dev_pt/wav.scp
- speech
- sound
- - dump/raw/dev_pt/text
- text
- text
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
optim: adadelta
optim_conf:
lr: 0.1
scheduler: null
scheduler_conf: {}
token_list:
- <blank>
- <unk>
- ▁
- S
- R
- I
- U
- E
- O
- A
- .
- N
- M
- L
- ▁A
- ▁DE
- RA
- ▁O
- T
- ▁E
- ▁UM
- C
- TA
- DO
- G
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- VE
- B
- NDO
- ▁SE
- ▁QUE
- P
- ▁UMA
- LA
- D
- ▁COM
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- '?'
- ▁PE
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- IN
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- IS
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- H
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- ▁CA
- ▁P
- CO
- ','
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- PA
- ▁NãO
- DE
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- ▁ME
- ▁PARA
- Z
- ▁MA
- VA
- PO
- ▁DO
- ▁VOCê
- RI
- ▁DI
- GA
- VI
- ▁é
- LO
- IA
- ▁ELE
- ▁EU
- ▁ESTá
- HA
- ▁M
- X
- ▁NA
- NA
- é
- CE
- LE
- GO
- VO
- ▁RE
- ▁FO
- ▁FA
- ▁CO
- QUE
- ▁EST
- BE
- ▁CON
- ó
- SE
- ▁POR
- ê
- í
- çãO
- ▁DA
- RES
- ▁QUA
- ▁HOMEM
- RIA
- çA
- ▁SA
- V
- ▁PRE
- MENTE
- ZE
- NHA
- '-'
- ▁BA
- MOS
- ▁SO
- ▁BO
- ç
- '"'
- '!'
- ú
- ã
- K
- Y
- É
- W
- ô
- Á
- ':'
- ;
- ''''
- ”
- Ô
- ñ
- “
- Ú
- Í
- Ó
- ü
- À
- â
- à
- õ
- J
- Q
- F
- Â
- <sos/eos>
init: null
input_size: null
ctc_conf:
dropout_rate: 0.0
ctc_type: builtin
reduce: true
ignore_nan_grad: true
joint_net_conf: null
model_conf:
ctc_weight: 0.5
use_preprocessor: true
token_type: bpe
bpemodel: data/pt_token_list/bpe_unigram150/bpe.model
non_linguistic_symbols: null
cleaner: null
g2p: null
speech_volume_normalize: null
rir_scp: null
rir_apply_prob: 1.0
noise_scp: null
noise_apply_prob: 1.0
noise_db_range: '13_15'
frontend: default
frontend_conf:
fs: 16k
specaug: specaug
specaug_conf:
apply_time_warp: true
time_warp_window: 5
time_warp_mode: bicubic
apply_freq_mask: true
freq_mask_width_range:
- 0
- 27
num_freq_mask: 2
apply_time_mask: true
time_mask_width_ratio_range:
- 0.0
- 0.05
num_time_mask: 2
normalize: global_mvn
normalize_conf:
stats_file: exp/asr_stats_raw_pt_bpe150_sp/train/feats_stats.npz
preencoder: null
preencoder_conf: {}
encoder: vgg_rnn
encoder_conf:
rnn_type: lstm
bidirectional: true
use_projection: true
num_layers: 4
hidden_size: 1024
output_size: 1024
postencoder: null
postencoder_conf: {}
decoder: rnn
decoder_conf:
num_layers: 2
hidden_size: 1024
sampling_probability: 0
att_conf:
atype: location
adim: 1024
aconv_chans: 10
aconv_filts: 100
required:
- output_dir
- token_list
version: 0.10.6a1
distributed: false
```
</details>
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
espnet/greek_commonvoice_blstm | espnet | 2022-05-02T15:35:07Z | 0 | 0 | espnet | [
"espnet",
"audio",
"automatic-speech-recognition",
"el",
"dataset:commonvoice",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | automatic-speech-recognition | 2022-05-02T15:34:01Z | ---
tags:
- espnet
- audio
- automatic-speech-recognition
language: el
datasets:
- commonvoice
license: cc-by-4.0
---
## ESPnet2 ASR model
### `espnet/greek_commonvoice_blstm`
This model was trained by dzeinali using commonvoice recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 716eb8f92e19708acfd08ba3bd39d40890d3a84b
pip install -e .
cd egs2/commonvoice/asr1
./run.sh --skip_data_prep false --skip_train true --download_model espnet/greek_commonvoice_blstm
```
<!-- Generated by scripts/utils/show_asr_result.sh -->
# RESULTS
## Environments
- date: `Sun Apr 17 19:51:46 EDT 2022`
- python version: `3.9.5 (default, Jun 4 2021, 12:28:51) [GCC 7.5.0]`
- espnet version: `espnet 0.10.6a1`
- pytorch version: `pytorch 1.8.1+cu102`
- Git hash: `5e6e95d087af8a7a4c33c4248b75114237eae64b`
- Commit date: `Mon Apr 4 21:04:45 2022 -0400`
## asr_train_asr_rnn_tr_raw_el_bpe150_sp
### WER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_rnn_asr_model_valid.acc.ave/test_el|1681|10574|90.7|7.7|1.6|0.5|9.9|27.4|
### CER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_rnn_asr_model_valid.acc.ave/test_el|1681|61731|96.6|1.5|1.9|0.6|4.0|27.5|
### TER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_rnn_asr_model_valid.acc.ave/test_el|1681|44869|95.7|2.4|1.9|0.7|5.0|27.5|
## ASR config
<details><summary>expand</summary>
```
config: conf/tuning/train_asr_rnn_tr.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/asr_train_asr_rnn_tr_raw_el_bpe150_sp
ngpu: 1
seed: 0
num_workers: 1
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: null
dist_rank: null
local_rank: 0
dist_master_addr: null
dist_master_port: null
dist_launcher: null
multiprocessing_distributed: false
unused_parameters: false
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
collect_stats: false
write_collected_feats: false
max_epoch: 50
patience: 3
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - train
- loss
- min
- - valid
- loss
- min
- - train
- acc
- max
- - valid
- acc
- max
keep_nbest_models:
- 10
nbest_averaging_interval: 0
grad_clip: 5.0
grad_clip_type: 2.0
grad_noise: false
accum_grad: 1
no_forward_run: false
resume: true
train_dtype: float32
use_amp: false
log_interval: null
use_matplotlib: true
use_tensorboard: true
use_wandb: false
wandb_project: null
wandb_id: null
wandb_entity: null
wandb_name: null
wandb_model_log_interval: -1
detect_anomaly: false
pretrain_path: null
init_param: []
ignore_init_mismatch: false
freeze_param: []
num_iters_per_epoch: null
batch_size: 16
valid_batch_size: null
batch_bins: 1000000
valid_batch_bins: null
train_shape_file:
- exp/asr_stats_raw_el_bpe150_sp/train/speech_shape
- exp/asr_stats_raw_el_bpe150_sp/train/text_shape.bpe
valid_shape_file:
- exp/asr_stats_raw_el_bpe150_sp/valid/speech_shape
- exp/asr_stats_raw_el_bpe150_sp/valid/text_shape.bpe
batch_type: folded
valid_batch_type: null
fold_length:
- 80000
- 150
sort_in_batch: descending
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
train_data_path_and_name_and_type:
- - dump/raw/train_el_sp/wav.scp
- speech
- sound
- - dump/raw/train_el_sp/text
- text
- text
valid_data_path_and_name_and_type:
- - dump/raw/dev_el/wav.scp
- speech
- sound
- - dump/raw/dev_el/text
- text
- text
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
optim: adadelta
optim_conf:
lr: 0.1
scheduler: null
scheduler_conf: {}
token_list:
- <blank>
- <unk>
- ▁
- α
- ν
- ρ
- ι
- ε
- ο
- τ
- ς
- λ
- ά
- σ
- κ
- ό
- .
- ί
- ▁π
- έ
- ω
- π
- γ
- η
- μ
- υ
- ','
- ή
- ▁το
- χ
- θ
- ώ
- ▁και
- ▁του
- δ
- τα
- αν
- ει
- ▁να
- ▁σ
- ου
- σε
- ▁κ
- ύ
- ού
- φ
- στ
- ρα
- ια
- ▁μ
- ▁δ
- ▁έ
- τι
- β
- ρι
- μα
- πο
- εί
- ▁φ
- ▁με
- κα
- ▁α
- ος
- ;
- ▁χ
- '!'
- ▁β
- ες
- ▁στο
- τε
- ▁γ
- '"'
- τη
- ▁ο
- ▁Π
- ▁δε
- ▁που
- ▁μου
- με
- ▁τα
- σα
- λα
- Μ
- ιά
- ▁από
- εις
- ▁την
- έρ
- ▁ε
- ▁τον
- ρά
- λο
- ▁είπε
- ▁μα
- ψ
- Τ
- ''''
- Κ
- Σ
- Ε
- Α
- Θ
- '-'
- Η
- Ά
- Ν
- Δ
- Χ
- ’
- Ξ
- »
- Π
- ΐ
- Ώ
- Ο
- A
- O
- ·
- ':'
- E
- G
- H
- N
- R
- T
- V
- Υ
- ϋ
- Ψ
- ́
- ‘
- Ι
- Ί
- Ρ
- Ω
- «
- Ύ
- Ζ
- ϊ
- Ή
- Φ
- Λ
- Ό
- Γ
- Έ
- Β
- ζ
- M
- ξ
- <sos/eos>
init: null
input_size: null
ctc_conf:
dropout_rate: 0.0
ctc_type: builtin
reduce: true
ignore_nan_grad: true
joint_net_conf: null
model_conf:
ctc_weight: 0.5
use_preprocessor: true
token_type: bpe
bpemodel: data/el_token_list/bpe_unigram150/bpe.model
non_linguistic_symbols: null
cleaner: null
g2p: null
speech_volume_normalize: null
rir_scp: null
rir_apply_prob: 1.0
noise_scp: null
noise_apply_prob: 1.0
noise_db_range: '13_15'
frontend: default
frontend_conf:
fs: 16k
specaug: specaug
specaug_conf:
apply_time_warp: true
time_warp_window: 5
time_warp_mode: bicubic
apply_freq_mask: true
freq_mask_width_range:
- 0
- 27
num_freq_mask: 2
apply_time_mask: true
time_mask_width_ratio_range:
- 0.0
- 0.05
num_time_mask: 2
normalize: global_mvn
normalize_conf:
stats_file: exp/asr_stats_raw_el_bpe150_sp/train/feats_stats.npz
preencoder: null
preencoder_conf: {}
encoder: vgg_rnn
encoder_conf:
rnn_type: lstm
bidirectional: true
use_projection: true
num_layers: 4
hidden_size: 1024
output_size: 1024
postencoder: null
postencoder_conf: {}
decoder: rnn
decoder_conf:
num_layers: 2
hidden_size: 1024
sampling_probability: 0
att_conf:
atype: location
adim: 1024
aconv_chans: 10
aconv_filts: 100
required:
- output_dir
- token_list
version: 0.10.6a1
distributed: false
```
</details>
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
fahadtouseef/wav2vec2-base-timit-demo-colab_2 | fahadtouseef | 2022-05-02T14:18:38Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2022-05-02T11:50:57Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-timit-demo-colab_2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-colab_2
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3801
- Wer: 0.3035
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 4.7227 | 3.52 | 500 | 2.6961 | 1.0 |
| 1.1237 | 7.04 | 1000 | 0.6088 | 0.5315 |
| 0.4886 | 10.56 | 1500 | 0.4709 | 0.4353 |
| 0.3148 | 14.08 | 2000 | 0.4341 | 0.3942 |
| 0.2229 | 17.61 | 2500 | 0.4035 | 0.3616 |
| 0.1693 | 21.13 | 3000 | 0.3868 | 0.3289 |
| 0.1393 | 24.65 | 3500 | 0.3993 | 0.3135 |
| 0.118 | 28.17 | 4000 | 0.3801 | 0.3035 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
umanlp/TOD-XLMR | umanlp | 2022-05-02T14:16:51Z | 13 | 2 | transformers | [
"transformers",
"pytorch",
"xlm-roberta",
"fill-mask",
"exbert",
"multilingual",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2022-04-21T09:29:28Z | ---
tags:
- exbert
language: multilingual
license: mit
---
# TOD-XLMR
TOD-XLMR is a conversationally specialized multilingual version based on [XLM-RoBERTa](https://huggingface.co/xlm-roberta-base). It is pre-trained on English conversational corpora consisting of nine human-to-human multi-turn task-oriented dialog (TOD) datasets as proposed in the paper [TOD-BERT: Pre-trained Natural Language Understanding for Task-Oriented Dialogue](https://aclanthology.org/2020.emnlp-main.66.pdf) by Wu et al. and first released in [this repository](https://huggingface.co/TODBERT).
The model is jointly trained with two objectives as proposed in TOD-BERT, including masked language modeling (MLM) and response contrastive loss (RCL). Masked language modeling is a common pretraining strategy utilized for BERT-based architectures, where a random sample of tokens in the input sequence is replaced with the special token [MASK] for predicting the original masked tokens. To further encourage the model to capture dialogic structure (i.e., dialog sequential order), response contrastive loss is implemented by using in-batch negative training with contrastive learning.
### How to use
Here is how to use this model to get the features of a given text in PyTorch:
```
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("umanlp/TOD-XLMR")
model = AutoModelForMaskedLM.from_pretrained("umanlp/TOD-XLMR")
# prepare input
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
# forward pass
output = model(**encoded_input)
```
Or you can also use `AutoModel` to load the pretrained model and further apply to downstream tasks:
```
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("umanlp/TOD-XLMR")
model = AutoModel("umanlp/TOD-XLMR")
# prepare input
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
# forward pass
output = model(**encoded_input)
```
|
ali2066/DistilBERTFINAL_ctxSentence_TRAIN_all_TEST_NULL_second_train_set_null_False | ali2066 | 2022-05-02T14:00:18Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-05-02T13:19:37Z | ---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: DistilBERTFINAL_ctxSentence_TRAIN_all_TEST_NULL_second_train_set_null_False
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. -->
# DistilBERTFINAL_ctxSentence_TRAIN_all_TEST_NULL_second_train_set_null_False
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base](https://huggingface.co/cardiffnlp/twitter-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0557
- Precision: 0.9930
- Recall: 0.9878
- F1: 0.9904
- Accuracy: 0.9814
## 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: 32
- eval_batch_size: 32
- 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 | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 479 | 0.3334 | 0.9041 | 0.9041 | 0.9041 | 0.8550 |
| 0.3756 | 2.0 | 958 | 0.3095 | 0.8991 | 0.9251 | 0.9119 | 0.8649 |
| 0.2653 | 3.0 | 1437 | 0.3603 | 0.8929 | 0.9527 | 0.9218 | 0.8779 |
| 0.1991 | 4.0 | 1916 | 0.3907 | 0.8919 | 0.9540 | 0.9219 | 0.8779 |
| 0.1586 | 5.0 | 2395 | 0.3642 | 0.9070 | 0.9356 | 0.9211 | 0.8788 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1+cu113
- Datasets 1.18.0
- Tokenizers 0.10.3
|
ali2066/DistilBERTFINAL_ctxSentence_TRAIN_webDiscourse_TEST_NULL_second_train_set_null_False | ali2066 | 2022-05-02T13:37:28Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-05-02T13:12:40Z | ---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: DistilBERTFINAL_ctxSentence_TRAIN_webDiscourse_TEST_NULL_second_train_set_null_False
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. -->
# DistilBERTFINAL_ctxSentence_TRAIN_webDiscourse_TEST_NULL_second_train_set_null_False
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base](https://huggingface.co/cardiffnlp/twitter-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.2555
- Precision: 1.0
- Recall: 0.0200
- F1: 0.0393
- Accuracy: 0.0486
## 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: 32
- eval_batch_size: 32
- 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 | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 95 | 0.5756 | nan | 0.0 | nan | 0.715 |
| No log | 2.0 | 190 | 0.5340 | 0.6429 | 0.1579 | 0.2535 | 0.735 |
| No log | 3.0 | 285 | 0.5298 | 0.5833 | 0.3684 | 0.4516 | 0.745 |
| No log | 4.0 | 380 | 0.5325 | 0.5789 | 0.3860 | 0.4632 | 0.745 |
| No log | 5.0 | 475 | 0.5452 | 0.4815 | 0.4561 | 0.4685 | 0.705 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1+cu113
- Datasets 1.18.0
- Tokenizers 0.10.3
|
ali2066/DistilBERTFINAL_ctxSentence_TRAIN_essays_TEST_NULL_second_train_set_null_False | ali2066 | 2022-05-02T13:33:27Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-05-02T13:10:30Z | ---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: DistilBERTFINAL_ctxSentence_TRAIN_essays_TEST_NULL_second_train_set_null_False
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. -->
# DistilBERTFINAL_ctxSentence_TRAIN_essays_TEST_NULL_second_train_set_null_False
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base](https://huggingface.co/cardiffnlp/twitter-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7680
- Precision: 0.9838
- Recall: 0.6632
- F1: 0.7923
- Accuracy: 0.6624
## 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: 32
- eval_batch_size: 32
- 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 | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 130 | 0.2980 | 0.9315 | 0.9533 | 0.9423 | 0.9081 |
| No log | 2.0 | 260 | 0.2053 | 0.9537 | 0.9626 | 0.9581 | 0.9338 |
| No log | 3.0 | 390 | 0.1873 | 0.9464 | 0.9907 | 0.9680 | 0.9485 |
| 0.3064 | 4.0 | 520 | 0.1811 | 0.9585 | 0.9720 | 0.9652 | 0.9449 |
| 0.3064 | 5.0 | 650 | 0.1887 | 0.9587 | 0.9766 | 0.9676 | 0.9485 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1+cu113
- Datasets 1.18.0
- Tokenizers 0.10.3
|
hassnain/wav2vec2-base-timit-demo-colab92 | hassnain | 2022-05-02T11:09:44Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2022-05-01T12:40:27Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-timit-demo-colab92
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. -->
# wav2vec2-base-timit-demo-colab92
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.6596
- eval_wer: 0.4164
- eval_runtime: 55.6472
- eval_samples_per_second: 12.615
- eval_steps_per_second: 1.581
- epoch: 2.85
- step: 1000
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 60
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
cfilt/HiNER-original-xlm-roberta-large | cfilt | 2022-05-02T10:19:28Z | 90 | 1 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:cfilt/HiNER-original",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2022-05-01T07:38:35Z | ---
tags:
- generated_from_trainer
datasets:
- cfilt/HiNER-original
metrics:
- precision
- recall
- f1
model-index:
- name: HiNER-original-xlm-roberta-large
results:
- task:
name: Token Classification
type: token-classification
dataset:
type: cfilt/HiNER-original
name: HiNER Original
metrics:
- name: Precision
type: precision
value: 0.8968858782575971
- name: Recall
type: recall
value: 0.8871207891308394
- name: F1
type: f1
value: 0.8919766081871345
---
<!-- 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. -->
# HiNER-original-xlm-roberta-large
This model was trained from scratch on HiNER-original dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 1
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10.0
### Framework versions
- Transformers 4.14.0
- Pytorch 1.9.1
- Datasets 1.15.1
- Tokenizers 0.10.3
|
kyryl0s/gpt2-uk-xxs | kyryl0s | 2022-05-02T09:14:29Z | 6 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"uk",
"license:afl-3.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-04-06T14:04:49Z | ---
license: afl-3.0
language: uk
---
## GPT2 being trained on Ukrainian news.
### General info:
The model is not ready yet but I'm working on it. It also has a relatively small context window, which makes it quite uninteresting.
### Example of usage:
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("kyryl0s/gpt2-uk-xxs")
model = AutoModelForCausalLM.from_pretrained("kyryl0s/gpt2-uk-xxs")
input_ids = tokenizer.encode("Путін — ", add_special_tokens=False, return_tensors='pt')
outputs = model.generate(
input_ids,
do_sample=True,
num_return_sequences=3,
max_length=50
)
for i, out in enumerate(outputs):
print("{}: {}".format(i, tokenizer.decode(out)))
``` |
driboune/skin_type | driboune | 2022-05-02T08:08:40Z | 183 | 3 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | 2022-04-29T15:59:55Z | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: skin_type
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.8222222328186035
---
# skin_type
Aiming for fairness in image classification for humans, knowing the skin type of subjects is relevant to make sure the model performs correctly on all skin types.
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
#### dark skin
#### light skin
 |
crcb/emo_go_new | crcb | 2022-05-02T04:17:02Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain",
"unk",
"dataset:crcb/autotrain-data-go_emo_new",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-05-02T04:07:25Z | ---
tags: autotrain
language: unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- crcb/autotrain-data-go_emo_new
co2_eq_emissions: 20.58663910106142
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 813325491
- CO2 Emissions (in grams): 20.58663910106142
## Validation Metrics
- Loss: 1.3628994226455688
- Accuracy: 0.5920355494787216
- Macro F1: 0.4844439507523978
- Micro F1: 0.5920355494787216
- Weighted F1: 0.5873137663478112
- Macro Precision: 0.5458988948121151
- Micro Precision: 0.5920355494787216
- Weighted Precision: 0.591386299522425
- Macro Recall: 0.4753100798358001
- Micro Recall: 0.5920355494787216
- Weighted Recall: 0.5920355494787216
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/crcb/autotrain-go_emo_new-813325491
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("crcb/autotrain-go_emo_new-813325491", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("crcb/autotrain-go_emo_new-813325491", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` |
DioLiu/distilbert-base-uncased-finetuned-sst2 | DioLiu | 2022-05-02T03:06:36Z | 8 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-05-02T02:28:34Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-sst2
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: sst2
metrics:
- name: Accuracy
type: accuracy
value: 0.8967889908256881
---
<!-- 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-sst2
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5963
- Accuracy: 0.8968
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.247 | 1.0 | 1404 | 0.3629 | 0.8865 |
| 0.1532 | 2.0 | 2808 | 0.3945 | 0.8979 |
| 0.0981 | 3.0 | 4212 | 0.4206 | 0.9025 |
| 0.0468 | 4.0 | 5616 | 0.5358 | 0.9014 |
| 0.0313 | 5.0 | 7020 | 0.5963 | 0.8968 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
Ghost1/bert-finetuned-squad1 | Ghost1 | 2022-05-02T02:28:59Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | question-answering | 2022-05-02T00:04:06Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bert-finetuned-squad1
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-finetuned-squad1
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
voodooMaestro/finetuned-stories | voodooMaestro | 2022-05-02T00:24:29Z | 4 | 0 | transformers | [
"transformers",
"tf",
"roberta",
"fill-mask",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2022-05-01T23:31:33Z | ---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: voodooMaestro/finetuned-stories
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. -->
# voodooMaestro/finetuned-stories
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.9188
- Validation Loss: 1.5604
- 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': -688, '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 |
|:----------:|:---------------:|:-----:|
| 1.9188 | 1.5604 | 0 |
### Framework versions
- Transformers 4.18.0
- TensorFlow 2.8.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
SebastianS/bert-finetuned-ner | SebastianS | 2022-05-01T21:38:30Z | 4 | 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 | 2022-05-01T21:12:37Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- accuracy
model-index:
- name: bert-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
args: conll2003
metrics:
- name: Accuracy
type: accuracy
value: 0.9910634321093416
---
<!-- 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: 0.0452
- Accuracy: 0.9911
## 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 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0544 | 1.0 | 1756 | 0.0440 | 0.9892 |
| 0.0246 | 2.0 | 3512 | 0.0417 | 0.9906 |
| 0.0105 | 3.0 | 5268 | 0.0452 | 0.9911 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
cfilt/HiNER-collapsed-muril-base-cased | cfilt | 2022-05-01T19:48:15Z | 15 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:cfilt/HiNER-collapsed",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2022-04-29T17:19:39Z | ---
tags:
- generated_from_trainer
datasets:
- cfilt/HiNER-collapsed
metrics:
- precision
- recall
- f1
model-index:
- name: HiNER-collapsed-muril-base-cased
results:
- task:
name: Token Classification
type: token-classification
dataset:
type: cfilt/HiNER-collapsed
name: HiNER Collapsed
metrics:
- name: Precision
type: precision
value: 0.9049101352603298
- name: Recall
type: recall
value: 0.9209156735555891
- name: F1
type: f1
value: 0.9128427506027924
---
<!-- 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. -->
# HiNER-collapsed-muril-base-cased
This model was trained from scratch on the cfilt/HiNER-collapsed dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 1
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10.0
### Framework versions
- Transformers 4.14.0
- Pytorch 1.9.1
- Datasets 1.15.1
- Tokenizers 0.10.3
|
cfilt/HiNER-collapsed-xlm-roberta-large | cfilt | 2022-05-01T19:47:49Z | 95 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:cfilt/HiNER-collapsed",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2022-05-01T06:43:57Z | ---
tags:
- generated_from_trainer
datasets:
- cfilt/HiNER-collapsed
metrics:
- precision
- recall
- f1
model-index:
- name: HiNER-collapsed-xlm-roberta-base
results:
- task:
name: Token Classification
type: token-classification
dataset:
type: cfilt/HiNER-collapsed
name: HiNER Collapsed
metrics:
- name: Precision
type: precision
value: 0.9137448834064936
- name: Recall
type: recall
value: 0.9296549644788663
- name: F1
type: f1
value: 0.9216312652954473
---
<!-- 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. -->
# HiNER-collapsed-xlm-roberta-base
This model was trained from scratch on the cfilt/HiNER-collapsed dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 1
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10.0
### Framework versions
- Transformers 4.14.0
- Pytorch 1.9.1
- Datasets 1.15.1
- Tokenizers 0.10.3
|
tomh/toxigen_roberta | tomh | 2022-05-01T19:42:09Z | 17,839 | 8 | transformers | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"en",
"arxiv:2203.09509",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-05-01T13:19:41Z | ---
language:
- en
tags:
- text-classification
---
Thomas Hartvigsen, Saadia Gabriel, Hamid Palangi, Maarten Sap, Dipankar Ray, Ece Kamar.
This model comes from the paper [ToxiGen: A Large-Scale Machine-Generated Dataset for Adversarial and Implicit Hate Speech Detection](https://arxiv.org/abs/2203.09509) and can be used to detect implicit hate speech.
Please visit the [Github Repository](https://github.com/microsoft/TOXIGEN) for the training dataset and further details.
```bibtex
@inproceedings{hartvigsen2022toxigen,
title = "{T}oxi{G}en: A Large-Scale Machine-Generated Dataset for Adversarial and Implicit Hate Speech Detection",
author = "Hartvigsen, Thomas and Gabriel, Saadia and Palangi, Hamid and Sap, Maarten and Ray, Dipankar and Kamar, Ece",
booktitle = "Proceedings of the 60th Annual Meeting of the Association of Computational Linguistics",
year = "2022"
}
``` |
voidism/diffcse-roberta-base-sts | voidism | 2022-05-01T19:30:19Z | 8 | 1 | transformers | [
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"arxiv:2204.10298",
"arxiv:2104.08821",
"arxiv:2111.00899",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | feature-extraction | 2022-04-14T15:19:51Z | ---
license: apache-2.0
---
# DiffCSE: Difference-based Contrastive Learning for Sentence Embeddings
[](https://github.com/voidism/DiffCSE/)
[](https://colab.research.google.com/github/voidism/DiffCSE/blob/master/diffcse_evaluation.ipynb)
arXiv link: https://arxiv.org/abs/2204.10298
To be published in [**NAACL 2022**](https://2022.naacl.org/)
Authors:
[Yung-Sung Chuang](https://people.csail.mit.edu/yungsung/),
[Rumen Dangovski](http://super-ms.mit.edu/rumen.html),
[Hongyin Luo](http://people.csail.mit.edu/hyluo/),
[Yang Zhang](https://mitibmwatsonailab.mit.edu/people/yang-zhang/),
[Shiyu Chang](https://code-terminator.github.io/),
[Marin Soljačić](http://www.mit.edu/~soljacic/marin.html),
[Shang-Wen Li](https://swdanielli.github.io/),
[Scott Wen-tau Yih](https://scottyih.org/),
[Yoon Kim](https://people.csail.mit.edu/yoonkim/),
[James Glass](http://groups.csail.mit.edu/sls/people/glass.shtml)
Our code is mainly based on the code of [SimCSE](https://arxiv.org/abs/2104.08821). Please refer to their [repository](https://github.com/princeton-nlp/SimCSE) for more detailed information.
## Overview
We propose DiffCSE, an unsupervised contrastive learning framework for learning sentence embeddings. DiffCSE learns sentence embeddings that are sensitive to the difference between the original sentence and an edited sentence, where the edited sentence is obtained by stochastically masking out the original sentence and then sampling from a masked language model. We show that DiffSCE is an instance of equivariant contrastive learning [(Dangovski et al., 2021)](https://arxiv.org/abs/2111.00899), which generalizes contrastive learning and learns representations that are insensitive to certain types of augmentations and sensitive to other "harmful" types of augmentations. Our experiments show that DiffCSE achieves state-of-the-art results among unsupervised sentence representation learning methods, outperforming unsupervised SimCSE by 2.3 absolute points on semantic textual similarity tasks.
## Setups
[](https://www.python.org/downloads/release/python-395/)
### Requirements
* Python 3.9.5
### Install our customized Transformers package
```
cd transformers-4.2.1
pip install .
```
> If you have already installed `transformers==4.2.1` through pip, you need to put `modeling_bert.py` into `<your_python_env>/site-packages/transformers/models/bert/modeling_bert.py` and `modeling_roberta.py` into `<your_python_env>/site-packages/transformers/models/bert/modeling_roberta.py`.
> We modify these two files in the package so that we can perform _conditional_ pretraining tasks using BERT/RoBERTa. If possible, please directly pip install our customized Transformers package.
### Install other packages
```
pip install -r requirements.txt
```
### Download the pretraining dataset
```
cd data
bash download_wiki.sh
```
### Download the downstream dataset
```
cd SentEval/data/downstream/
bash download_dataset.sh
```
## Training
(The same as `run_diffcse.sh`.)
```bash
python train.py \
--model_name_or_path bert-base-uncased \
--generator_name distilbert-base-uncased \
--train_file data/wiki1m_for_simcse.txt \
--output_dir <your_output_model_dir> \
--num_train_epochs 2 \
--per_device_train_batch_size 64 \
--learning_rate 7e-6 \
--max_seq_length 32 \
--evaluation_strategy steps \
--metric_for_best_model stsb_spearman \
--load_best_model_at_end \
--eval_steps 125 \
--pooler_type cls \
--mlp_only_train \
--overwrite_output_dir \
--logging_first_step \
--logging_dir <your_logging_dir> \
--temp 0.05 \
--do_train \
--do_eval \
--batchnorm \
--lambda_weight 0.005 \
--fp16 --masking_ratio 0.30
```
Our new arguments:
* `--lambda_weight`: the lambda coefficient mentioned in Section 3 of our paper.
* `--masking_ratio`: the masking ratio for MLM generator to randomly replace tokens.
* `--generator_name`: the model name of generator. For `bert-base-uncased`, we use `distilbert-base-uncased`. For `roberta-base`, we use `distilroberta-base`.
Arguments from [SimCSE](https://github.com/princeton-nlp/SimCSE):
* `--train_file`: Training file path (`data/wiki1m_for_simcse.txt`).
* `--model_name_or_path`: Pre-trained checkpoints to start with such as BERT-based models (`bert-base-uncased`, `bert-large-uncased`, etc.) and RoBERTa-based models (`RoBERTa-base`, `RoBERTa-large`).
* `--temp`: Temperature for the contrastive loss. We always use `0.05`.
* `--pooler_type`: Pooling method.
* `--mlp_only_train`: For unsupervised SimCSE or DiffCSE, it works better to train the model with MLP layer but test the model without it. You should use this argument when training unsupervised SimCSE/DiffCSE models.
For the results in our paper, we use a NVidia 2080Ti GPU with CUDA 11.2. Using different types of devices or different versions of CUDA/Python/PyTorch may lead to slightly different performance.
## Evaluation
[](https://colab.research.google.com/github/voidism/DiffCSE/blob/master/diffcse_evaluation.ipynb)
We provide a simple colab notebook to reproduce our results easily. We can also run the commands below for evaluation:
```bash
python evaluation.py \
--model_name_or_path <your_output_model_dir> \
--pooler cls_before_pooler \
--task_set <sts|transfer|full> \
--mode test
```
To evaluate our pretrained DiffCSE checkpoints, we can use the following scripts:
### BERT
#### STS
```bash
python evaluation.py \
--model_name_or_path voidism/diffcse-bert-base-uncased-sts \
--pooler cls_before_pooler \
--task_set sts \
--mode test
```
#### Transfer Tasks
```bash
python evaluation.py \
--model_name_or_path voidism/diffcse-bert-base-uncased-trans \
--pooler cls_before_pooler \
--task_set transfer \
--mode test
```
### RoBERTa
#### STS
```bash
python evaluation.py \
--model_name_or_path voidism/diffcse-roberta-base-sts \
--pooler cls_before_pooler \
--task_set sts \
--mode test
```
#### Transfer Tasks
```bash
python evaluation.py \
--model_name_or_path voidism/diffcse-roberta-base-trans \
--pooler cls_before_pooler \
--task_set transfer \
--mode test
```
For more detailed information, please check [SimCSE's GitHub repo](https://github.com/princeton-nlp/SimCSE).
## Pretrained models
[](https://huggingface.co/voidism)
* DiffCSE-BERT-base (STS): https://huggingface.co/voidism/diffcse-bert-base-uncased-sts
* DiffCSE-BERT-base (transfer tasks): https://huggingface.co/voidism/diffcse-bert-base-uncased-trans
* DiffCSE-RoBERTa-base (STS): https://huggingface.co/voidism/diffcse-roberta-base-sts
* DiffCSE-RoBERTa-base (transfer tasks): https://huggingface.co/voidism/diffcse-roberta-base-trans
We can load the models using the API provided by [SimCSE](https://github.com/princeton-nlp/SimCSE).
See [Getting Started](https://github.com/princeton-nlp/SimCSE#getting-started) for more information.
```python
from diffcse import DiffCSE
model_bert_sts = DiffCSE("voidism/diffcse-bert-base-uncased-sts")
model_bert_trans = DiffCSE("voidism/diffcse-bert-base-uncased-trans")
model_roberta_sts = DiffCSE("voidism/diffcse-roberta-base-sts")
model_roberta_trans = DiffCSE("voidism/diffcse-roberta-base-trans")
```
## Citations
[](https://doi.org/10.48550/arXiv.2204.10298)
Please cite our paper and the SimCSE paper if they are helpful to your work!
```bibtex
@inproceedings{chuang2022diffcse,
title={{DiffCSE}: Difference-based Contrastive Learning for Sentence Embeddings},
author={Chuang, Yung-Sung and Dangovski, Rumen and Luo, Hongyin and Zhang, Yang and Chang, Shiyu and Soljacic, Marin and Li, Shang-Wen and Yih, Wen-tau and Kim, Yoon and Glass, James},
booktitle={Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)},
year={2022}
}
@inproceedings{gao2021simcse,
title={{SimCSE}: Simple Contrastive Learning of Sentence Embeddings},
author={Gao, Tianyu and Yao, Xingcheng and Chen, Danqi},
booktitle={Empirical Methods in Natural Language Processing (EMNLP)},
year={2021}
}
```
|
ietz/token-paraphrase-MiniLM-L6-v2 | ietz | 2022-05-01T19:28:23Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"license:apache-2.0",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | feature-extraction | 2022-03-05T19:46:25Z | ---
license: apache-2.0
---
|
hassnain/wav2vec2-base-timit-demo-colab57 | hassnain | 2022-05-01T18:17:07Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2022-05-01T17:06:31Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-timit-demo-colab57
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. -->
# wav2vec2-base-timit-demo-colab57
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7328
- Wer: 0.4593
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 60
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 4.9876 | 7.04 | 500 | 3.1483 | 1.0 |
| 1.4621 | 14.08 | 1000 | 0.6960 | 0.6037 |
| 0.4404 | 21.13 | 1500 | 0.6392 | 0.5630 |
| 0.2499 | 28.17 | 2000 | 0.6738 | 0.5281 |
| 0.1732 | 35.21 | 2500 | 0.6789 | 0.4952 |
| 0.1347 | 42.25 | 3000 | 0.7328 | 0.4835 |
| 0.1044 | 49.3 | 3500 | 0.7258 | 0.4840 |
| 0.0896 | 56.34 | 4000 | 0.7328 | 0.4593 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
hassnain/wav2vec2-base-timit-demo-colab53 | hassnain | 2022-05-01T17:13:03Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2022-05-01T14:11:29Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-timit-demo-colab53
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. -->
# wav2vec2-base-timit-demo-colab53
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.2003
- Wer: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---:|
| 5.619 | 7.04 | 500 | 3.2338 | 1.0 |
| 3.1855 | 14.08 | 1000 | 3.1968 | 1.0 |
| 3.1669 | 21.13 | 1500 | 3.1796 | 1.0 |
| 3.1586 | 28.17 | 2000 | 3.2003 | 1.0 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
rjuez00/meddocan-beto-ner | rjuez00 | 2022-05-01T16:23:58Z | 8 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2022-05-01T16:21:07Z | ---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: beto_full_train_3_epochs
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. -->
# beto_full_train_3_epochs
This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-cased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0445
- Precision: 0.9541
- Recall: 0.9481
- F1: 0.9511
- Accuracy: 0.9951
## 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: 3
- eval_batch_size: 3
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.11.6
|
Siyam/SKYLy | Siyam | 2022-05-01T16:02:55Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2022-05-01T08:47:50Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: SKYLy
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. -->
# SKYLy
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7645
- Wer: 0.4083
## 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: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- 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
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 4.4215 | 4.26 | 400 | 1.6323 | 0.9857 |
| 0.5716 | 8.51 | 800 | 0.6679 | 0.5107 |
| 0.1721 | 12.77 | 1200 | 0.6935 | 0.4632 |
| 0.1063 | 17.02 | 1600 | 0.7533 | 0.4432 |
| 0.0785 | 21.28 | 2000 | 0.7208 | 0.4255 |
| 0.0608 | 25.53 | 2400 | 0.7481 | 0.4117 |
| 0.0493 | 29.79 | 2800 | 0.7645 | 0.4083 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu113
- Datasets 2.1.0
- Tokenizers 0.10.3
|
hassnain/wav2vec2-base-timit-demo-colab9 | hassnain | 2022-05-01T15:58:30Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2022-05-01T09:32:36Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-timit-demo-colab9
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. -->
# wav2vec2-base-timit-demo-colab9
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1922
- Wer: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:---:|
| 5.0683 | 1.42 | 500 | 3.2471 | 1.0 |
| 3.1349 | 2.85 | 1000 | 3.2219 | 1.0 |
| 3.1317 | 4.27 | 1500 | 3.2090 | 1.0 |
| 3.1262 | 5.7 | 2000 | 3.2152 | 1.0 |
| 3.1307 | 7.12 | 2500 | 3.2147 | 1.0 |
| 3.1264 | 8.55 | 3000 | 3.2072 | 1.0 |
| 3.1279 | 9.97 | 3500 | 3.2158 | 1.0 |
| 3.1287 | 11.4 | 4000 | 3.2190 | 1.0 |
| 3.1256 | 12.82 | 4500 | 3.2069 | 1.0 |
| 3.1254 | 14.25 | 5000 | 3.2134 | 1.0 |
| 3.1259 | 15.67 | 5500 | 3.2231 | 1.0 |
| 3.1269 | 17.09 | 6000 | 3.2005 | 1.0 |
| 3.1279 | 18.52 | 6500 | 3.1988 | 1.0 |
| 3.1246 | 19.94 | 7000 | 3.1929 | 1.0 |
| 3.128 | 21.37 | 7500 | 3.1864 | 1.0 |
| 3.1245 | 22.79 | 8000 | 3.1868 | 1.0 |
| 3.1266 | 24.22 | 8500 | 3.1852 | 1.0 |
| 3.1239 | 25.64 | 9000 | 3.1855 | 1.0 |
| 3.125 | 27.07 | 9500 | 3.1917 | 1.0 |
| 3.1233 | 28.49 | 10000 | 3.1929 | 1.0 |
| 3.1229 | 29.91 | 10500 | 3.1922 | 1.0 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
hassnain/wav2vec2-base-timit-demo-colab647 | hassnain | 2022-05-01T15:54:24Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2022-05-01T14:42:45Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-timit-demo-colab647
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. -->
# wav2vec2-base-timit-demo-colab647
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5534
- Wer: 0.4799
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 5.2072 | 7.04 | 500 | 3.7757 | 1.0 |
| 1.2053 | 14.08 | 1000 | 0.6128 | 0.5648 |
| 0.3922 | 21.13 | 1500 | 0.5547 | 0.5035 |
| 0.2157 | 28.17 | 2000 | 0.5534 | 0.4799 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
Yanael/bert-finetuned-mrpc | Yanael | 2022-05-01T15:25:05Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-05-01T14:54:36Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
model-index:
- name: bert-finetuned-mrpc
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-finetuned-mrpc
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.18.0
- Pytorch 1.8.1+cu102
- Datasets 2.1.0
- Tokenizers 0.12.1
|
Rodion/sbert_uno_sustainable_development_goals | Rodion | 2022-05-01T14:33:23Z | 64 | 3 | transformers | [
"transformers",
"pytorch",
"mpnet",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | feature-extraction | 2022-04-26T05:14:40Z |
The SBERT model was trained on the dataset of UNO sustainable development goals. The total dataset size is 20000 records. 16000 were used for training and 4000 for evaluation.
The similarity between records was calculated based on the class similarity:
0 (case 1 - no common classes)
(number of common classes)/(number of all classes) (case 2)
(number of common classes)/(maximal number of record classes)+(number of common classes)/(number of all classes) (case 3)
---
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, max 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 219 with parameters:
```
{'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"callback": null,
"epochs": 2,
"evaluation_steps": 5,
"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 0,
"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 --> |
hassnain/wav2vec2-base-timit-demo-colab50 | hassnain | 2022-05-01T13:32:25Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2022-05-01T10:57:02Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-timit-demo-colab50
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. -->
# wav2vec2-base-timit-demo-colab50
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.2257
- Wer: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---:|
| 5.4568 | 7.04 | 500 | 3.3002 | 1.0 |
| 3.1795 | 14.08 | 1000 | 3.2170 | 1.0 |
| 3.1607 | 21.13 | 1500 | 3.2119 | 1.0 |
| 3.1537 | 28.17 | 2000 | 3.2257 | 1.0 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
hassnain/wav2vec2-base-timit-demo-colab52 | hassnain | 2022-05-01T12:59:06Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2022-05-01T12:14:35Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-timit-demo-colab52
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. -->
# wav2vec2-base-timit-demo-colab52
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7941
- Wer: 0.7501
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 5.3424 | 7.04 | 500 | 3.3225 | 1.0 |
| 2.518 | 14.08 | 1000 | 1.5884 | 0.8300 |
| 1.0217 | 21.13 | 1500 | 1.6643 | 0.7719 |
| 0.6074 | 28.17 | 2000 | 1.7941 | 0.7501 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
hassnain/wav2vec2-base-timit-demo-colab30 | hassnain | 2022-05-01T12:46:21Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2022-05-01T10:21:09Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-timit-demo-colab30
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. -->
# wav2vec2-base-timit-demo-colab30
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8496
- Wer: 0.6534
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 5.2705 | 14.71 | 500 | 3.1073 | 1.0 |
| 1.3631 | 29.41 | 1000 | 0.8496 | 0.6534 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
hassnain/wav2vec2-base-timit-demo-colab51 | hassnain | 2022-05-01T11:59:55Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2022-05-01T11:15:50Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-timit-demo-colab51
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. -->
# wav2vec2-base-timit-demo-colab51
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8395
- Wer: 0.7480
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 5.481 | 7.04 | 500 | 3.2834 | 1.0 |
| 2.2521 | 14.08 | 1000 | 1.6333 | 0.8093 |
| 0.9467 | 21.13 | 1500 | 1.7458 | 0.7560 |
| 0.5888 | 28.17 | 2000 | 1.8395 | 0.7480 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
huggingtweets/sandspiel_feed | huggingtweets | 2022-05-01T11:28:20Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-05-01T10:34:20Z | ---
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/1073861926097117184/FB3bBgcN_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">sandspiel</div>
<div style="text-align: center; font-size: 14px;">@sandspiel_feed</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 sandspiel.
| Data | sandspiel |
| --- | --- |
| Tweets downloaded | 3200 |
| Retweets | 2 |
| Short tweets | 1506 |
| Tweets kept | 1692 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3fvrcwe0/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 @sandspiel_feed's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/24l7h3az) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/24l7h3az/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/sandspiel_feed')
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)
|
sameearif88/wav2vec2-base-timit-demo-colab7 | sameearif88 | 2022-05-01T11:12:28Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2022-05-01T10:15:02Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-timit-demo-colab7
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. -->
# wav2vec2-base-timit-demo-colab7
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6917
- Wer: 0.5426
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1400
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 5.1854 | 13.89 | 500 | 3.1687 | 1.0 |
| 1.7033 | 27.78 | 1000 | 0.7289 | 0.5659 |
| 0.4208 | 41.67 | 1500 | 0.6917 | 0.5426 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
huggingtweets/a_ergt-sausifaktai-suuiluap | huggingtweets | 2022-05-01T11:05:56Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-05-01T11:05:49Z | ---
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/1512730099614953472/dyaBioOx_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/703268070962372608/sWc1Y_Ch_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/783999503711997952/BHnn3C1Z_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">Æ𝚐𝚛𝚝 & Sausi Faktai & Pαulius</div>
<div style="text-align: center; font-size: 14px;">@a_ergt-sausifaktai-suuiluap</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 Æ𝚐𝚛𝚝 & Sausi Faktai & Pαulius.
| Data | Æ𝚐𝚛𝚝 | Sausi Faktai | Pαulius |
| --- | --- | --- | --- |
| Tweets downloaded | 3241 | 3194 | 3192 |
| Retweets | 299 | 19 | 811 |
| Short tweets | 977 | 16 | 484 |
| Tweets kept | 1965 | 3159 | 1897 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3bn9w1ob/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 @a_ergt-sausifaktai-suuiluap's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3txmfh51) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3txmfh51/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/a_ergt-sausifaktai-suuiluap')
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)
|
sameearif88/wav2vec2-base-timit-demo-colab10 | sameearif88 | 2022-05-01T11:00:20Z | 6 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2022-05-01T09:25:20Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-timit-demo-colab10
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. -->
# wav2vec2-base-timit-demo-colab10
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4460
- Wer: 0.3425
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 4.9891 | 3.52 | 500 | 3.1554 | 1.0 |
| 1.71 | 7.04 | 1000 | 0.7122 | 0.5811 |
| 0.6164 | 10.56 | 1500 | 0.5149 | 0.4880 |
| 0.4188 | 14.08 | 2000 | 0.4726 | 0.4344 |
| 0.3038 | 17.61 | 2500 | 0.4765 | 0.4092 |
| 0.2312 | 21.13 | 3000 | 0.4387 | 0.3765 |
| 0.1867 | 24.65 | 3500 | 0.4411 | 0.3583 |
| 0.1582 | 28.17 | 4000 | 0.4460 | 0.3425 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
hassnain/wav2vec2-base-timit-demo-colab11 | hassnain | 2022-05-01T10:54:00Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2022-05-01T09:49:05Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-timit-demo-colab11
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. -->
# wav2vec2-base-timit-demo-colab11
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6269
- Wer: 0.7418
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 5.6439 | 7.04 | 500 | 3.3083 | 1.0 |
| 2.3763 | 14.08 | 1000 | 1.5059 | 0.8146 |
| 1.0161 | 21.13 | 1500 | 1.5101 | 0.7488 |
| 0.6195 | 28.17 | 2000 | 1.6269 | 0.7418 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
hassnain/wav2vec2-base-timit-demo-colab7 | hassnain | 2022-05-01T09:02:18Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2022-05-01T07:40:34Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-timit-demo-colab7
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. -->
# wav2vec2-base-timit-demo-colab7
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1687
- Wer: 0.6478
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 60
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 4.8409 | 7.04 | 500 | 3.1487 | 1.0 |
| 2.6259 | 14.08 | 1000 | 1.5598 | 0.8730 |
| 1.083 | 21.13 | 1500 | 1.0600 | 0.7347 |
| 0.6061 | 28.17 | 2000 | 1.0697 | 0.7006 |
| 0.4022 | 35.21 | 2500 | 1.0617 | 0.6913 |
| 0.2884 | 42.25 | 3000 | 1.1962 | 0.6768 |
| 0.225 | 49.3 | 3500 | 1.1753 | 0.6567 |
| 0.1852 | 56.34 | 4000 | 1.1687 | 0.6478 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
cuzeverynameistaken/wav2vec2-base-timit-demo-colab0 | cuzeverynameistaken | 2022-05-01T08:59:37Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2022-04-30T21:06:44Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-timit-demo-colab0
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. -->
# wav2vec2-base-timit-demo-colab0
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6960
- Wer: 0.5694
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 5.3196 | 13.89 | 500 | 3.1225 | 1.0 |
| 1.2756 | 27.78 | 1000 | 0.6960 | 0.5694 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
sameearif88/wav2vec2-base-timit-demo-colab4 | sameearif88 | 2022-05-01T08:37:50Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2022-05-01T07:59:05Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-timit-demo-colab4
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. -->
# wav2vec2-base-timit-demo-colab4
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9149
- Wer: 0.5907
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 800
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 4.9363 | 13.89 | 500 | 2.7532 | 1.0 |
| 0.9875 | 27.78 | 1000 | 0.9149 | 0.5907 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
sherry7144/wav2vec2-base-timit-demo-colab1 | sherry7144 | 2022-05-01T08:08:05Z | 13 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2022-05-01T07:01:31Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-timit-demo-colab1
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. -->
# wav2vec2-base-timit-demo-colab1
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0358
- Wer: 0.5729
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 800
- num_epochs: 35
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.3217 | 13.89 | 500 | 0.8951 | 0.5834 |
| 0.2263 | 27.78 | 1000 | 1.0358 | 0.5729 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
shumail/wav2vec2-base-timit-demo-colab | shumail | 2022-05-01T07:13:08Z | 6 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2022-04-30T12:34:29Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-timit-demo-colab
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. -->
# wav2vec2-base-timit-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8686
- Wer: 0.6263
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 5.0505 | 13.89 | 500 | 3.0760 | 1.0 |
| 1.2748 | 27.78 | 1000 | 0.8686 | 0.6263 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
hassnain/wav2vec2-base-timit-demo-colab3 | hassnain | 2022-05-01T07:06:20Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2022-05-01T00:50:44Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-timit-demo-colab3
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. -->
# wav2vec2-base-timit-demo-colab3
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1016
- Wer: 0.6704
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 60
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 5.0006 | 13.89 | 500 | 3.0706 | 1.0 |
| 1.8796 | 27.78 | 1000 | 1.1154 | 0.7414 |
| 0.548 | 41.67 | 1500 | 1.0826 | 0.7034 |
| 0.2747 | 55.56 | 2000 | 1.1016 | 0.6704 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
hassnain/wav2vec2-base-timit-demo-colab1 | hassnain | 2022-05-01T05:22:37Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2022-04-30T22:09:18Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-timit-demo-colab1
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. -->
# wav2vec2-base-timit-demo-colab1
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1904
- Wer: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:---:|
| 5.0877 | 1.42 | 500 | 3.2909 | 1.0 |
| 3.1333 | 2.85 | 1000 | 3.2624 | 1.0 |
| 3.1335 | 4.27 | 1500 | 3.2121 | 1.0 |
| 3.1294 | 5.7 | 2000 | 3.2047 | 1.0 |
| 3.1307 | 7.12 | 2500 | 3.2020 | 1.0 |
| 3.1279 | 8.55 | 3000 | 3.1978 | 1.0 |
| 3.1296 | 9.97 | 3500 | 3.2015 | 1.0 |
| 3.1273 | 11.4 | 4000 | 3.1983 | 1.0 |
| 3.1273 | 12.82 | 4500 | 3.2258 | 1.0 |
| 3.1274 | 14.25 | 5000 | 3.2151 | 1.0 |
| 3.1256 | 15.67 | 5500 | 3.2105 | 1.0 |
| 3.1302 | 17.09 | 6000 | 3.2018 | 1.0 |
| 3.1285 | 18.52 | 6500 | 3.2006 | 1.0 |
| 3.1251 | 19.94 | 7000 | 3.1858 | 1.0 |
| 3.1283 | 21.37 | 7500 | 3.1829 | 1.0 |
| 3.1267 | 22.79 | 8000 | 3.1773 | 1.0 |
| 3.1283 | 24.22 | 8500 | 3.1857 | 1.0 |
| 3.1253 | 25.64 | 9000 | 3.1847 | 1.0 |
| 3.1251 | 27.07 | 9500 | 3.1832 | 1.0 |
| 3.1245 | 28.49 | 10000 | 3.1869 | 1.0 |
| 3.1225 | 29.91 | 10500 | 3.1904 | 1.0 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
ouyh18/distilbert-base-uncased-finetuned-cola | ouyh18 | 2022-05-01T03:43:35Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-05-01T02:34:13Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: distilbert-base-uncased-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.5500173690801187
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8456
- Matthews Correlation: 0.5500
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5197 | 1.0 | 535 | 0.5477 | 0.4130 |
| 0.3456 | 2.0 | 1070 | 0.5035 | 0.5239 |
| 0.2342 | 3.0 | 1605 | 0.6100 | 0.5285 |
| 0.1698 | 4.0 | 2140 | 0.7556 | 0.5456 |
| 0.1295 | 5.0 | 2675 | 0.8456 | 0.5500 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.1+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
charlieoneill/distilbert-base-uncased-finetuned-tweet_eval-offensive | charlieoneill | 2022-05-01T03:36:21Z | 11 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:tweet_eval",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-05-01T03:22:31Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-tweet_eval-offensive
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tweet_eval
type: tweet_eval
args: offensive
metrics:
- name: Accuracy
type: accuracy
value: 0.8089123867069486
- name: F1
type: f1
value: 0.8060281168230459
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-tweet_eval-offensive
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4185
- Accuracy: 0.8089
- F1: 0.8060
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 187 | 0.4259 | 0.8059 | 0.7975 |
| 0.46 | 2.0 | 374 | 0.4185 | 0.8089 | 0.8060 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.9.1
- Datasets 2.1.0
- Tokenizers 0.12.1
|
princeton-nlp/CoFi-MNLI-s95 | princeton-nlp | 2022-05-01T01:20:45Z | 15 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"text-classification",
"arxiv:2204.00408",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-29T21:57:29Z | This is a model checkpoint for "[Structured Pruning Learns Compact and Accurate Models](https://arxiv.org/pdf/2204.00408.pdf)". The model is pruned from `bert-base-uncased` to a 95% sparsity on dataset MNLI. Please go to [our repository](https://github.com/princeton-nlp/CoFiPruning) for more details on how to use the model for inference. Note that you would have to use the model class specified in our repository to load the model.
|
princeton-nlp/CoFi-MNLI-s60 | princeton-nlp | 2022-05-01T01:20:27Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"text-classification",
"arxiv:2204.00408",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-29T21:58:04Z | This is a model checkpoint for "[Structured Pruning Learns Compact and Accurate Models](https://arxiv.org/pdf/2204.00408.pdf)". The model is pruned from `bert-base-uncased` to a 60% sparsity on dataset MNLI. Please go to [our repository](https://github.com/princeton-nlp/CoFiPruning) for more details on how to use the model for inference. Note that you would have to use the model class specified in our repository to load the model.
|
princeton-nlp/CoFi-SST2-s95 | princeton-nlp | 2022-05-01T01:19:38Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"text-classification",
"arxiv:2204.00408",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-29T21:58:56Z | This is a model checkpoint for "[Structured Pruning Learns Compact and Accurate Models](https://arxiv.org/pdf/2204.00408.pdf)". The model is pruned from `bert-base-uncased` to a 95% sparsity on dataset SST-2. Please go to [our repository](https://github.com/princeton-nlp/CoFiPruning) for more details on how to use the model for inference. Note that you would have to use the model class specified in our repository to load the model.
|
tahazakir/wav2vec2-base-timit-demo-colab2 | tahazakir | 2022-04-30T22:54:15Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2022-04-30T20:32:56Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-timit-demo-colab2
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. -->
# wav2vec2-base-timit-demo-colab2
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1899
- Wer: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---:|
| 8.0486 | 13.89 | 500 | 3.6570 | 1.0 |
| 3.2905 | 27.78 | 1000 | 3.1899 | 1.0 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
moaiz237/wav2vec2-base-timit-moaiz_explast | moaiz237 | 2022-04-30T22:11:49Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2022-04-30T21:18:59Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-timit-moaiz_explast
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. -->
# wav2vec2-base-timit-moaiz_explast
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6714
- Wer: 0.5404
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 40
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 4.034 | 13.89 | 500 | 1.0507 | 0.6871 |
| 0.6024 | 27.78 | 1000 | 0.6714 | 0.5404 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
LiYuan/amazon-review-sentiment-analysis | LiYuan | 2022-04-30T22:03:23Z | 4,927 | 41 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-04-30T20:37:44Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-mnli-amazon-query-shopping
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-mnli-amazon-query-shopping
This model is a fine-tuned version of [nlptown/bert-base-multilingual-uncased-sentiment](https://huggingface.co/nlptown/bert-base-multilingual-uncased-sentiment?text=I+like+you.+I+love+you) on an [Amazon US Customer Reviews Dataset](https://www.kaggle.com/datasets/cynthiarempel/amazon-us-customer-reviews-dataset). The code for the fine-tuning process can be found
[here](https://github.com/vanderbilt-data-science/bigdata/blob/main/06-fine-tune-BERT-on-our-dataset.ipynb). This model is uncased: it does
not make a difference between english and English.
It achieves the following results on the evaluation set:
- Loss: 0.5202942490577698
- Accuracy: 0.8
## Model description
This a bert-base-multilingual-uncased model finetuned for sentiment analysis on product reviews in six languages: English, Dutch, German, French, Spanish and Italian. It predicts the sentiment of the review as a number of stars (between 1 and 5).
This model is intended for direct use as a sentiment analysis model for product reviews in any of the six languages above, or for further finetuning on related sentiment analysis tasks.
We replaced its head with our customer reviews to fine-tune it on 17,280 rows of training set while validating it on 4,320 rows of dev set. Finally, we evaluated our model performance on a held-out test set: 2,400 rows.
## Intended uses & limitations
Bert-base is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)
to make decisions, such as sequence classification, token classification, or question answering. This fine-tuned version of BERT-base is used to predict review rating star given the review.
The limitations are this trained model is focusing on reviews and products on Amazon. If you apply this model to other domains, it may perform poorly.
## How to use
You can use this model directly by downloading the trained weights and configurations like the below code snippet:
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("LiYuan/amazon-review-sentiment-analysis")
model = AutoModelForSequenceClassification.from_pretrained("LiYuan/amazon-review-sentiment-analysis")
```
## Training and evaluation data
Download all the raw [dataset](https://www.kaggle.com/datasets/cynthiarempel/amazon-us-customer-reviews-dataset) from the Kaggle website.
## 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
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.555400 | 1.0 | 1080 | 0.520294 | 0.800000 |
| 0.424300 | 2.0 | 1080 | 0.549649 | 0.798380 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1 |
ChrisZeng/t5-base-detox | ChrisZeng | 2022-04-30T21:53:04Z | 9 | 0 | transformers | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-04-30T17:43:42Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: t5-base-detox
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. -->
# t5-base-detox
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2615
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.337 | 1.0 | 135 | 0.4810 |
| 0.5238 | 2.0 | 270 | 0.3886 |
| 0.4301 | 3.0 | 405 | 0.3378 |
| 0.3755 | 4.0 | 540 | 0.3122 |
| 0.3359 | 5.0 | 675 | 0.2910 |
| 0.3068 | 6.0 | 810 | 0.2737 |
| 0.2861 | 7.0 | 945 | 0.2710 |
| 0.2744 | 8.0 | 1080 | 0.2617 |
| 0.2649 | 9.0 | 1215 | 0.2630 |
| 0.2585 | 10.0 | 1350 | 0.2615 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.12.0.dev20220429
- Datasets 2.1.0
- Tokenizers 0.10.3
|
hassnain/wav2vec2-base-timit-demo-colab | hassnain | 2022-04-30T20:20:34Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2022-04-29T14:46:57Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-timit-demo-colab
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. -->
# wav2vec2-base-timit-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- 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
- lr_scheduler_warmup_steps: 1000
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
sherry7144/wav2vec2-base-timit-demo-colab0 | sherry7144 | 2022-04-30T20:04:12Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2022-04-30T15:52:29Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-timit-demo-colab0
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. -->
# wav2vec2-base-timit-demo-colab0
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0395
- Wer: 0.5635
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.3976 | 13.89 | 500 | 0.8616 | 0.5968 |
| 0.2637 | 27.78 | 1000 | 0.9973 | 0.5826 |
| 0.1794 | 41.67 | 1500 | 1.0395 | 0.5635 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
moaiz237/wav2vec2-base-timit-moaiz_exp2_new | moaiz237 | 2022-04-30T20:03:49Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2022-04-30T19:19:12Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-timit-moaiz_exp2_new
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. -->
# wav2vec2-base-timit-moaiz_exp2_new
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6849
- Wer: 0.5396
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 4.1266 | 13.89 | 500 | 1.0233 | 0.7034 |
| 0.5928 | 27.78 | 1000 | 0.6849 | 0.5396 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
ahmad573/wav2vec2-base-timit-demo-colab2 | ahmad573 | 2022-04-30T19:12:53Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2022-04-30T15:19:55Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-timit-demo-colab2
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. -->
# wav2vec2-base-timit-demo-colab2
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1914
- Wer: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- 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
- lr_scheduler_warmup_steps: 700
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---:|
| 3.8196 | 7.04 | 500 | 3.2201 | 1.0 |
| 3.1517 | 14.08 | 1000 | 3.1876 | 1.0 |
| 3.1493 | 21.13 | 1500 | 3.1837 | 1.0 |
| 3.1438 | 28.17 | 2000 | 3.1914 | 1.0 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
ali221000262/wav2vec2-base-timit-ali-hasan-colab-EX2 | ali221000262 | 2022-04-30T19:02:59Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2022-04-30T17:42:47Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-timit-ali-hasan-colab-EX2
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. -->
# wav2vec2-base-timit-ali-hasan-colab-EX2
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5087
- Wer: 0.4458
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.1956 | 13.89 | 500 | 0.5087 | 0.4458 |
| 0.1946 | 27.78 | 1000 | 0.5087 | 0.4458 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
julycodes/wav2vec2-base-timit-demo-colab-2 | julycodes | 2022-04-30T18:57:05Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2022-04-30T15:53:37Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-timit-demo-colab-2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-colab-2
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7429
- Wer: 0.5080
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 10
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 900
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.984 | 8.77 | 500 | 0.9028 | 0.7036 |
| 0.6412 | 17.54 | 1000 | 0.7275 | 0.5868 |
| 0.3073 | 26.32 | 1500 | 0.7429 | 0.5080 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
ParanoidAndroid/bert-finetuned-squad | ParanoidAndroid | 2022-04-30T18:29:58Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | question-answering | 2022-04-30T18:16:42Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bert-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. -->
# bert-finetuned-squad
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
ali221000262/wav2vec2-base-timit-demo-colab | ali221000262 | 2022-04-30T18:01:43Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2022-04-30T13:26:28Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-timit-demo-colab
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. -->
# wav2vec2-base-timit-demo-colab
This model is a fine-tuned version of [ali221000262/wav2vec2-base-timit-demo-colab](https://huggingface.co/ali221000262/wav2vec2-base-timit-demo-colab) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.2161
- Wer: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.01
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 25
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---:|
| 2.6432 | 13.89 | 500 | 3.2161 | 1.0 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
tahazakir/wav2vec2-base-timit-demo-colab0 | tahazakir | 2022-04-30T18:01:33Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2022-04-30T15:37:39Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-timit-demo-colab0
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. -->
# wav2vec2-base-timit-demo-colab0
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8768
- Wer: 0.6089
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 5.1121 | 13.89 | 500 | 2.9931 | 1.0 |
| 1.1475 | 27.78 | 1000 | 0.8768 | 0.6089 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
ali221000262/wav2vec2-base-timit-ali-hasan-colab | ali221000262 | 2022-04-30T17:36:34Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2022-04-30T17:04:44Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-timit-ali-hasan-colab
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. -->
# wav2vec2-base-timit-ali-hasan-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.2471
- Wer: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.01
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 25
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---:|
| 3.5485 | 13.89 | 500 | 3.2471 | 1.0 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
ningkko/drug-stance-bert | ningkko | 2022-04-30T17:29:17Z | 13 | 1 | transformers | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-17T21:05:00Z | ---
tags:
- generated_from_trainer
model-index:
- name: drug-stance-bert
results: [1, 0, 2]
---
<!-- 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. -->
# drug-stance-bert
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-sentiment](https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment) on [COVID-CQ](https://github.com/eceveco/COVID-CQ), a dataset that contains 3-label annotated opinions (negative, neutral, and positive) of the tweet initiators regarding the use of Chloroquine or Hydroxychloroquine for the treatment or prevention of the coronavirus.
## Intended uses & limitations
Predict opinions (negative, neutral, and positive) of tweet initiators regarding the use of a drug for the treatment or prevention of the coronavirus. Note that having multiple drug names with different stances in a single tweet can confuse the model.
## Inference & understanding
We followed COVID-CQ to use the following label representation:
- 0 -> None/Neutral;
- 1 -> Against;
- 2 -> Favor
Try these examples:
- The gov's killing people by banning Ivm
- Great news cheers everybody:) ivermectin proven to not work by rct lol
## Tutorial
See our Github repo for [inference scripts](https://github.com/ningkko/COVID-drug/blob/main/stance_detection/inference.ipynb)
## Model description
"We developed two COVID-drug-stance RoBERTa-base models by fine-tuning a pre-trained Twitter-specific stance detection model on a stance data set called COVID-CQ. The data were divided into training-dev-test validation datasets with a 70:10:20 ratio. Model I (COVID-drug-stance-BERT) was trained on the original tweet data, and Model II (COVID-drug-stance-BERT-masked) was trained on tweets with drug names masked as “[mask]” for model generalizability on different drugs. The two models had similar performance on the COVID-19 validation set: COVID-drug-stance-BERT had an accuracy of 86.88%, and the masked model had an accuracy of 86.67%. The two models were then evaluated by predicting tweet initiators’ attitudes towards the drug mentioned in each tweet using randomly selected test sets (100 tweets) of each drug (Hydroxychloquine, Ivermectin, Molnupiravir, Remdesivir). As suggested by the evaluation in Table 2, Model I had better performance and was therefore used in this study".
| **Drug** | **Model I: Original Tweet** | | | **Model II: Drug Names Masked** | | |
|------------------------|:---------------------------:|:-----------:|:------------:|:-------------------------------:|:-----------:|:------------:|
| | **Precision** | **Recall** | **F1-Score** | **Precision** | **Recall** | **F1-Score** |
| **Hydroxychloroquine** | 0.93 | 0.92 | **0.92** | 0.84 | 0.83 | 0.83 |
| **Ivermectin** | 0.92 | 0.91 | **0.91** | 0.72 | 0.68 | 0.68 |
| **Molnupiravir** | 0.89 | 0.89 | **0.89** | 0.78 | 0.77 | 0.77 |
| **Remdesivir** | 0.82 | 0.79 | **0.79** | 0.70 | 0.66 | 0.66 |
The model uploaded here is Model I.
## Training and evaluation data
COVID-CQ
## Training procedure
See Github
### 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: 3.0
### Framework versions
- Transformers 4.11.0
- Pytorch 1.8.1+cu102
- Datasets 1.15.1
- Tokenizers 0.10.3
|
moaiz237/wav2vec2-base-timit-moaiz_exp1 | moaiz237 | 2022-04-30T15:13:12Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2022-04-30T12:17:17Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-timit-moaiz_exp1
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. -->
# wav2vec2-base-timit-moaiz_exp1
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6910
- Wer: 0.5549
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 4.7261 | 13.89 | 500 | 2.4864 | 0.9942 |
| 1.0036 | 27.78 | 1000 | 0.6910 | 0.5549 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
maxime7770/model | maxime7770 | 2022-04-30T15:12:40Z | 5 | 0 | transformers | [
"transformers",
"tf",
"camembert",
"text-classification",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-04-29T11:54:14Z | ---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: maxime7770/model
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. -->
# maxime7770/model
This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1211
- Validation Loss: 0.4812
- Epoch: 49
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 650, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 1.5966 | 1.5898 | 0 |
| 1.5577 | 1.5576 | 1 |
| 1.5034 | 1.4761 | 2 |
| 1.4034 | 1.3538 | 3 |
| 1.2864 | 1.2163 | 4 |
| 1.1502 | 1.0980 | 5 |
| 1.0085 | 0.9988 | 6 |
| 0.8828 | 0.9130 | 7 |
| 0.7863 | 0.8445 | 8 |
| 0.7036 | 0.7871 | 9 |
| 0.6322 | 0.7399 | 10 |
| 0.5731 | 0.7030 | 11 |
| 0.5180 | 0.6714 | 12 |
| 0.4757 | 0.6432 | 13 |
| 0.4366 | 0.6204 | 14 |
| 0.4057 | 0.6006 | 15 |
| 0.3743 | 0.5827 | 16 |
| 0.3475 | 0.5689 | 17 |
| 0.3221 | 0.5577 | 18 |
| 0.2971 | 0.5467 | 19 |
| 0.2815 | 0.5372 | 20 |
| 0.2700 | 0.5297 | 21 |
| 0.2521 | 0.5225 | 22 |
| 0.2343 | 0.5168 | 23 |
| 0.2265 | 0.5117 | 24 |
| 0.2143 | 0.5074 | 25 |
| 0.2063 | 0.5038 | 26 |
| 0.1941 | 0.5001 | 27 |
| 0.1843 | 0.4976 | 28 |
| 0.1782 | 0.4949 | 29 |
| 0.2012 | 0.4938 | 30 |
| 0.1691 | 0.4930 | 31 |
| 0.1626 | 0.4910 | 32 |
| 0.1884 | 0.4886 | 33 |
| 0.1547 | 0.4870 | 34 |
| 0.1492 | 0.4858 | 35 |
| 0.1445 | 0.4850 | 36 |
| 0.1415 | 0.4842 | 37 |
| 0.1383 | 0.4836 | 38 |
| 0.1374 | 0.4832 | 39 |
| 0.1336 | 0.4826 | 40 |
| 0.1322 | 0.4823 | 41 |
| 0.1295 | 0.4820 | 42 |
| 0.1268 | 0.4818 | 43 |
| 0.1261 | 0.4816 | 44 |
| 0.1253 | 0.4815 | 45 |
| 0.1275 | 0.4814 | 46 |
| 0.1247 | 0.4812 | 47 |
| 0.1256 | 0.4812 | 48 |
| 0.1211 | 0.4812 | 49 |
### Framework versions
- Transformers 4.18.0
- TensorFlow 2.8.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
Davincilee/door_inner | Davincilee | 2022-04-30T15:07:38Z | 0 | 1 | null | [
"region:us"
] | null | 2022-04-30T14:47:04Z | language:
- "List of ISO 639-1 code for your language" |
Muennighoff/t5-small-finetuned-xsum | Muennighoff | 2022-04-30T14:26:40Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:xsum",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-04-30T14:15:00Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- xsum
metrics:
- rouge
model-index:
- name: t5-small-finetuned-xsum
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: xsum
type: xsum
args: default
metrics:
- name: Rouge1
type: rouge
value: 28.2881
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-xsum
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4784
- Rouge1: 28.2881
- Rouge2: 7.6834
- Rougel: 22.2163
- Rougelsum: 22.219
- Gen Len: 18.8292
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 2.7184 | 1.0 | 12753 | 2.4784 | 28.2881 | 7.6834 | 22.2163 | 22.219 | 18.8292 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
sameearif88/wav2vec2-base-timit-demo-colab | sameearif88 | 2022-04-30T13:08:28Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2022-04-26T10:31:51Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-timit-demo-colab
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. -->
# wav2vec2-base-timit-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
adielsa/distilbert-base-uncased-finetuned-cola | adielsa | 2022-04-30T12:37:50Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-04-30T12:16:33Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: distilbert-base-uncased-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.5387376669923544
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8256
- Matthews Correlation: 0.5387
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5257 | 1.0 | 535 | 0.5286 | 0.4093 |
| 0.3447 | 2.0 | 1070 | 0.5061 | 0.4972 |
| 0.2303 | 3.0 | 1605 | 0.5878 | 0.5245 |
| 0.1761 | 4.0 | 2140 | 0.7969 | 0.5153 |
| 0.1346 | 5.0 | 2675 | 0.8256 | 0.5387 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
ai4bharat/MultiIndicSentenceSummarizationSS | ai4bharat | 2022-04-30T10:35:01Z | 6 | 1 | transformers | [
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"sentence-summarization",
"multilingual",
"nlp",
"indicnlp",
"as",
"bn",
"gu",
"hi",
"kn",
"ml",
"mr",
"or",
"pa",
"ta",
"te",
"dataset:ai4bharat/IndicSentenceSummarization",
"arxiv:2203.05437",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-04-23T17:54:14Z | ---
tags:
- sentence-summarization
- multilingual
- nlp
- indicnlp
datasets:
- ai4bharat/IndicSentenceSummarization
language:
- as
- bn
- gu
- hi
- kn
- ml
- mr
- or
- pa
- ta
- te
license:
- mit
widget:
- जम्मू एवं कश्मीर के अनंतनाग जिले में शनिवार को सुरक्षाबलों के साथ मुठभेड़ में दो आतंकवादियों को मार गिराया गया। <s> <2hi>
---
# MultiIndicSentenceSummarizationSS
This repository contains the [IndicBARTSS](https://huggingface.co/ai4bharat/IndicBARTSS) checkpoint finetuned on the 11 languages of [IndicSentenceSummarization](https://huggingface.co/datasets/ai4bharat/IndicSentenceSummarization) dataset. For finetuning details,
see the [paper](https://arxiv.org/abs/2203.05437).
<ul>
<li >Supported languages: Assamese, Bengali, Gujarati, Hindi, Marathi, Odiya, Punjabi, Kannada, Malayalam, Tamil, and Telugu. Not all of these languages are supported by mBART50 and mT5. </li>
<li >The model is much smaller than the mBART and mT5(-base) models, so less computationally expensive for decoding. </li>
<li> Trained on large Indic language corpora (5.53 million sentences). </li>
<li> Unlike <a href="https://huggingface.co/ai4bharat/MultiIndicSentenceSummarization">MultiIndicSentenceSummarization</a> each language is written in its own script, so you do not need to perform any script mapping to/from Devanagari. </li>
</ul>
## Using this model in `transformers`
```
from transformers import MBartForConditionalGeneration, AutoModelForSeq2SeqLM
from transformers import AlbertTokenizer, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("ai4bharat/MultiIndicSentenceSummarizationSS", do_lower_case=False, use_fast=False, keep_accents=True)
# Or use tokenizer = AlbertTokenizer.from_pretrained("ai4bharat/MultiIndicSentenceSummarizationSS", do_lower_case=False, use_fast=False, keep_accents=True)
model = AutoModelForSeq2SeqLM.from_pretrained("ai4bharat/MultiIndicSentenceSummarizationSS")
# Or use model = MBartForConditionalGeneration.from_pretrained("ai4bharat/MultiIndicSentenceSummarizationSS")
# Some initial mapping
bos_id = tokenizer._convert_token_to_id_with_added_voc("<s>")
eos_id = tokenizer._convert_token_to_id_with_added_voc("</s>")
pad_id = tokenizer._convert_token_to_id_with_added_voc("<pad>")
# To get lang_id use any of ['<2as>', '<2bn>', '<2en>', '<2gu>', '<2hi>', '<2kn>', '<2ml>', '<2mr>', '<2or>', '<2pa>', '<2ta>', '<2te>']
# First tokenize the input. The format below is how IndicBART was trained so the input should be "Sentence </s> <2xx>" where xx is the language code. Similarly, the output should be "<2yy> Sentence </s>".
inp = tokenizer("जम्मू एवं कश्मीर के अनंतनाग जिले में शनिवार को सुरक्षाबलों के साथ मुठभेड़ में दो आतंकवादियों को मार गिराया गया। </s> <2hi>", add_special_tokens=False, return_tensors="pt", padding=True).input_ids
# For generation. Pardon the messiness. Note the decoder_start_token_id.
model_output=model.generate(inp, use_cache=True,no_repeat_ngram_size=3, num_beams=5, length_penalty=0.8, max_length=20, min_length=1, early_stopping=True, pad_token_id=pad_id, bos_token_id=bos_id, eos_token_id=eos_id, decoder_start_token_id=tokenizer._convert_token_to_id_with_added_voc("<2hi>"))
# Decode to get output strings
decoded_output=tokenizer.decode(model_output[0], skip_special_tokens=True, clean_up_tokenization_spaces=False)
print(decoded_output) # अनंतनाग में सुरक्षाबलों के साथ मुठभेड़ में दो आतंकवादी ढेर
```
## Benchmarks
Scores on the `IndicSentenceSummarization` test sets are as follows:
Language | Rouge-1 / Rouge-2 / Rouge-L
---------|----------------------------
as | 63.56 / 49.90 / 62.57
bn | 52.52 / 36.15 / 50.60
gu | 47.69 / 29.77 / 45.61
hi | 50.43 / 28.13 / 45.15
kn | 77.06 / 69.36 / 76.33
ml | 65.00 / 51.99 / 63.76
mr | 47.05 / 25.97 / 45.52
or | 50.96 / 30.32 / 49.23
pa | 54.95 / 36.26 / 51.26
ta | 58.52 / 38.36 / 56.49
te | 53.75 / 35.17 / 52.66
## Citation
If you use this model, please cite the following paper:
```
@inproceedings{Kumar2022IndicNLGSM,
title={IndicNLG Suite: Multilingual Datasets for Diverse NLG Tasks in Indic Languages},
author={Aman Kumar and Himani Shrotriya and Prachi Sahu and Raj Dabre and Ratish Puduppully and Anoop Kunchukuttan and Amogh Mishra and Mitesh M. Khapra and Pratyush Kumar},
year={2022},
url = "https://arxiv.org/abs/2203.05437"
}
```
|
DrishtiSharma/TEST123 | DrishtiSharma | 2022-04-30T10:24:56Z | 0 | 0 | null | [
"tflite",
"mixtec",
"region:us"
] | null | 2022-04-30T10:11:52Z | ---
tags:
- mixtec
# See a list of available tags here:
# https://coqui.ai/mixtec/jemeyer/v1.0.0#model-details
# task: Speech-to-Text for the Yoloxóchitl Mixtec Language on 16kHz, mono-channel audio
---
# Model card for Yoloxóchitl Mixtec STT
Jump to section:
- [Model details](#model-details)
- [Intended use](#intended-use)
- [Performance Factors](#performance-factors)
- [Metrics](#metrics)
- [Training data](#training-data)
- [Evaluation data](#evaluation-data)
- [Ethical considerations](#ethical-considerations)
- [Caveats and recommendations](#caveats-and-recommendations)
## Model details
- Person or organization developing model: Originally trained by [Joe Meyer](https://www.linkedin.com/in/joe-meyer-25753951/).
- Model language: Yoloxóchitl Mixtec / / `xty`
- Model date: April 17, 2022
- Model type: `Speech-to-Text`
- Model version: `v0.1.0`
- Compatible with 🐸 STT version: `v1.0.0`
- License: CC BY-NC-SA 3.0
- Citation details: `@techreport{xty-stt, author = {Meyer,Joe}, title = {Yoloxóchitl Mixtec STT 0.1}, institution = {Coqui}, address = {\url{https://github.com/coqui-ai/STT-models}} year = {2022}, month = {April}, number = {STT-SLR89-XTY-0.1} }`
- Where to send questions or comments about the model: You can leave an issue on [`STT-model` issues](https://github.com/coqui-ai/STT-models/issues), open a new discussion on [`STT-model` discussions](https://github.com/coqui-ai/STT-models/discussions), or chat with us on [Gitter](https://gitter.im/coqui-ai/).
## Intended use
Speech-to-Text for the [Yoloxóchitl Mixtec Language](https://en.wikipedia.org/wiki/Yolox%C3%B3chitl_Mixtec) on 16kHz, mono-channel audio.
## Performance Factors
Factors relevant to Speech-to-Text performance include but are not limited to speaker demographics, recording quality, and background noise. Read more about STT performance factors [here](https://stt.readthedocs.io/en/latest/DEPLOYMENT.html#how-will-a-model-perform-on-my-data).
## Metrics
STT models are usually evaluated in terms of their transcription accuracy, deployment Real-Time Factor, and model size on disk.
#### Transcription Accuracy
The following Word Error Rates and Character Error Rates are reported for a modified data set from OpenSLR [SLR89](https://www.openslr.org/89/). The official `validated.tsv` had rows removed which had errors processing, and the data was re-processed by [Cmmon Voice Utils](https://github.com/ftyers/commonvoice-utils) to convert to 16kHz mono-channel PCM .wav files.
|Test Corpus|WER|CER|
|-----------|---|---|
|OpenSLR|48.85\%|18.04\%|
#### Real-Time Factor
Real-Time Factor (RTF) is defined as `processing-time / length-of-audio`. The exact real-time factor of an STT model will depend on the hardware setup, so you may experience a different RTF.
Recorded average RTF on laptop CPU: ``
#### Model Size
`model.pbmm`: M
`model.tflite`: M
### Approaches to uncertainty and variability
Confidence scores and multiple paths from the decoding beam can be used to measure model uncertainty and provide multiple, variable transcripts for any processed audio.
## Training data
This model was trained on a modified data set from OpenSLR [SLR89](https://www.openslr.org/89/). The official `validated.tsv` had rows removed which had errors processing, and the data was re-processed by [Cmmon Voice Utils](https://github.com/ftyers/commonvoice-utils) to convert to 16kHz mono-channel PCM .wav files.
## Evaluation data
This model was evaluated on a modified data set from OpenSLR [SLR89](https://www.openslr.org/89/). The official `validated.tsv` had rows removed which had errors processing, and the data was re-processed by [Cmmon Voice Utils](https://github.com/ftyers/commonvoice-utils) to convert to 16kHz mono-channel PCM .wav files.
## Ethical considerations
Deploying a Speech-to-Text model into any production setting has ethical implications. You should consider these implications before use.
### Demographic Bias
You should assume every machine learning model has demographic bias unless proven otherwise. For STT models, it is often the case that transcription accuracy is better for men than it is for women. If you are using this model in production, you should acknowledge this as a potential issue.
### Surveillance
Speech-to-Text may be mis-used to invade the privacy of others by recording and mining information from private conversations. This kind of individual privacy is protected by law in may countries. You should not assume consent to record and analyze private speech.
## Caveats and recommendations
Machine learning models (like this STT model) perform best on data that is similar to the data on which they were trained. Read about what to expect from an STT model with regard to your data [here](https://stt.readthedocs.io/en/latest/DEPLOYMENT.html#how-will-a-model-perform-on-my-data).
In most applications, it is recommended that you [train your own language model](https://stt.readthedocs.io/en/latest/LANGUAGE_MODEL.html) to improve transcription accuracy on your speech data.
|
moaiz237/wav2vec2-base-timit-demo-colab | moaiz237 | 2022-04-30T07:51:57Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2022-04-30T00:22:12Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-timit-demo-colab
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. -->
# wav2vec2-base-timit-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4769
- Wer: 0.4305
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 5.2022 | 13.89 | 500 | 2.9267 | 0.9995 |
| 0.834 | 27.78 | 1000 | 0.4769 | 0.4305 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
huggingtweets/itstomrobinson | huggingtweets | 2022-04-30T07:06:15Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-04-30T06:45:28Z | ---
language: en
thumbnail: http://www.huggingtweets.com/itstomrobinson/1651302371165/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/1388470365723168770/irz46Ykl_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Tom Robinson</div>
<div style="text-align: center; font-size: 14px;">@itstomrobinson</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 Tom Robinson.
| Data | Tom Robinson |
| --- | --- |
| Tweets downloaded | 733 |
| Retweets | 40 |
| Short tweets | 52 |
| Tweets kept | 641 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3bluc7sk/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 @itstomrobinson's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2ryc26oz) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2ryc26oz/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/itstomrobinson')
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)
|
dropout05/t5-realnewslike-super-tiny | dropout05 | 2022-04-30T01:35:53Z | 4 | 1 | transformers | [
"transformers",
"jax",
"t5",
"text2text-generation",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-04-14T01:34:38Z | ---
license: apache-2.0
---
**Don't use this model for any applied task. It too small to be practically useful. It is just a part of a weird research project.**
An extremely small version of T5 with these parameters
```python
"d_ff": 1024,
"d_kv": 64,
"d_model": 256,
"num_heads": 4,
"num_layers": 1, # yes, just one layer
```
The model was pre-trained on `realnewslike` subset of C4 for 1 epoch with sequence length `64`. Corresponding WandB run: [click](https://wandb.ai/guitaricet/t5-lm/runs/2yvuxsfz?workspace=user-guitaricet). |
tonydiana1/distilroberta-base-finetuned-wikitext2 | tonydiana1 | 2022-04-30T01:23:18Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2022-04-30T01:01:59Z | ---
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: 1.8347
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 2.0853 | 1.0 | 2406 | 1.9214 |
| 1.986 | 2.0 | 4812 | 1.8799 |
| 1.9568 | 3.0 | 7218 | 1.8202 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
Siddhart/t5-small-finetuned-xsum | Siddhart | 2022-04-30T00:04:50Z | 3 | 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 | 2022-04-29T23:51:32Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: t5-small-finetuned-xsum
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. -->
# t5-small-finetuned-xsum
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| No log | 1.0 | 23 | 2.7230 | 33.2094 | 14.0331 | 28.4433 | 29.4644 | 18.8947 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
stas/tiny-m2m_100 | stas | 2022-04-29T23:57:25Z | 1,370 | 0 | transformers | [
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"testing",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-04-29T23:50:29Z | ---
language:
- en
thumbnail:
tags:
- testing
license: apache-2.0
---
# Tiny M2M100 model
This is a tiny model that is used in the `transformers` test suite. It doesn't do anything useful beyond functional testing.
Do not try to use it for anything that requires quality.
The model is indeed 4MB in size.
You can see how it was created [here](https://huggingface.co/stas/tiny-m2m_100/blob/main/m2m-make-tiny-model.py)
If you're looking for the real model, please go to [https://huggingface.co/facebook/m2m100_418M](https://huggingface.co/facebook/m2m100_418M).
|
csikasote/xlsr-53-bemba-5hrs | csikasote | 2022-04-29T23:40:17Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2022-04-29T21:24:54Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: xlsr-53-bemba-5hrs
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. -->
# xlsr-53-bemba-5hrs
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3414
- Wer: 0.4867
## 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: 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_steps: 400
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.2701 | 2.16 | 400 | 0.4047 | 0.6230 |
| 0.488 | 4.32 | 800 | 0.3002 | 0.4917 |
| 0.2807 | 6.49 | 1200 | 0.3342 | 0.4802 |
| 0.1696 | 8.65 | 1600 | 0.3414 | 0.4867 |
### Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
Percival/finetuning-sentiment-model-3000-samples | Percival | 2022-04-29T22:52:18Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-04-29T22:34:49Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
model-index:
- name: finetuning-sentiment-model-3000-samples
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-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
doc2query/msmarco-vietnamese-mt5-base-v1 | doc2query | 2022-04-29T22:06:03Z | 18 | 4 | transformers | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"vi",
"dataset:unicamp-dl/mmarco",
"arxiv:1904.08375",
"arxiv:2104.08663",
"arxiv:2112.07577",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-04-29T22:05:47Z | ---
language: vi
datasets:
- unicamp-dl/mmarco
widget:
- text: "Python (phát âm tiếng Anh: /ˈpaɪθɑːn/) là một ngôn ngữ lập trình bậc cao cho các mục đích lập trình đa năng, do Guido van Rossum tạo ra và lần đầu ra mắt vào năm 1991. Python được thiết kế với ưu điểm mạnh là dễ đọc, dễ học và dễ nhớ. Python là ngôn ngữ có hình thức rất sáng sủa, cấu trúc rõ ràng, thuận tiện cho người mới học lập trình và là ngôn ngữ lập trình dễ học; được dùng rộng rãi trong phát triển trí tuệ nhân tạo. Cấu trúc của Python còn cho phép người sử dụng viết mã lệnh với số lần gõ phím tối thiểu. Vào tháng 7 năm 2018, van Rossum đã từ chức lãnh đạo trong cộng đồng ngôn ngữ Python sau 30 năm làm việc."
license: apache-2.0
---
# doc2query/msmarco-vietnamese-mt5-base-v1
This is a [doc2query](https://arxiv.org/abs/1904.08375) model based on mT5 (also known as [docT5query](https://cs.uwaterloo.ca/~jimmylin/publications/Nogueira_Lin_2019_docTTTTTquery-v2.pdf)).
It can be used for:
- **Document expansion**: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, or Lucene. The generated queries help to close the lexical gap of lexical search, as the generate queries contain synonyms. Further, it re-weights words giving important words a higher weight even if they appear seldomn in a paragraph. In our [BEIR](https://arxiv.org/abs/2104.08663) paper we showed that BM25+docT5query is a powerful search engine. In the [BEIR repository](https://github.com/beir-cellar/beir) we have an example how to use docT5query with Pyserini.
- **Domain Specific Training Data Generation**: It can be used to generate training data to learn an embedding model. In our [GPL-Paper](https://arxiv.org/abs/2112.07577) / [GPL Example on SBERT.net](https://www.sbert.net/examples/domain_adaptation/README.html#gpl-generative-pseudo-labeling) we have an example how to use the model to generate (query, text) pairs for a given collection of unlabeled texts. These pairs can then be used to train powerful dense embedding models.
## Usage
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch
model_name = 'doc2query/msmarco-vietnamese-mt5-base-v1'
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
text = "Python (phát âm tiếng Anh: /ˈpaɪθɑːn/) là một ngôn ngữ lập trình bậc cao cho các mục đích lập trình đa năng, do Guido van Rossum tạo ra và lần đầu ra mắt vào năm 1991. Python được thiết kế với ưu điểm mạnh là dễ đọc, dễ học và dễ nhớ. Python là ngôn ngữ có hình thức rất sáng sủa, cấu trúc rõ ràng, thuận tiện cho người mới học lập trình và là ngôn ngữ lập trình dễ học; được dùng rộng rãi trong phát triển trí tuệ nhân tạo. Cấu trúc của Python còn cho phép người sử dụng viết mã lệnh với số lần gõ phím tối thiểu. Vào tháng 7 năm 2018, van Rossum đã từ chức lãnh đạo trong cộng đồng ngôn ngữ Python sau 30 năm làm việc."
def create_queries(para):
input_ids = tokenizer.encode(para, return_tensors='pt')
with torch.no_grad():
# Here we use top_k / top_k random sampling. It generates more diverse queries, but of lower quality
sampling_outputs = model.generate(
input_ids=input_ids,
max_length=64,
do_sample=True,
top_p=0.95,
top_k=10,
num_return_sequences=5
)
# Here we use Beam-search. It generates better quality queries, but with less diversity
beam_outputs = model.generate(
input_ids=input_ids,
max_length=64,
num_beams=5,
no_repeat_ngram_size=2,
num_return_sequences=5,
early_stopping=True
)
print("Paragraph:")
print(para)
print("\nBeam Outputs:")
for i in range(len(beam_outputs)):
query = tokenizer.decode(beam_outputs[i], skip_special_tokens=True)
print(f'{i + 1}: {query}')
print("\nSampling Outputs:")
for i in range(len(sampling_outputs)):
query = tokenizer.decode(sampling_outputs[i], skip_special_tokens=True)
print(f'{i + 1}: {query}')
create_queries(text)
```
**Note:** `model.generate()` is non-deterministic for top_k/top_n sampling. It produces different queries each time you run it.
## Training
This model fine-tuned [google/mt5-base](https://huggingface.co/google/mt5-base) for 66k training steps (4 epochs on the 500k training pairs from MS MARCO). For the training script, see the `train_script.py` in this repository.
The input-text was truncated to 320 word pieces. Output text was generated up to 64 word pieces.
This model was trained on a (query, passage) from the [mMARCO dataset](https://github.com/unicamp-dl/mMARCO).
|
espnet/turkish_commonvoice_blstm | espnet | 2022-04-29T21:33:48Z | 0 | 0 | espnet | [
"espnet",
"audio",
"automatic-speech-recognition",
"tr",
"dataset:commonvoice",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | automatic-speech-recognition | 2022-04-29T21:32:59Z | ---
tags:
- espnet
- audio
- automatic-speech-recognition
language: tr
datasets:
- commonvoice
license: cc-by-4.0
---
## ESPnet2 ASR model
### `espnet/turkish_commonvoice_blstm`
This model was trained by dzeinali using commonvoice recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 716eb8f92e19708acfd08ba3bd39d40890d3a84b
pip install -e .
cd egs2/commonvoice/asr1
./run.sh --skip_data_prep false --skip_train true --download_model espnet/turkish_commonvoice_blstm
```
<!-- Generated by scripts/utils/show_asr_result.sh -->
# RESULTS
## Environments
- date: `Sat Apr 16 17:16:06 EDT 2022`
- python version: `3.9.5 (default, Jun 4 2021, 12:28:51) [GCC 7.5.0]`
- espnet version: `espnet 0.10.6a1`
- pytorch version: `pytorch 1.8.1+cu102`
- Git hash: `5e6e95d087af8a7a4c33c4248b75114237eae64b`
- Commit date: `Mon Apr 4 21:04:45 2022 -0400`
## asr_tr_50_epoch_lr_0.1
### WER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_rnn_asr_model_valid.acc.ave/test_tr|8339|43647|78.5|19.6|2.0|1.6|23.1|50.9|
### CER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_rnn_asr_model_valid.acc.ave/test_tr|8339|306849|94.3|3.2|2.5|1.1|6.8|50.9|
### TER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_rnn_asr_model_valid.acc.ave/test_tr|8339|203431|91.0|5.8|3.2|1.3|10.3|50.6|
## ASR config
<details><summary>expand</summary>
```
config: conf/tuning/train_asr_rnn_tr.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/asr_tr_50_epoch_lr_0.1
ngpu: 1
seed: 0
num_workers: 1
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: null
dist_rank: null
local_rank: 0
dist_master_addr: null
dist_master_port: null
dist_launcher: null
multiprocessing_distributed: false
unused_parameters: false
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
collect_stats: false
write_collected_feats: false
max_epoch: 50
patience: 3
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - train
- loss
- min
- - valid
- loss
- min
- - train
- acc
- max
- - valid
- acc
- max
keep_nbest_models:
- 10
nbest_averaging_interval: 0
grad_clip: 5.0
grad_clip_type: 2.0
grad_noise: false
accum_grad: 1
no_forward_run: false
resume: true
train_dtype: float32
use_amp: false
log_interval: null
use_matplotlib: true
use_tensorboard: true
use_wandb: false
wandb_project: null
wandb_id: null
wandb_entity: null
wandb_name: null
wandb_model_log_interval: -1
detect_anomaly: false
pretrain_path: null
init_param: []
ignore_init_mismatch: false
freeze_param: []
num_iters_per_epoch: null
batch_size: 16
valid_batch_size: null
batch_bins: 1000000
valid_batch_bins: null
train_shape_file:
- exp/asr_stats_raw_tr_bpe150_sp/train/speech_shape
- exp/asr_stats_raw_tr_bpe150_sp/train/text_shape.bpe
valid_shape_file:
- exp/asr_stats_raw_tr_bpe150_sp/valid/speech_shape
- exp/asr_stats_raw_tr_bpe150_sp/valid/text_shape.bpe
batch_type: folded
valid_batch_type: null
fold_length:
- 80000
- 150
sort_in_batch: descending
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
train_data_path_and_name_and_type:
- - dump/raw/train_tr_sp/wav.scp
- speech
- sound
- - dump/raw/train_tr_sp/text
- text
- text
valid_data_path_and_name_and_type:
- - dump/raw/dev_tr/wav.scp
- speech
- sound
- - dump/raw/dev_tr/text
- text
- text
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
optim: adadelta
optim_conf:
lr: 0.1
scheduler: null
scheduler_conf: {}
token_list:
- <blank>
- <unk>
- ▁
- R
- K
- E
- .
- I
- N
- L
- ı
- A
- M
- T
- U
- Y
- S
- Z
- ş
- ü
- O
- ▁A
- ç
- DI
- MA
- IN
- ▁BU
- LA
- ','
- H
- RA
- LAR
- ▁BIR
- DE
- ME
- ö
- '?'
- Dı
- DA
- AN
- ▁KA
- LI
- LER
- F
- LE
- EN
- P
- B
- V
- DU
- YE
- UN
- ▁G
- TE
- ▁BE
- BI
- YA
- KI
- Tı
- BA
- ▁OL
- TI
- ▁DE
- ▁HA
- ▁YA
- ıN
- AR
- IM
- Sı
- D
- Lı
- ER
- C
- ▁S
- NA
- üN
- IYOR
- ▁NE
- ▁I
- ▁O
- ▁SA
- ▁"
- ▁DA
- SI
- G
- ▁P
- TA
- ▁SE
- ▁VE
- KA
- ''''
- UM
- DEN
- ▁GE
- Dü
- ."
- ıYOR
- ▁TA
- '!'
- CE
- VA
- ▁HE
- UZ
- GI
- ıNDA
- ıNı
- ▁MI
- LAN
- ▁BAş
- ▁ON
- CA
- İ
- DAN
- SIN
- '...'
- ▁DO
- ▁GöR
- ▁KO
- ▁VAR
- ACAK
- ▁GEL
- ▁YAP
- ▁SON
- ▁ET
- ▁IKI
- Ç
- Ş
- '"'
- J
- Ö
- ':'
- â
- Ü
- ;
- '-'
- W
- X
- ’
- ”
- ‘
- î
- ë
- Q
- (
- Â
- û
- “
- )
- ğ
- <sos/eos>
init: null
input_size: null
ctc_conf:
dropout_rate: 0.0
ctc_type: builtin
reduce: true
ignore_nan_grad: true
joint_net_conf: null
model_conf:
ctc_weight: 0.5
use_preprocessor: true
token_type: bpe
bpemodel: data/tr_token_list/bpe_unigram150/bpe.model
non_linguistic_symbols: null
cleaner: null
g2p: null
speech_volume_normalize: null
rir_scp: null
rir_apply_prob: 1.0
noise_scp: null
noise_apply_prob: 1.0
noise_db_range: '13_15'
frontend: default
frontend_conf:
fs: 16k
specaug: specaug
specaug_conf:
apply_time_warp: true
time_warp_window: 5
time_warp_mode: bicubic
apply_freq_mask: true
freq_mask_width_range:
- 0
- 27
num_freq_mask: 2
apply_time_mask: true
time_mask_width_ratio_range:
- 0.0
- 0.05
num_time_mask: 2
normalize: global_mvn
normalize_conf:
stats_file: exp/asr_stats_raw_tr_bpe150_sp/train/feats_stats.npz
preencoder: null
preencoder_conf: {}
encoder: vgg_rnn
encoder_conf:
rnn_type: lstm
bidirectional: true
use_projection: true
num_layers: 4
hidden_size: 1024
output_size: 1024
postencoder: null
postencoder_conf: {}
decoder: rnn
decoder_conf:
num_layers: 2
hidden_size: 1024
sampling_probability: 0
att_conf:
atype: location
adim: 1024
aconv_chans: 10
aconv_filts: 100
required:
- output_dir
- token_list
version: 0.10.6a1
distributed: false
```
</details>
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
espnet/french_commonvoice_blstm | espnet | 2022-04-29T21:22:54Z | 0 | 0 | espnet | [
"espnet",
"audio",
"automatic-speech-recognition",
"fr",
"dataset:commonvoice",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | automatic-speech-recognition | 2022-04-29T21:22:08Z | ---
tags:
- espnet
- audio
- automatic-speech-recognition
language: fr
datasets:
- commonvoice
license: cc-by-4.0
---
## ESPnet2 ASR model
### `espnet/french_commonvoice_blstm`
This model was trained by dzeinali using commonvoice recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 716eb8f92e19708acfd08ba3bd39d40890d3a84b
pip install -e .
cd egs2/commonvoice/asr1
./run.sh --skip_data_prep false --skip_train true --download_model espnet/french_commonvoice_blstm
```
<!-- Generated by scripts/utils/show_asr_result.sh -->
# RESULTS
## Environments
- date: `Fri Apr 29 17:20:37 EDT 2022`
- python version: `3.9.5 (default, Jun 4 2021, 12:28:51) [GCC 7.5.0]`
- espnet version: `espnet 0.10.6a1`
- pytorch version: `pytorch 1.8.1+cu102`
- Git hash: `716eb8f92e19708acfd08ba3bd39d40890d3a84b`
- Commit date: `Thu Apr 28 19:50:59 2022 -0400`
## asr_train_asr_rnn_raw_fr_bpe350_sp
### WER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_rnn_asr_model_valid.acc.best/test_fr|15621|151227|75.1|22.6|2.3|2.3|27.2|81.0|
### CER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_rnn_asr_model_valid.acc.best/test_fr|15621|952803|92.9|3.6|3.5|2.0|9.1|81.0|
### TER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_rnn_asr_model_valid.acc.best/test_fr|15621|730898|89.9|6.5|3.6|1.9|12.0|81.0|
## ASR config
<details><summary>expand</summary>
```
config: conf/tuning/train_asr_rnn.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/asr_train_asr_rnn_raw_fr_bpe350_sp
ngpu: 1
seed: 0
num_workers: 1
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: null
dist_rank: null
local_rank: 0
dist_master_addr: null
dist_master_port: null
dist_launcher: null
multiprocessing_distributed: false
unused_parameters: false
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
collect_stats: false
write_collected_feats: false
max_epoch: 15
patience: 3
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - train
- loss
- min
- - valid
- loss
- min
- - train
- acc
- max
- - valid
- acc
- max
keep_nbest_models:
- 10
nbest_averaging_interval: 0
grad_clip: 5.0
grad_clip_type: 2.0
grad_noise: false
accum_grad: 1
no_forward_run: false
resume: true
train_dtype: float32
use_amp: false
log_interval: null
use_matplotlib: true
use_tensorboard: true
use_wandb: false
wandb_project: null
wandb_id: null
wandb_entity: null
wandb_name: null
wandb_model_log_interval: -1
detect_anomaly: false
pretrain_path: null
init_param: []
ignore_init_mismatch: false
freeze_param: []
num_iters_per_epoch: null
batch_size: 30
valid_batch_size: null
batch_bins: 1000000
valid_batch_bins: null
train_shape_file:
- exp/asr_stats_raw_fr_bpe350_sp/train/speech_shape
- exp/asr_stats_raw_fr_bpe350_sp/train/text_shape.bpe
valid_shape_file:
- exp/asr_stats_raw_fr_bpe350_sp/valid/speech_shape
- exp/asr_stats_raw_fr_bpe350_sp/valid/text_shape.bpe
batch_type: folded
valid_batch_type: null
fold_length:
- 80000
- 150
sort_in_batch: descending
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
train_data_path_and_name_and_type:
- - dump/raw/train_fr_sp/wav.scp
- speech
- sound
- - dump/raw/train_fr_sp/text
- text
- text
valid_data_path_and_name_and_type:
- - dump/raw/dev_fr/wav.scp
- speech
- sound
- - dump/raw/dev_fr/text
- text
- text
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
optim: adadelta
optim_conf:
lr: 0.1
scheduler: null
scheduler_conf: {}
token_list:
- <blank>
- <unk>
- S
- ▁
- E
- I
- T
- A
- U
- O
- .
- L
- R
- é
- P
- C
- V
- 'ON'
- M
- ▁DE
- ','
- N
- ▁S
- D
- IN
- ''''
- OU
- ▁D
- G
- IS
- ▁P
- ER
- ▁C
- ▁L
- ▁LA
- B
- ▁"
- ▁A
- RE
- AN
- ."
- ▁M
- ▁F
- '-'
- F
- ▁T
- ES
- ENT
- ▁LE
- EN
- IT
- LE
- ▁N
- è
- H
- ’
- Y
- X
- Z
- K
- J
- ê
- '?'
- '!'
- É
- ç
- W
- à
- ô
- â
- Q
- î
- À
- '"'
- œ
- û
- ù
- ï
- ':'
- ;
- —
- È
- «
- »
- Ç
- Ê
- ë
- á
- ü
- í
- ö
- ó
- )
- Î
- Â
- ō
- ä
- –
- Ô
- ć
- š
- '&'
- ñ
- '='
- ł
- č
- Û
- ú
- ū
- ø
- ā
- ã
- ă
- /
- ń
- _
- ș
- å
- æ
- °
- ß
- “
- ”
- ž
- ı
- Œ
- Ö
- ř
- Š
- ý
- Ō
- ‘
- ş
- ·
- o
- ę
- ÿ
- Å
- ą
- ð
- ī
- ò
- ż
- ě
- ś
- '`'
- Ë
- ì
- ē
- ğ
- İ
- '*'
- Í
- ė
- Ó
- ő
- đ
- ʻ
- Ü
- õ
- Ä
- ņ
- ṣ
- '|'
- ʾ
- π
- Ā
- σ
- '%'
- ả
- κ
- ʼ
- ň
- Ú
- ļ
- ư
- '1'
- '2'
- '}'
- ĩ
- Ҫ
- ا
- ầ
- ⁄
- ṇ
- þ
- ǎ
- ο
- ′
- s
- §
- ľ
- ǹ
- Ʉ
- ː
- ̱
- γ
- ν
- ن
- ạ
- ễ
- ộ
- ≥
- 星
- ề
- ṯ
- τ
- δ
- Δ
- Ț
- Ș
- Ū
- Ř
- ∆
- →
- ệ
- Г
- ơ
- ţ
- Þ
- Ñ
- ±
- ť
- ŏ
- €
- „
- ʿ
- Ć
- £
- α
- Ż
- Ş
- β
- ź
- Đ
- Ø
- Ś
- Ž
- Æ
- $
- Ï
- Ł
- ț
- Č
- Á
- ́
- Ù
- Μ
- ι
- ρ
- ό
- И
- з
- 京
- 北
- ď
- Ġ
- Ṭ
- −
- ☉
- '~'
- ®
- Ì
- Ò
- Õ
- ×
- ħ
- ĺ
- Ľ
- ũ
- ů
- Ų
- ǃ
- ǔ
- ̠
- ̲
- Κ
- Π
- ε
- ζ
- μ
- ς
- υ
- ψ
- І
- Ј
- А
- Е
- П
- а
- е
- м
- н
- Գ
- Զ
- ب
- د
- ر
- ل
- و
- ي
- ወ
- ደ
- ḍ
- ṅ
- ṭ
- ậ
- ắ
- ẵ
- ị
- ồ
- ờ
- ợ
- ủ
- ‐
- ―
- †
- ‹
- ›
- ₽
- ∈
- ∞
- ─
- い
- う
- た
- つ
- へ
- ま
- め
- や
- ゔ
- 扬
- 术
- 美
- 貴
- 青
- 馆
- Ꝑ
- ̐
- Ω
- ử
- ỳ
- ∨
- 乃
- 杜
- (
- Ē
- ǫ
- <sos/eos>
init: null
input_size: null
ctc_conf:
dropout_rate: 0.0
ctc_type: builtin
reduce: true
ignore_nan_grad: true
joint_net_conf: null
model_conf:
ctc_weight: 0.5
use_preprocessor: true
token_type: bpe
bpemodel: data/fr_token_list/bpe_unigram350/bpe.model
non_linguistic_symbols: null
cleaner: null
g2p: null
speech_volume_normalize: null
rir_scp: null
rir_apply_prob: 1.0
noise_scp: null
noise_apply_prob: 1.0
noise_db_range: '13_15'
frontend: default
frontend_conf:
fs: 16k
specaug: specaug
specaug_conf:
apply_time_warp: true
time_warp_window: 5
time_warp_mode: bicubic
apply_freq_mask: true
freq_mask_width_range:
- 0
- 27
num_freq_mask: 2
apply_time_mask: true
time_mask_width_ratio_range:
- 0.0
- 0.05
num_time_mask: 2
normalize: global_mvn
normalize_conf:
stats_file: exp/asr_stats_raw_fr_bpe350_sp/train/feats_stats.npz
preencoder: null
preencoder_conf: {}
encoder: vgg_rnn
encoder_conf:
rnn_type: lstm
bidirectional: true
use_projection: true
num_layers: 4
hidden_size: 1024
output_size: 1024
postencoder: null
postencoder_conf: {}
decoder: rnn
decoder_conf:
num_layers: 2
hidden_size: 1024
sampling_probability: 0
att_conf:
atype: location
adim: 1024
aconv_chans: 10
aconv_filts: 100
required:
- output_dir
- token_list
version: 0.10.6a1
distributed: false
```
</details>
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
timhbach/Team_Gryffindor_NER | timhbach | 2022-04-29T21:13:30Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2022-04-11T07:08:50Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Team_Gryffindor_NER
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. -->
# Team-Gryffindor-distilbert-base-finetuned-NER-creditcardcontract-100epoch
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the Credit card agreement dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0470
- Precision: 0.7319
- Recall: 0.7064
- F1: 0.7190
- Accuracy: 0.9920
## 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: 11
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0113 | 0.33 | 500 | 0.0443 | 0.6547 | 0.7028 | 0.6779 | 0.9908 |
| 0.0118 | 0.67 | 1000 | 0.0435 | 0.7207 | 0.6440 | 0.6802 | 0.9916 |
| 0.013 | 1.0 | 1500 | 0.0449 | 0.7113 | 0.6826 | 0.6966 | 0.9918 |
| 0.0113 | 1.34 | 2000 | 0.0434 | 0.7213 | 0.6697 | 0.6946 | 0.9915 |
| 0.0121 | 1.67 | 2500 | 0.0467 | 0.6955 | 0.6789 | 0.6871 | 0.9914 |
| 0.0125 | 2.01 | 3000 | 0.0417 | 0.7095 | 0.6991 | 0.7043 | 0.9920 |
| 0.0106 | 2.34 | 3500 | 0.0437 | 0.7191 | 0.6624 | 0.6896 | 0.9918 |
| 0.0114 | 2.68 | 4000 | 0.0468 | 0.7165 | 0.6679 | 0.6914 | 0.9920 |
| 0.0125 | 3.01 | 4500 | 0.0431 | 0.6888 | 0.6862 | 0.6875 | 0.9917 |
| 0.0107 | 3.35 | 5000 | 0.0446 | 0.7184 | 0.6459 | 0.6802 | 0.9913 |
| 0.0096 | 3.68 | 5500 | 0.0485 | 0.6926 | 0.6532 | 0.6723 | 0.9912 |
| 0.013 | 4.02 | 6000 | 0.0448 | 0.6134 | 0.6697 | 0.6404 | 0.9907 |
| 0.0102 | 4.35 | 6500 | 0.0497 | 0.6895 | 0.6642 | 0.6766 | 0.9913 |
| 0.0112 | 4.69 | 7000 | 0.0464 | 0.6759 | 0.6697 | 0.6728 | 0.9910 |
| 0.0117 | 5.02 | 7500 | 0.0484 | 0.7451 | 0.6275 | 0.6813 | 0.9916 |
| 0.0114 | 5.36 | 8000 | 0.0411 | 0.7086 | 0.6826 | 0.6953 | 0.9919 |
| 0.0108 | 5.69 | 8500 | 0.0443 | 0.7041 | 0.6679 | 0.6855 | 0.9916 |
| 0.0109 | 6.03 | 9000 | 0.0470 | 0.7228 | 0.6697 | 0.6952 | 0.9916 |
| 0.0099 | 6.36 | 9500 | 0.0471 | 0.7253 | 0.6881 | 0.7062 | 0.9913 |
| 0.0103 | 6.7 | 10000 | 0.0430 | 0.6986 | 0.7101 | 0.7043 | 0.9914 |
| 0.0117 | 7.03 | 10500 | 0.0462 | 0.7327 | 0.6991 | 0.7155 | 0.9918 |
| 0.0098 | 7.37 | 11000 | 0.0483 | 0.6910 | 0.6771 | 0.6840 | 0.9914 |
| 0.0107 | 7.7 | 11500 | 0.0468 | 0.7189 | 0.6899 | 0.7041 | 0.9916 |
| 0.0119 | 8.04 | 12000 | 0.0434 | 0.6970 | 0.6881 | 0.6925 | 0.9918 |
| 0.0112 | 8.37 | 12500 | 0.0469 | 0.7007 | 0.6917 | 0.6962 | 0.9918 |
| 0.011 | 8.71 | 13000 | 0.0469 | 0.6736 | 0.6514 | 0.6623 | 0.9914 |
| 0.0101 | 9.04 | 13500 | 0.0451 | 0.6691 | 0.6606 | 0.6648 | 0.9913 |
| 0.0099 | 9.38 | 14000 | 0.0462 | 0.7006 | 0.6826 | 0.6914 | 0.9918 |
| 0.0107 | 9.71 | 14500 | 0.0444 | 0.6840 | 0.6752 | 0.6796 | 0.9915 |
| 0.0118 | 10.05 | 15000 | 0.0457 | 0.7015 | 0.6771 | 0.6891 | 0.9918 |
| 0.0102 | 10.38 | 15500 | 0.0500 | 0.7413 | 0.6679 | 0.7027 | 0.9919 |
| 0.0107 | 10.72 | 16000 | 0.0470 | 0.7319 | 0.7064 | 0.7190 | 0.9920 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
espnet/german_commonvoice_blstm | espnet | 2022-04-29T21:11:06Z | 2 | 0 | espnet | [
"espnet",
"audio",
"automatic-speech-recognition",
"de",
"dataset:commonvoice",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | automatic-speech-recognition | 2022-04-05T01:07:06Z | ---
tags:
- espnet
- audio
- automatic-speech-recognition
language: de
datasets:
- commonvoice
license: cc-by-4.0
---
## ESPnet2 ASR model
### `espnet/german_commonvoice_blstm`
This model was trained by dzeinali using commonvoice recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 716eb8f92e19708acfd08ba3bd39d40890d3a84b
pip install -e .
cd egs2/commonvoice/asr1
./run.sh --skip_data_prep false --skip_train true --download_model espnet/german_commonvoice_blstm
```
<!-- Generated by scripts/utils/show_asr_result.sh -->
# RESULTS
## Environments
- date: `Mon Apr 4 16:41:54 EDT 2022`
- python version: `3.9.5 (default, Jun 4 2021, 12:28:51) [GCC 7.5.0]`
- espnet version: `espnet 0.10.6a1`
- pytorch version: `pytorch 1.8.1+cu102`
- Git hash: `fa1b865352475b744c37f70440de1cc6b257ba70`
- Commit date: `Wed Feb 16 16:42:36 2022 -0500`
## asr_de_blstm_specaug_num_time_mask_2_lr_0.1
### WER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_rnn_asr_model_valid.acc.best/test_de|15341|137512|80.0|18.0|2.0|2.5|22.5|69.9|
### CER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_rnn_asr_model_valid.acc.best/test_de|15341|959619|94.6|3.0|2.3|1.5|6.8|69.9|
### TER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_rnn_asr_model_valid.acc.best/test_de|15341|974965|94.7|3.0|2.3|1.5|6.7|69.9|
## ASR config
<details><summary>expand</summary>
```
config: conf/tuning/train_asr_rnn.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/asr_de_blstm_specaug_num_time_mask_2_lr_0.1
ngpu: 1
seed: 0
num_workers: 1
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: null
dist_rank: null
local_rank: 0
dist_master_addr: null
dist_master_port: null
dist_launcher: null
multiprocessing_distributed: false
unused_parameters: false
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
collect_stats: false
write_collected_feats: false
max_epoch: 15
patience: 3
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - train
- loss
- min
- - valid
- loss
- min
- - train
- acc
- max
- - valid
- acc
- max
keep_nbest_models:
- 10
nbest_averaging_interval: 0
grad_clip: 5.0
grad_clip_type: 2.0
grad_noise: false
accum_grad: 1
no_forward_run: false
resume: true
train_dtype: float32
use_amp: false
log_interval: null
use_matplotlib: true
use_tensorboard: true
use_wandb: false
wandb_project: null
wandb_id: null
wandb_entity: null
wandb_name: null
wandb_model_log_interval: -1
detect_anomaly: false
pretrain_path: null
init_param: []
ignore_init_mismatch: false
freeze_param: []
num_iters_per_epoch: null
batch_size: 30
valid_batch_size: null
batch_bins: 1000000
valid_batch_bins: null
train_shape_file:
- exp/asr_stats_raw_de_bpe204_sp/train/speech_shape
- exp/asr_stats_raw_de_bpe204_sp/train/text_shape.bpe
valid_shape_file:
- exp/asr_stats_raw_de_bpe204_sp/valid/speech_shape
- exp/asr_stats_raw_de_bpe204_sp/valid/text_shape.bpe
batch_type: folded
valid_batch_type: null
fold_length:
- 80000
- 150
sort_in_batch: descending
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
train_data_path_and_name_and_type:
- - dump/raw/train_de_sp/wav.scp
- speech
- sound
- - dump/raw/train_de_sp/text
- text
- text
valid_data_path_and_name_and_type:
- - dump/raw/dev_de/wav.scp
- speech
- sound
- - dump/raw/dev_de/text
- text
- text
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
optim: adadelta
optim_conf:
lr: 0.1
scheduler: null
scheduler_conf: {}
token_list:
- <blank>
- <unk>
- ▁
- T
- S
- E
- I
- R
- M
- A
- N
- L
- U
- D
- .
- O
- H
- B
- G
- F
- Z
- K
- P
- ü
- W
- ','
- ä
- V
- ö
- J
- '?'
- ß
- '-'
- Y
- C
- '!'
- '"'
- X
- Q
- “
- Ä
- Ö
- ''''
- ':'
- ’
- –
- é
- ;
- í
- á
- ó
- ō
- ã
- š
- »
- «
- ú
- ‘
- ł
- ş
- ă
- ř
- ʻ
- '&'
- à
- ø
- č
- ı
- É
- ý
- â
- ô
- ū
- ñ
- ā
- ë
- ž
- '@'
- /
- ʿ
- ě
- ī
- ”
- ə
- å
- ń
- ′
- æ
- ň
- ś
- ð
- ą
- ė
- Œ
- Ç
- (
- )
- ò
- đ
- î
- '='
- −
- ů
- Ú
- и
- ġ
- а
- ę
- ›
- ṣ
- '`'
- ì
- õ
- ď
- ť
- ả
- —
- ‹
- œ
- ő
- û
- ế
- ф
- р
- о
- м
- е
- в
- С
- Ḫ
- ź
- Î
- Æ
- Ż
- Ś
- ï
- Ó
- Ř
- ğ
- Ł
- İ
- Đ
- Ž
- Ş
- ț
- ê
- Á
- Ō
- ́
- Š
- Č
- ć
- ‚
- ș
- „
- +
- Ø
- μ
- ‐
- $
- '['
- ']'
- ¡
- Â
- Í
- Ô
- ù
- ē
- Ħ
- Ī
- ņ
- ŏ
- ż
- ǐ
- О
- Ш
- к
- ч
- ш
- ་
- ན
- ṟ
- ṭ
- ạ
- ắ
- ễ
- ộ
- ‟
- ≡
- ⟨
- ⟩
- カ
- 临
- 孙
- 尣
- 支
- 無
- 臣
- →
- À
- 道
- Ü
- Þ
- <sos/eos>
init: null
input_size: null
ctc_conf:
dropout_rate: 0.0
ctc_type: builtin
reduce: true
ignore_nan_grad: true
joint_net_conf: null
model_conf:
ctc_weight: 0.5
use_preprocessor: true
token_type: bpe
bpemodel: data/de_token_list/bpe_unigram204/bpe.model
non_linguistic_symbols: null
cleaner: null
g2p: null
speech_volume_normalize: null
rir_scp: null
rir_apply_prob: 1.0
noise_scp: null
noise_apply_prob: 1.0
noise_db_range: '13_15'
frontend: default
frontend_conf:
fs: 16k
specaug: specaug
specaug_conf:
apply_time_warp: true
time_warp_window: 5
time_warp_mode: bicubic
apply_freq_mask: true
freq_mask_width_range:
- 0
- 27
num_freq_mask: 2
apply_time_mask: true
time_mask_width_ratio_range:
- 0.0
- 0.05
num_time_mask: 2
normalize: global_mvn
normalize_conf:
stats_file: exp/asr_stats_raw_de_bpe204_sp/train/feats_stats.npz
preencoder: null
preencoder_conf: {}
encoder: vgg_rnn
encoder_conf:
rnn_type: lstm
bidirectional: true
use_projection: true
num_layers: 4
hidden_size: 1024
output_size: 1024
postencoder: null
postencoder_conf: {}
decoder: rnn
decoder_conf:
num_layers: 2
hidden_size: 1024
sampling_probability: 0
att_conf:
atype: location
adim: 1024
aconv_chans: 10
aconv_filts: 100
required:
- output_dir
- token_list
version: 0.10.6a1
distributed: false
```
</details>
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
umarkhalid96/t5-small-trainings | umarkhalid96 | 2022-04-29T18:36:13Z | 8 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"summarization",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | summarization | 2022-04-29T18:27:40Z | ---
license: apache-2.0
tags:
- summarization
- generated_from_trainer
metrics:
- rouge
model-index:
- name: t5-small-trainings
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. -->
# t5-small-trainings
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2580
- Rouge1: 41.5251
- Rouge2: 19.8842
- Rougel: 36.4895
- Rougelsum: 37.2565
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|
| 3.1338 | 1.0 | 51 | 2.5825 | 35.4169 | 15.379 | 30.8859 | 31.524 |
| 2.5905 | 2.0 | 102 | 2.3975 | 38.4266 | 17.2571 | 33.5912 | 34.312 |
| 2.3881 | 3.0 | 153 | 2.3329 | 39.8082 | 19.1925 | 34.8269 | 35.5295 |
| 2.3167 | 4.0 | 204 | 2.2938 | 41.3488 | 20.1513 | 35.6879 | 36.5864 |
| 2.2357 | 5.0 | 255 | 2.2727 | 41.2457 | 19.5358 | 36.0033 | 36.8405 |
| 2.232 | 6.0 | 306 | 2.2645 | 41.2746 | 20.0345 | 35.9226 | 36.7001 |
| 2.1986 | 7.0 | 357 | 2.2595 | 41.7542 | 19.9428 | 36.6819 | 37.4718 |
| 2.1457 | 8.0 | 408 | 2.2580 | 41.5251 | 19.8842 | 36.4895 | 37.2565 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
Subsets and Splits