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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
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| downloads
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| likes
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11.7k
| library_name
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Khalsuu/filipino-wav2vec2-l-xls-r-300m-test
|
Khalsuu
| 2022-04-23T08:27:45Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:filipino_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-04-22T04:36:24Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- filipino_voice
model-index:
- name: filipino-wav2vec2-l-xls-r-300m-test
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. -->
# filipino-wav2vec2-l-xls-r-300m-test
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the filipino_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7753
- Wer: 0.4831
## 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
- 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: 500
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 2.7314 | 2.09 | 400 | 0.7541 | 0.7262 |
| 0.6065 | 4.19 | 800 | 0.6738 | 0.6314 |
| 0.4063 | 6.28 | 1200 | 0.6310 | 0.5992 |
| 0.2986 | 8.38 | 1600 | 0.6301 | 0.5340 |
| 0.2263 | 10.47 | 2000 | 0.6598 | 0.5391 |
| 0.1714 | 12.57 | 2400 | 0.7778 | 0.5593 |
| 0.1303 | 14.66 | 2800 | 0.7231 | 0.4907 |
| 0.1056 | 16.75 | 3200 | 0.8031 | 0.4885 |
| 0.0851 | 18.85 | 3600 | 0.7753 | 0.4831 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3
|
zhufy/squad-en-bert-base
|
zhufy
| 2022-04-23T05:09:27Z | 10 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"question-answering",
"bert-base",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-10T10:12:56Z |
---
language: English
task: extractive question answering
datasets: SQuAD 2.0
tags:
- bert-base
---
# Model Description
This model is for English extractive question answering. It is based on the [bert-base-cased](https://huggingface.co/bert-base-uncased) model, and it is case-sensitive: it makes a difference between english and English.
# Training data
[English SQuAD v2.0](https://rajpurkar.github.io/SQuAD-explorer/)
# How to use
You can use it directly from the [🤗 Transformers](https://github.com/huggingface/transformers) library with a pipeline:
``` python
>>> from transformers.pipelines import pipeline
>>> from transformers import AutoTokenizer, AutoModelForQuestionAnswering
>>> tokenizer = AutoTokenizer.from_pretrained("zhufy/squad-en-bert-base")
>>> model = AutoModelForQuestionAnswering.from_pretrained("zhufy/squad-en-bert-base")
>>> nlp = pipeline("question-answering", model=model, tokenizer=tokenizer)
>>> context = "A problem is regarded as inherently difficult if its
solution requires significant resources, whatever the
algorithm used. The theory formalizes this intuition,
by introducing mathematical models of computation to
study these problems and quantifying the amount of
resources needed to solve them, such as time and storage.
Other complexity measures are also used, such as the
amount of communication (used in communication complexity),
the number of gates in a circuit (used in circuit
complexity) and the number of processors (used in parallel
computing). One of the roles of computational complexity
theory is to determine the practical limits on what
computers can and cannot do."
>>> question = "What are two basic primary resources used to
guage complexity?"
>>> inputs = {"question": question,
"context":context }
>>> nlp(inputs)
{'score': 0.8589141368865967,
'start': 305,
'end': 321,
'answer': 'time and storage'}
```
|
zhufy/squad-ms-bert-base
|
zhufy
| 2022-04-23T05:09:03Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"question-answering",
"bert-base",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-11T04:46:48Z |
---
language: Malay
task: extractive question answering
datasets: Malay SQuAD
tags:
- bert-base
---
# Model Description
This model is for Malay extractive question answering. It is based on the [malay-huggingface/bert-base-bahasa-cased](https://huggingface.co/malay-huggingface/bert-base-bahasa-cased/tree/main) model, and it is case-sensitive: it makes a difference between english and English.
# Training data
[Malay SQuAD v2.0](https://github.com/huseinzol05/malay-dataset/tree/master/question-answer/squad)
# How to use
You can use it directly from the [🤗 Transformers](https://github.com/huggingface/transformers) library with a pipeline:
``` python
>>> from transformers.pipelines import pipeline
>>> from transformers import AutoTokenizer, AutoModelForQuestionAnswering
>>> tokenizer = AutoTokenizer.from_pretrained("zhufy/squad-ms-bert-base")
>>> model = AutoModelForQuestionAnswering.from_pretrained("zhufy/squad-ms-bert-base")
>>> nlp = pipeline("question-answering", model=model, tokenizer=tokenizer)
>>> context = "Pada manusia, tindak balas ini diaktifkan dengan pelengkap
pengikatan kepada antibodi yang telah melekat pada mikrob ini
atau pengikatan protein pelengkap kepada karbohidrat pada permukaan
mikrob. Isyarat pengiktirafan ini mencetuskan tindak balas pembunuhan
yang pantas. Kelajuan tindak balas adalah hasil penguatan isyarat
yang berlaku berikutan pengaktifan proteolytik berturutan molekul
pelengkap, yang juga protease. Selepas protein pelengkap pada mulanya
mengikat kepada mikrob, mereka mengaktifkan aktiviti protease mereka,
yang seterusnya mengaktifkan protease pelengkap lain, dan sebagainya.
Ini menghasilkan cascade bermangkin yang menguatkan isyarat awal dengan
maklum balas positif terkawal. Kastil menghasilkan penghasilan peptida
yang menarik sel imun, meningkatkan kebolehtelapan vaskular, dan opsonize
(kot) permukaan patogen, menandakannya untuk kemusnahan. Pemendapan
pelengkap ini juga boleh membunuh sel secara terus dengan mengganggu
membran plasma mereka."
>>> question = "Protein pelengkap mengikat molekul apa yang berada di
permukaan mikrob untuk mendapatkan tindak balas imunWhat
are two basic primary resources used to guage complexity?"
>>> inputs = {"question": question,
"context":context }
>>> nlp(inputs)
{'score': 0.9848766922950745,
'start': 162,
'end': 173,
'answer': 'karbohidrat'}
```
|
agi-css/distilroberta-base-mrl-sym
|
agi-css
| 2022-04-23T04:30:29Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-23T04:28:50Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilroberta-base-mrl-sym
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-mrl-sym
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0001
- Accuracy: 1.0
- F1: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2.740146306575944e-05
- train_batch_size: 128
- eval_batch_size: 128
- 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 | 150 | 0.0001 | 1.0 | 1.0 |
| No log | 2.0 | 300 | 0.0001 | 1.0 | 1.0 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.12.0.dev20220422+cu116
- Datasets 2.1.0
- Tokenizers 0.12.1
|
agi-css/distilroberta-base-etc-sym
|
agi-css
| 2022-04-23T04:26:16Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-23T04:24:06Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilroberta-base-etc-sym
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-etc-sym
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0005
- Accuracy: 0.9997
- F1: 0.9997
## 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: 2.740146306575944e-05
- train_batch_size: 128
- eval_batch_size: 128
- 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 | 262 | 0.0068 | 0.9987 | 0.9987 |
| No log | 2.0 | 524 | 0.0005 | 0.9997 | 0.9997 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.12.0.dev20220422+cu116
- Datasets 2.1.0
- Tokenizers 0.12.1
|
agi-css/distilroberta-base-etc-nlp
|
agi-css
| 2022-04-23T04:20:09Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-23T04:18:08Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilroberta-base-etc-nlp
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-etc-nlp
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0039
- Accuracy: 0.9993
- F1: 0.9993
## 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: 2.740146306575944e-05
- train_batch_size: 128
- eval_batch_size: 128
- 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 | 262 | 0.0025 | 0.9997 | 0.9997 |
| No log | 2.0 | 524 | 0.0039 | 0.9993 | 0.9993 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.12.0.dev20220422+cu116
- Datasets 2.1.0
- Tokenizers 0.12.1
|
TibbtechUser/wav2vec2-base-urdu-demo-colab
|
TibbtechUser
| 2022-04-23T02:50:59Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-04-23T01:14:07Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-urdu-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-urdu-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) 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: 32
- 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
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3
|
huggingtweets/angelicism010-propertyexile-wretched_worm
|
huggingtweets
| 2022-04-23T01:52:59Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-04-23T01:18:51Z |
---
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/1517583783020666881/mmUj6mkI_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/1383763210314997773/aIIDR23G_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/1517290992361422848/E5jRRDlu_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">Primo & offlineism010 & wretched worm</div>
<div style="text-align: center; font-size: 14px;">@angelicism010-propertyexile-wretched_worm</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 Primo & offlineism010 & wretched worm.
| Data | Primo | offlineism010 | wretched worm |
| --- | --- | --- | --- |
| Tweets downloaded | 200 | 278 | 3234 |
| Retweets | 32 | 4 | 320 |
| Short tweets | 17 | 28 | 549 |
| Tweets kept | 151 | 246 | 2365 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3o7b93qp/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 @angelicism010-propertyexile-wretched_worm's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/30uxuf66) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/30uxuf66/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/angelicism010-propertyexile-wretched_worm')
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)
|
Tanhim/gpt2-model-de
|
Tanhim
| 2022-04-22T23:24:24Z | 21 | 3 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"de",
"license:gpl",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: de
widget:
- text: Hallo, ich bin ein Sprachmodell
license: gpl
---
<h2> GPT2 Model for German Language </h2>
Model Name: Tanhim/gpt2-model-de <br />
language: German or Deutsch <br />
thumbnail: https://huggingface.co/Tanhim/gpt2-model-de <br />
datasets: Ten Thousand German News Articles Dataset <br />
### How to use
You can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, I
set a seed for reproducibility:
```python
>>> from transformers import pipeline, set_seed
>>> generation= pipeline('text-generation', model='Tanhim/gpt2-model-de', tokenizer='Tanhim/gpt2-model-de')
>>> set_seed(42)
>>> generation("Hallo, ich bin ein Sprachmodell,", max_length=30, num_return_sequences=5)
```
Here is how to use this model to get the features of a given text in PyTorch:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("Tanhim/gpt2-model-de")
model = AutoModelWithLMHead.from_pretrained("Tanhim/gpt2-model-de")
text = "Ersetzen Sie mich durch einen beliebigen Text, den Sie wünschen."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
Citation request:
If you use the model of this repository in your research, please consider citing the following way:
```python
@misc{GermanTransformer,
author = {Tanhim Islam},
title = {{PyTorch Based Transformer Machine Learning Model for German Text Generation Task}},
howpublished = "\url{https://huggingface.co/Tanhim/gpt2-model-de}",
year = {2021},
note = "[Online; accessed 17-June-2021]"
}
```
|
huggingtweets/plsnobullywaaa
|
huggingtweets
| 2022-04-22T20:47:21Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-04-22T16:00:54Z |
---
language: en
thumbnail: http://www.huggingtweets.com/plsnobullywaaa/1650660437516/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/1511292594214551557/4T_znkpc_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">clementine</div>
<div style="text-align: center; font-size: 14px;">@plsnobullywaaa</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 clementine.
| Data | clementine |
| --- | --- |
| Tweets downloaded | 774 |
| Retweets | 32 |
| Short tweets | 258 |
| Tweets kept | 484 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/125ldexx/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 @plsnobullywaaa's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2whc68l3) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2whc68l3/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/plsnobullywaaa')
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)
|
salihkavaf/distilbert-base-uncased-finetuned-imdb
|
salihkavaf
| 2022-04-22T19:34:15Z | 3 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-04-22T11:19:02Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: salihkavaf/distilbert-base-uncased-finetuned-imdb
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. -->
# salihkavaf/distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [salihkavaf/distilbert-base-uncased-finetuned-imdb](https://huggingface.co/salihkavaf/distilbert-base-uncased-finetuned-imdb) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 2.6769
- Validation Loss: 2.5848
- 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 |
|:----------:|:---------------:|:-----:|
| 2.6769 | 2.5848 | 0 |
### Framework versions
- Transformers 4.18.0
- TensorFlow 2.8.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
huggingtweets/it_its_are_are-miyarepostbot-unbridled_id
|
huggingtweets
| 2022-04-22T19:04:30Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-04-22T19:04:23Z |
---
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/1376263696389914629/_FzhUcTW_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/1480214799539740676/S3W8I0f2_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/1400304659688878088/Lbb8zMZE_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">Sierra Armour 𝔼𝕣𝕚𝕤 & angelicism2727272628 & Miya</div>
<div style="text-align: center; font-size: 14px;">@it_its_are_are-miyarepostbot-unbridled_id</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 Sierra Armour 𝔼𝕣𝕚𝕤 & angelicism2727272628 & Miya.
| Data | Sierra Armour 𝔼𝕣𝕚𝕤 | angelicism2727272628 | Miya |
| --- | --- | --- | --- |
| Tweets downloaded | 3146 | 179 | 1840 |
| Retweets | 545 | 28 | 23 |
| Short tweets | 413 | 20 | 214 |
| Tweets kept | 2188 | 131 | 1603 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/wlae4njw/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 @it_its_are_are-miyarepostbot-unbridled_id's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2xs5iik1) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2xs5iik1/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/it_its_are_are-miyarepostbot-unbridled_id')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
huggingtweets/mimpathy
|
huggingtweets
| 2022-04-22T18:39:10Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-04-22T18:38:06Z |
---
language: en
thumbnail: http://www.huggingtweets.com/mimpathy/1650652745938/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/1269411300624363520/-xYW6d_6_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">𝓗𝓸𝓷𝓸𝓻</div>
<div style="text-align: center; font-size: 14px;">@mimpathy</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 𝓗𝓸𝓷𝓸𝓻.
| Data | 𝓗𝓸𝓷𝓸𝓻 |
| --- | --- |
| Tweets downloaded | 2299 |
| Retweets | 211 |
| Short tweets | 331 |
| Tweets kept | 1757 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/17w4ucd3/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 @mimpathy's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1qr7mqkc) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1qr7mqkc/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/mimpathy')
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)
|
cj-mills/bert-base-uncased-issues-128
|
cj-mills
| 2022-04-22T18:29:07Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-04-22T18:10:32Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bert-base-uncased-issues-128
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-issues-128
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2526
## 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: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 16
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.1071 | 1.0 | 291 | 1.6964 |
| 1.6421 | 2.0 | 582 | 1.4279 |
| 1.4853 | 3.0 | 873 | 1.3924 |
| 1.4014 | 4.0 | 1164 | 1.3701 |
| 1.3388 | 5.0 | 1455 | 1.1944 |
| 1.283 | 6.0 | 1746 | 1.2795 |
| 1.2394 | 7.0 | 2037 | 1.2671 |
| 1.2014 | 8.0 | 2328 | 1.2084 |
| 1.1668 | 9.0 | 2619 | 1.1783 |
| 1.14 | 10.0 | 2910 | 1.2076 |
| 1.1277 | 11.0 | 3201 | 1.2081 |
| 1.1053 | 12.0 | 3492 | 1.1628 |
| 1.0819 | 13.0 | 3783 | 1.2544 |
| 1.0763 | 14.0 | 4074 | 1.1695 |
| 1.0634 | 15.0 | 4365 | 1.1157 |
| 1.0637 | 16.0 | 4656 | 1.2526 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
huggingtweets/proanatwink
|
huggingtweets
| 2022-04-22T17:26:21Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-04-22T16:43:24Z |
---
language: en
thumbnail: http://www.huggingtweets.com/proanatwink/1650648376939/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/1509040026625224705/B_S4MCbD_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">God is Love (((they)))/them🪲✊🏼🇺🇦🇮🇱🏳️⚧️</div>
<div style="text-align: center; font-size: 14px;">@proanatwink</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 God is Love (((they)))/them🪲✊🏼🇺🇦🇮🇱🏳️⚧️.
| Data | God is Love (((they)))/them🪲✊🏼🇺🇦🇮🇱🏳️⚧️ |
| --- | --- |
| Tweets downloaded | 613 |
| Retweets | 120 |
| Short tweets | 142 |
| Tweets kept | 351 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/yp8eka3q/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 @proanatwink's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3lu2xkr5) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3lu2xkr5/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/proanatwink')
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)
|
caurdy/wav2vec2-large-960h-lv60-self_MIDIARIES_72H_FT
|
caurdy
| 2022-04-22T16:45:56Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"license:afl-3.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-04-21T20:03:06Z |
---
license: afl-3.0
---
FineTuned wav2vec2 large 960H lv60 self pre-trained facebook model on 72 Hours of MI Diaries Data
WER 13 % -> 9.7% on 20 min test set of MI Diaries audio clips (https://mi-diaries.org/)
### Usage ###
model = Wav2Vec2ForCTC.from_pretrained("caurdy/wav2vec2-large-960h-lv60-self_MIDIARIES_72H_FT")
processor = Wav2Vec2Processor.from_pretrained("caurdy/wav2vec2-large-960h-lv60-self_MIDIARIES_72H_FT")
|
csebuetnlp/mT5_m2o_english_crossSum
|
csebuetnlp
| 2022-04-22T15:06:41Z | 31 | 4 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"summarization",
"mT5",
"am",
"ar",
"az",
"bn",
"my",
"zh",
"en",
"fr",
"gu",
"ha",
"hi",
"ig",
"id",
"ja",
"rn",
"ko",
"ky",
"mr",
"ne",
"om",
"ps",
"fa",
"pcm",
"pt",
"pa",
"ru",
"gd",
"sr",
"si",
"so",
"es",
"sw",
"ta",
"te",
"th",
"ti",
"tr",
"uk",
"ur",
"uz",
"vi",
"cy",
"yo",
"arxiv:2112.08804",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
summarization
| 2022-03-02T23:29:05Z |
---
tags:
- summarization
- mT5
language:
- am
- ar
- az
- bn
- my
- zh
- en
- fr
- gu
- ha
- hi
- ig
- id
- ja
- rn
- ko
- ky
- mr
- ne
- om
- ps
- fa
- pcm
- pt
- pa
- ru
- gd
- sr
- si
- so
- es
- sw
- ta
- te
- th
- ti
- tr
- uk
- ur
- uz
- vi
- cy
- yo
licenses:
- cc-by-nc-sa-4.0
widget:
- text: "Videos that say approved vaccines are dangerous and cause autism, cancer or infertility are among those that will be taken down, the company said. The policy includes the termination of accounts of anti-vaccine influencers. Tech giants have been criticised for not doing more to counter false health information on their sites. In July, US President Joe Biden said social media platforms were largely responsible for people's scepticism in getting vaccinated by spreading misinformation, and appealed for them to address the issue. YouTube, which is owned by Google, said 130,000 videos were removed from its platform since last year, when it implemented a ban on content spreading misinformation about Covid vaccines. In a blog post, the company said it had seen false claims about Covid jabs \"spill over into misinformation about vaccines in general\". The new policy covers long-approved vaccines, such as those against measles or hepatitis B. \"We're expanding our medical misinformation policies on YouTube with new guidelines on currently administered vaccines that are approved and confirmed to be safe and effective by local health authorities and the WHO,\" the post said, referring to the World Health Organization."
---
# mT5-m2o-english-CrossSum
This repository contains the many-to-one (m2o) mT5 checkpoint finetuned on all cross-lingual pairs of the [CrossSum](https://huggingface.co/datasets/csebuetnlp/CrossSum) dataset, where the target summary was in **english**, i.e. this model tries to **summarize text written in any language in English.** For finetuning details and scripts, see the [paper](https://arxiv.org/abs/2112.08804) and the [official repository](https://github.com/csebuetnlp/CrossSum).
## Using this model in `transformers` (tested on 4.11.0.dev0)
```python
import re
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
WHITESPACE_HANDLER = lambda k: re.sub('\s+', ' ', re.sub('\n+', ' ', k.strip()))
article_text = """Videos that say approved vaccines are dangerous and cause autism, cancer or infertility are among those that will be taken down, the company said. The policy includes the termination of accounts of anti-vaccine influencers. Tech giants have been criticised for not doing more to counter false health information on their sites. In July, US President Joe Biden said social media platforms were largely responsible for people's scepticism in getting vaccinated by spreading misinformation, and appealed for them to address the issue. YouTube, which is owned by Google, said 130,000 videos were removed from its platform since last year, when it implemented a ban on content spreading misinformation about Covid vaccines. In a blog post, the company said it had seen false claims about Covid jabs "spill over into misinformation about vaccines in general". The new policy covers long-approved vaccines, such as those against measles or hepatitis B. "We're expanding our medical misinformation policies on YouTube with new guidelines on currently administered vaccines that are approved and confirmed to be safe and effective by local health authorities and the WHO," the post said, referring to the World Health Organization."""
model_name = "csebuetnlp/mT5_m2o_english_crossSum"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
input_ids = tokenizer(
[WHITESPACE_HANDLER(article_text)],
return_tensors="pt",
padding="max_length",
truncation=True,
max_length=512
)["input_ids"]
output_ids = model.generate(
input_ids=input_ids,
max_length=84,
no_repeat_ngram_size=2,
num_beams=4
)[0]
summary = tokenizer.decode(
output_ids,
skip_special_tokens=True,
clean_up_tokenization_spaces=False
)
print(summary)
```
## Citation
If you use this model, please cite the following paper:
```
@article{hasan2021crosssum,
author = {Tahmid Hasan and Abhik Bhattacharjee and Wasi Uddin Ahmad and Yuan-Fang Li and Yong-bin Kang and Rifat Shahriyar},
title = {CrossSum: Beyond English-Centric Cross-Lingual Abstractive Text Summarization for 1500+ Language Pairs},
journal = {CoRR},
volume = {abs/2112.08804},
year = {2021},
url = {https://arxiv.org/abs/2112.08804},
eprinttype = {arXiv},
eprint = {2112.08804}
}
```
|
csebuetnlp/mT5_m2o_hindi_crossSum
|
csebuetnlp
| 2022-04-22T15:03:33Z | 17 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"summarization",
"mT5",
"am",
"ar",
"az",
"bn",
"my",
"zh",
"en",
"fr",
"gu",
"ha",
"hi",
"ig",
"id",
"ja",
"rn",
"ko",
"ky",
"mr",
"ne",
"om",
"ps",
"fa",
"pcm",
"pt",
"pa",
"ru",
"gd",
"sr",
"si",
"so",
"es",
"sw",
"ta",
"te",
"th",
"ti",
"tr",
"uk",
"ur",
"uz",
"vi",
"cy",
"yo",
"arxiv:2112.08804",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
summarization
| 2022-04-20T15:41:03Z |
---
tags:
- summarization
- mT5
language:
- am
- ar
- az
- bn
- my
- zh
- en
- fr
- gu
- ha
- hi
- ig
- id
- ja
- rn
- ko
- ky
- mr
- ne
- om
- ps
- fa
- pcm
- pt
- pa
- ru
- gd
- sr
- si
- so
- es
- sw
- ta
- te
- th
- ti
- tr
- uk
- ur
- uz
- vi
- cy
- yo
licenses:
- cc-by-nc-sa-4.0
widget:
- text: "Videos that say approved vaccines are dangerous and cause autism, cancer or infertility are among those that will be taken down, the company said. The policy includes the termination of accounts of anti-vaccine influencers. Tech giants have been criticised for not doing more to counter false health information on their sites. In July, US President Joe Biden said social media platforms were largely responsible for people's scepticism in getting vaccinated by spreading misinformation, and appealed for them to address the issue. YouTube, which is owned by Google, said 130,000 videos were removed from its platform since last year, when it implemented a ban on content spreading misinformation about Covid vaccines. In a blog post, the company said it had seen false claims about Covid jabs \"spill over into misinformation about vaccines in general\". The new policy covers long-approved vaccines, such as those against measles or hepatitis B. \"We're expanding our medical misinformation policies on YouTube with new guidelines on currently administered vaccines that are approved and confirmed to be safe and effective by local health authorities and the WHO,\" the post said, referring to the World Health Organization."
---
# mT5-m2o-hindi-CrossSum
This repository contains the many-to-one (m2o) mT5 checkpoint finetuned on all cross-lingual pairs of the [CrossSum](https://huggingface.co/datasets/csebuetnlp/CrossSum) dataset, where the target summary was in **hindi**, i.e. this model tries to **summarize text written in any language in Hindi.** For finetuning details and scripts, see the [paper](https://arxiv.org/abs/2112.08804) and the [official repository](https://github.com/csebuetnlp/CrossSum).
## Using this model in `transformers` (tested on 4.11.0.dev0)
```python
import re
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
WHITESPACE_HANDLER = lambda k: re.sub('\s+', ' ', re.sub('\n+', ' ', k.strip()))
article_text = """Videos that say approved vaccines are dangerous and cause autism, cancer or infertility are among those that will be taken down, the company said. The policy includes the termination of accounts of anti-vaccine influencers. Tech giants have been criticised for not doing more to counter false health information on their sites. In July, US President Joe Biden said social media platforms were largely responsible for people's scepticism in getting vaccinated by spreading misinformation, and appealed for them to address the issue. YouTube, which is owned by Google, said 130,000 videos were removed from its platform since last year, when it implemented a ban on content spreading misinformation about Covid vaccines. In a blog post, the company said it had seen false claims about Covid jabs "spill over into misinformation about vaccines in general". The new policy covers long-approved vaccines, such as those against measles or hepatitis B. "We're expanding our medical misinformation policies on YouTube with new guidelines on currently administered vaccines that are approved and confirmed to be safe and effective by local health authorities and the WHO," the post said, referring to the World Health Organization."""
model_name = "csebuetnlp/mT5_m2o_hindi_crossSum"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
input_ids = tokenizer(
[WHITESPACE_HANDLER(article_text)],
return_tensors="pt",
padding="max_length",
truncation=True,
max_length=512
)["input_ids"]
output_ids = model.generate(
input_ids=input_ids,
max_length=84,
no_repeat_ngram_size=2,
num_beams=4
)[0]
summary = tokenizer.decode(
output_ids,
skip_special_tokens=True,
clean_up_tokenization_spaces=False
)
print(summary)
```
## Citation
If you use this model, please cite the following paper:
```
@article{hasan2021crosssum,
author = {Tahmid Hasan and Abhik Bhattacharjee and Wasi Uddin Ahmad and Yuan-Fang Li and Yong-bin Kang and Rifat Shahriyar},
title = {CrossSum: Beyond English-Centric Cross-Lingual Abstractive Text Summarization for 1500+ Language Pairs},
journal = {CoRR},
volume = {abs/2112.08804},
year = {2021},
url = {https://arxiv.org/abs/2112.08804},
eprinttype = {arXiv},
eprint = {2112.08804}
}
```
|
junnyu/chinese_GAU-alpha-char_L-24_H-768-paddle
|
junnyu
| 2022-04-22T12:29:07Z | 0 | 0 | null |
[
"paddlepaddle",
"gau-alpha",
"zh",
"region:us"
] | null | 2022-04-22T12:13:38Z |
---
language: zh
tags:
- gau-alpha
- paddlepaddle
inference: False
---
# pytorch 和 paddle代码
https://github.com/JunnYu/GAU-alpha-pytorch
# bert4keras代码
https://github.com/ZhuiyiTechnology/GAU-alpha
# Install
```bash
进入https://github.com/JunnYu/GAU-alpha-pytorch，
下载paddle代码gau_alpha_paddle
```
# Usage
```python
import paddle
from transformers import BertTokenizer as GAUAlphaTokenizer
from gau_alpha_paddle import GAUAlphaForMaskedLM
text = "今天[MASK]很好，我[MASK]去公园玩。"
tokenizer = GAUAlphaTokenizer.from_pretrained(
"junnyu/chinese_GAU-alpha-char_L-24_H-768"
)
pd_model = GAUAlphaForMaskedLM.from_pretrained("chinese_GAU-alpha-char_L-24_H-768")
pd_model.eval()
pd_inputs = tokenizer(text)
pd_inputs = {k: paddle.to_tensor([v]) for k, v in pd_inputs.items()}
with paddle.no_grad():
pd_outputs = pd_model(**pd_inputs)[0][0]
pd_outputs_sentence = "paddle: "
for i, id in enumerate(tokenizer.encode(text)):
if id == tokenizer.mask_token_id:
val, idx = paddle.nn.functional.softmax(pd_outputs[i], -1).topk(k=5)
tokens = tokenizer.convert_ids_to_tokens(idx)
new_tokens = []
for v, t in zip(val.cpu(), tokens):
new_tokens.append(f"{t}+{round(v.item(),4)}")
pd_outputs_sentence += "[" + "||".join(new_tokens) + "]"
else:
pd_outputs_sentence += "".join(
tokenizer.convert_ids_to_tokens([id], skip_special_tokens=True)
)
print(pd_outputs_sentence)
# paddle: 今天[天+0.8657||气+0.0535||阳+0.0165||，+0.0126||晴+0.0111]很好，我[要+0.4619||想+0.4352||又+0.0252||就+0.0157||跑+0.0064]去公园玩。
```
# Reference
Bibtex：
```tex
@techreport{gau-alpha,
title={GAU-α: GAU-based Transformers for NLP - ZhuiyiAI},
author={Jianlin Su, Shengfeng Pan, Bo Wen, Yunfeng Liu},
year={2022},
url="https://github.com/ZhuiyiTechnology/GAU-alpha",
}
```
|
maretamasaeva/thesis-freeform-yesno
|
maretamasaeva
| 2022-04-22T12:14:20Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-13T12:34:24Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: thesis-freeform-yesno
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. -->
# thesis-freeform-yesno
This model is a fine-tuned version of [maretamasaeva/thesis-freeform](https://huggingface.co/maretamasaeva/thesis-freeform) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4547
- Accuracy: 0.0194
## 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
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 2.5001 | 1.0 | 9052 | 2.4600 | 0.0194 |
| 2.4921 | 2.0 | 18104 | 2.4595 | 0.0194 |
| 2.4879 | 3.0 | 27156 | 2.4576 | 0.0194 |
| 2.4793 | 4.0 | 36208 | 2.4547 | 0.0194 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.1.0
- Tokenizers 0.12.1
|
Vishfeb27/wav2vec2-base-timit-demo-colab
|
Vishfeb27
| 2022-04-22T11:31:09Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-04-22T10:30:28Z |
---
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 [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: 32
- 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 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3
|
stevems1/bert-base-uncased-Ganesh123
|
stevems1
| 2022-04-22T07:46:54Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-04-22T07:12:32Z |
---
tags:
- generated_from_trainer
model-index:
- name: bert-base-uncased-Ganesh123
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-Ganesh123
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.1.0
- Tokenizers 0.12.1
|
cosmo/distilbert-base-uncased-finetuned-squad
|
cosmo
| 2022-04-22T07:14:22Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-04-13T10:18:06Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: distilbert-base-uncased-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.1.0
- Tokenizers 0.12.1
|
obokkkk/wav2vec2-base-960h-timit-demo-colab
|
obokkkk
| 2022-04-22T04:45:54Z | 3 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-04-22T02:59:15Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-960h-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-960h-timit-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-base-960h](https://huggingface.co/facebook/wav2vec2-base-960h) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2002
- Wer: 0.2160
## 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: 32
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 5.7805 | 4.0 | 500 | 3.0558 | 1.0 |
| 2.2936 | 8.0 | 1000 | 0.2937 | 0.3479 |
| 0.4155 | 12.0 | 1500 | 0.2108 | 0.2473 |
| 0.2439 | 16.0 | 2000 | 0.2313 | 0.2391 |
| 0.1617 | 20.0 | 2500 | 0.2003 | 0.2255 |
| 0.1443 | 24.0 | 3000 | 0.2175 | 0.2207 |
| 0.119 | 28.0 | 3500 | 0.2002 | 0.2160 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3
|
aalogan/bert-finetuned-ner
|
aalogan
| 2022-04-22T04:15:19Z | 3 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-04-21T15:39:21Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: aalogan/bert-finetuned-ner
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. -->
# aalogan/bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0170
- Validation Loss: 0.0546
- Epoch: 3
## 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': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 3508, '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, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 0.1722 | 0.0676 | 0 |
| 0.0481 | 0.0531 | 1 |
| 0.0270 | 0.0551 | 2 |
| 0.0170 | 0.0546 | 3 |
### Framework versions
- Transformers 4.18.0
- TensorFlow 2.8.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
kabelomalapane/model_zu-en_updated
|
kabelomalapane
| 2022-04-22T02:55:18Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2022-04-21T09:33:12Z |
---
license: apache-2.0
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: model_zu-en_updated
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. -->
# model_zu-en_updated
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-mul-en](https://huggingface.co/Helsinki-NLP/opus-mt-mul-en) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8306
- Bleu: 27.1218
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.2
- Datasets 1.18.3
- Tokenizers 0.11.0
|
satish860/sms_spam_detection-manning
|
satish860
| 2022-04-22T02:22:56Z | 13 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-22T02:20:36Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: sms_spam_detection-manning
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. -->
# sms_spam_detection-manning
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0512
- Accuracy: 0.9886
- F1: 0.9573
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0a0+17540c5
- Datasets 2.1.0
- Tokenizers 0.12.1
|
okho0653/distilbert-base-uncased-zero-shot-sentiment-model
|
okho0653
| 2022-04-22T01:33:28Z | 326 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-22T01:28:21Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-zero-shot-sentiment-model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-zero-shot-sentiment-model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) 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: 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 1.10.0+cu111
- Datasets 2.1.0
- Tokenizers 0.12.1
|
PrasunMishra/finetuning-sentiment-model-3000-samples
|
PrasunMishra
| 2022-04-22T01:20:52Z | 3 | 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
| 2022-04-22T00:59:22Z |
---
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
### Training results
### Framework versions
- Transformers 4.17.0
- Pytorch 1.9.1
- Datasets 2.1.0
- Tokenizers 0.11.6
|
shyamsn97/Jax-NCA
|
shyamsn97
| 2022-04-21T23:35:37Z | 0 | 0 | null |
[
"image-generation",
"region:us"
] | null | 2022-04-21T20:09:15Z |
---
tags:
- image-generation
---
# Neural Cellular Automata (Based on https://distill.pub/2020/growing-ca/) implemented in Jax (Flax)
## Installation
from source
```bash
git clone git@github.com:shyamsn97/jax-nca.git
cd jax-nca
python setup.py install
```
from PYPI
```bash
pip install jax-nca
```
## How do NCAs work?
For more information, view the awesome article https://distill.pub/2020/growing-ca/ -- Mordvintsev, et al., "Growing Neural Cellular Automata", Distill, 2020
Image below describes a single update step: https://github.com/distillpub/post--growing-ca/blob/master/public/figures/model.svg
## Why Jax?
<b> Note: This project served as a nice introduction to jax, so its performance can probably be improved </b>
NCAs are autoregressive models like RNNs, where new states are calculated from previous ones. With jax, we can make these operations a lot more performant with `jax.lax.scan` and `jax.jit` (https://jax.readthedocs.io/en/latest/_autosummary/jax.lax.scan.html)
Instead of writing the nca growth process as
```python
def multi_step(params, nca, current_state, num_steps):
# params: parameters for NCA
# nca: Flax Module describing NCA
# current_state: Current NCA state
# num_steps: number of steps to run
for i in range(num_steps):
current_state = nca.apply(params, current_state)
return current_state
```
We can write this with `jax.lax.scan`
```python
def multi_step(params, nca, current_state, num_steps):
# params: parameters for NCA
# nca: Flax Module describing NCA
# current_state: Current NCA state
# num_steps: number of steps to run
def forward(carry, inp):
carry = nca.apply({"params": params}, carry)
return carry, carry
final_state, nca_states = jax.lax.scan(forward, current_state, None, length=num_steps)
return final_state
```
The actual multi_step implementation can be found here: https://github.com/shyamsn97/jax-nca/blob/main/jax_nca/nca.py#L103
## Usage
See [notebooks/Gecko.ipynb](notebooks/Gecko.ipynb) for a full example
<b> Currently there's a bug with the stochastic update, so only `cell_fire_rate = 1.0` works at the moment </b>
Creating and using NCA
```python
class NCA(nn.Module):
num_hidden_channels: int
num_target_channels: int = 3
alpha_living_threshold: float = 0.1
cell_fire_rate: float = 1.0
trainable_perception: bool = False
alpha: float = 1.0
"""
num_hidden_channels: Number of hidden channels for each cell to use
num_target_channels: Number of target channels to be used
alpha_living_threshold: threshold to determine whether a cell lives or dies
cell_fire_rate: probability that a cell receives an update per step
trainable_perception: if true, instead of using sobel filters use a trainable conv net
alpha: scalar value to be multiplied to updates
"""
...
from jax_nca.nca import NCA
# usage
nca = NCA(
num_hidden_channels = 16,
num_target_channels = 3,
trainable_perception = False,
cell_fire_rate = 1.0,
alpha_living_threshold = 0.1
)
nca_seed = nca.create_seed(
nca.num_hidden_channels, nca.num_target_channels, shape=(64,64), batch_size=1
)
rng = jax.random.PRNGKey(0)
params = = nca.init(rng, nca_seed, rng)["params"]
update = nca.apply({"params":params}, nca_seed, jax.random.PRNGKey(10))
# multi step
final_state, nca_states = nca.multi_step(poarams, nca_seed, jax.random.PRNGKey(10), num_steps=32)
```
To train the NCA
```python
from jax_nca.dataset import ImageDataset
from jax_nca.trainer import EmojiTrainer
dataset = ImageDataset(emoji='🦎', img_size=64)
nca = NCA(
num_hidden_channels = 16,
num_target_channels = 3,
trainable_perception = False,
cell_fire_rate = 1.0,
alpha_living_threshold = 0.1
)
trainer = EmojiTrainer(dataset, nca, n_damage=0)
trainer.train(100000, batch_size=8, seed=10, lr=2e-4, min_steps=64, max_steps=96)
# to access train state:
state = trainer.state
# save
nca.save(state.params, "saved_params")
# load params
loaded_params = nca.load("saved_params")
```
|
Sarim24/xlm-roberta-base-finetuned-panx-de
|
Sarim24
| 2022-04-21T23:12:20Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-04-21T22:07:59Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.de
metrics:
- name: F1
type: f1
value: 0.862669465085938
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1374
- F1: 0.8627
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2596 | 1.0 | 525 | 0.1571 | 0.8302 |
| 0.1292 | 2.0 | 1050 | 0.1416 | 0.8455 |
| 0.0809 | 3.0 | 1575 | 0.1374 | 0.8627 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.1.0
- Tokenizers 0.12.1
|
surajnair/r3m-18
|
surajnair
| 2022-04-21T20:32:32Z | 3 | 1 |
transformers
|
[
"transformers",
"pytorch",
"r3m",
"endpoints_compatible",
"region:us"
] | null | 2022-04-21T20:10:15Z |
This model contains the pre-trained ResNet18 R3M model from the paper "R3M: A Universal Visual Representation for Robot Manipulation" (Nair et al.) The model is trained on the Ego4D dataset using time-contrastive learning, video-language alignment, and sparsity objectives. It is used for efficient downstream robotic learning.
|
jackmleitch/distilbert-base-uncased-distilled-clinc
|
jackmleitch
| 2022-04-21T20:04:39Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-21T19:48:59Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- clinc_oos
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-distilled-clinc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: clinc_oos
type: clinc_oos
args: plus
metrics:
- name: Accuracy
type: accuracy
value: 0.9432258064516129
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-distilled-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1004
- Accuracy: 0.9432
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.9044 | 1.0 | 318 | 0.5748 | 0.7390 |
| 0.4491 | 2.0 | 636 | 0.2876 | 0.88 |
| 0.2538 | 3.0 | 954 | 0.1813 | 0.9229 |
| 0.1765 | 4.0 | 1272 | 0.1388 | 0.9294 |
| 0.1422 | 5.0 | 1590 | 0.1214 | 0.9345 |
| 0.1243 | 6.0 | 1908 | 0.1114 | 0.9406 |
| 0.1138 | 7.0 | 2226 | 0.1066 | 0.94 |
| 0.1076 | 8.0 | 2544 | 0.1030 | 0.9423 |
| 0.104 | 9.0 | 2862 | 0.1010 | 0.9419 |
| 0.1019 | 10.0 | 3180 | 0.1004 | 0.9432 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0
- Datasets 2.0.0
- Tokenizers 0.11.6
|
4m1g0/wav2vec2-large-xls-r-300m-gl-jupyter9
|
4m1g0
| 2022-04-21T19:45:24Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-04-21T09:07:56Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-large-xls-r-300m-gl-jupyter9
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-large-xls-r-300m-gl-jupyter9
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0970
- Wer: 0.0624
## 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: 60
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.6977 | 3.36 | 400 | 0.4273 | 0.4574 |
| 0.2282 | 6.72 | 800 | 0.1492 | 0.1723 |
| 0.0884 | 10.08 | 1200 | 0.1344 | 0.1336 |
| 0.0594 | 13.44 | 1600 | 0.1329 | 0.1238 |
| 0.0437 | 16.8 | 2000 | 0.1137 | 0.1153 |
| 0.0384 | 20.17 | 2400 | 0.1197 | 0.1033 |
| 0.0332 | 23.53 | 2800 | 0.1147 | 0.0980 |
| 0.0282 | 26.89 | 3200 | 0.1079 | 0.0917 |
| 0.0236 | 30.25 | 3600 | 0.1144 | 0.0922 |
| 0.0237 | 33.61 | 4000 | 0.1130 | 0.0880 |
| 0.019 | 36.97 | 4400 | 0.1035 | 0.0818 |
| 0.0164 | 40.33 | 4800 | 0.1045 | 0.0813 |
| 0.0146 | 43.69 | 5200 | 0.1037 | 0.0735 |
| 0.0111 | 47.06 | 5600 | 0.1085 | 0.0701 |
| 0.0093 | 50.42 | 6000 | 0.1039 | 0.0659 |
| 0.0084 | 53.78 | 6400 | 0.0970 | 0.0636 |
| 0.0073 | 57.14 | 6800 | 0.0970 | 0.0624 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.1+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3
|
Tejas21/Totto_t5_base_BERT_Score_20k_steps
|
Tejas21
| 2022-04-21T18:47:18Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-04-20T17:35:32Z |
---
license: apache-2.0
---
language:
- en
tags:
- Table to text
- Data to text
## Dataset:
- [ToTTo](https://github.com/google-research-datasets/ToTTo)
A Controlled Table-to-Text Dataset. Totto is an open-source table-to-text dataset with over 1,20,000 examples in the English language. It defines a controlled generation task as: given a Wikipedia table and a set of highlighted cells, generate a one-sentence description.
## Base Model - T5-Base
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html)
The T5 was built by the Google team in order to create a general-purpose model that can understand the text. The basic idea behind t5 was to deal with the text processing problem as a “text-to-text” problem, i.e. taking the text as input and producing new text as output.
## Baseline Preprocessing
[Baseline Preprocessing](https://github.com/google-research/language/tree/master/language/totto)
This code repository serves as a supplementary for the main repository, which can be used to do basic preprocessing of the Totto dataset.
## Fine-tuning
On the Totto dataset, we used the T5 for the conditional generation model and fine-tuned it with 10000 steps BLEU and then 20000 steps [BERT-SCORE](https://github.com/Tiiiger/bert_score) as a metric.
|
Tejas21/Totto_t5_base_BLEURT_24k_steps
|
Tejas21
| 2022-04-21T18:43:02Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"arxiv:2004.04696",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-04-20T13:36:14Z |
---
license: apache-2.0
---
language:
- en
tags:
- Table to text
- Data to text
## Dataset:
- [ToTTo](https://github.com/google-research-datasets/ToTTo)
A Controlled Table-to-Text Dataset. Totto is an open-source table-to-text dataset with over 1,20,000 examples in the English language. It defines a controlled generation task as: given a Wikipedia table and a set of highlighted cells, generate a one-sentence description.
## Base Model - T5-Base
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html)
The T5 was built by the Google team in order to create a general-purpose model that can understand the text. The basic idea behind t5 was to deal with the text processing problem as a “text-to-text” problem, i.e. taking the text as input and producing new text as output.
## Baseline Preprocessing
[Baseline Preprocessing](https://github.com/google-research/language/tree/master/language/totto)
This code repository serves as a supplementary for the main repository, which can be used to do basic preprocessing of the Totto dataset.
## Fine-tuning
We used the T5 for the conditional generation model to fine-tune with, 24000 steps with the ToTTo dataset using [BLEURT](https://arxiv.org/abs/2004.04696) as a metric.
|
KevinForm/bert-finetuned-ner
|
KevinForm
| 2022-04-21T17:08:02Z | 3 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-04-21T17:05:16Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: KevinForm/bert-finetuned-ner
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. -->
# KevinForm/bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1831
- Validation Loss: 0.0644
- 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: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2631, '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, 'weight_decay_rate': 0.01}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 0.1831 | 0.0644 | 0 |
### Framework versions
- Transformers 4.16.2
- TensorFlow 2.6.2
- Datasets 1.18.4
- Tokenizers 0.11.6
|
satish860/finetuning-sentiment-model-3000-samples
|
satish860
| 2022-04-21T17:02:49Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-21T16:53:44Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
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 an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0454
- Accuracy: 0.9886
- F1: 0.9571
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0a0+17540c5
- Datasets 2.1.0
- Tokenizers 0.12.1
|
hyesunyun/update-summarization-led-edit-at-a-time
|
hyesunyun
| 2022-04-21T16:05:24Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"led",
"text2text-generation",
"update summarization",
"longformer",
"BART",
"PyTorch",
"Tensorboard",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-04-21T14:55:31Z |
---
language:
- en
tags:
- update summarization
- longformer
- transformers
- BART
- PyTorch
- Tensorboard
- led
metrics:
- edit distance
- ROUGE
- BertScore
---
# Update Summarization with BART Large and Longformer Encoder Decoder
## Model description
This model is a Transformer-based model that supports long document generative sequence-to-sequence.
Based on [BART Large](https://huggingface.co/transformers/model_doc/bart.html) with [Longformer Encode Decoder](https://huggingface.co/transformers/model_doc/led.html) to allow for longer inputs.
The output is one edit operation which includes action (deletion or insertion), index of where edit should happen (represented by words), and the actual text to delete or insert.
## Intended uses & limitations
#### How to use
Format your data so that each new article or evidence to add have `<EV>` token in front with each title prefixed by `<t>` and each abstract prefixed by `<abs>`. Please have the original summary also in the same format. You can have the list of articles and original summary concatenated in any order as long as they have the correct separator tokens.
```python
from transformers import LEDTokenizer, LEDForConditionalGeneration
tokenizer = LEDTokenizer.from_pretrained("hyesunyun/update-summarization-led-edit-at-a-time")
model = LEDForConditionalGeneration.from_pretrained("hyesunyun/update-summarization-led-edit-at-a-time")
input = "<EV> <t> Hypoglycemic effect of bitter melon compared with metformin in newly diagnosed type 2 diabetes patients. <abs> ETHNOPHARMACOLOGICAL RELEVANCE: Bitter melon (Momordica charantia L.) has been widely used as an traditional medicine treatment for diabetic patients in Asia. In vitro and animal studies suggested its hypoglycemic activity, but limited human studies are available to support its use. AIM OF STUDY: This study was conducted to assess the efficacy and safety of three doses of bitter melon compared with metformin. MATERIALS AND METHODS: This is a 4-week, multicenter, randomized, double-blind, active-control trial. Patients were randomized into 4 groups to receive bitter melon 500 mg/day, 1,000 mg/day, and 2,000 mg/day or metformin 1,000 mg/day. All patients were followed for 4 weeks. RESULTS: There was a significant decline in fructosamine at week 4 of the metformin group (-16.8; 95% CI, -31.2, -2.4 mumol/L) and the bitter melon 2,000 mg/day group (-10.2; 95% CI, -19.1, -1.3 mumol/L). Bitter melon 500 and 1,000 mg/day did not significantly decrease fructosamine levels (-3.5; 95% CI -11.7, 4.6 and -10.3; 95% CI -22.7, 2.2 mumol/L, respectively). CONCLUSIONS: Bitter melon had a modest hypoglycemic effect and significantly reduced fructosamine levels from baseline among patients with type 2 diabetes who received 2,000 mg/day. However, the hypoglycemic effect of bitter melon was less than metformin 1,000 mg/day. <EV> <t> Momordica charantia for type 2 diabetes mellitus. <abs> There is insufficient evidence to recommend momordica charantia for type 2 diabetes mellitus. Further studies are therefore required to address the issues of standardization and the quality control of preparations. For medical nutritional therapy, further observational trials evaluating the effects of momordica charantia are needed before RCTs are established to guide any recommendations in clinical practice."
inputs_dict = tokenizer(input, padding="max_length", max_length=10240, return_tensors="pt", truncation=True)
input_ids = inputs_dict.input_ids
attention_mask = inputs_dict.attention_mask
global_attention_mask = torch.zeros_like(attention_mask)
# put global attention on <s> token
global_attention_mask[:, 0] = 1
predicted_summary_ids = model.generate(input_ids, attention_mask=attention_mask, global_attention_mask=global_attention_mask)
print(tokenizer.batch_decode(predicted_summary_ids, skip_special_tokens=False))
```
The expected output should be something like `<s> insertion <edit_pad> zero <edit_pad> bla bla bla some text </s>`
#### Limitations and bias
Provide examples of latent issues and potential remediations.
## Training data
Used pre-trained [LED model](https://huggingface.co/transformers/model_doc/led.html) and fine-tuned using the dataset found in [this github repo](https://github.com/hyesunyun/update_summarization_data).
## Training procedure
Preprocessing, hardware used, hyperparameters...
## Eval results
### BibTeX entry and citation info
```bibtex
@inproceedings{...,
year={2022}
}
```
|
waboucay/camembert-base-finetuned-nli-repnum_wl-rua_wl
|
waboucay
| 2022-04-21T15:10:51Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"camembert",
"text-classification",
"nli",
"fr",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-19T09:39:53Z |
---
language:
- fr
tags:
- nli
metrics:
- f1
---
## Eval results
We obtain the following results on ```validation``` and ```test``` sets:
| Set | F1<sub>micro</sub> | F1<sub>macro</sub> |
|------------|--------------------|--------------------|
| validation | 73.5 | 73.5 |
| test | 75.5 | 75.5 |
|
Intel/electra-small-discriminator-mrpc
|
Intel
| 2022-04-21T14:33:49Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"electra",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-21T14:32:59Z |
---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: electra-small-discriminator-mrpc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE MRPC
type: glue
args: mrpc
metrics:
- name: Accuracy
type: accuracy
value: 0.8529411764705882
- name: F1
type: f1
value: 0.8983050847457628
---
<!-- 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. -->
# electra-small-discriminator-mrpc
This model is a fine-tuned version of [google/electra-small-discriminator](https://huggingface.co/google/electra-small-discriminator) on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3909
- Accuracy: 0.8529
- F1: 0.8983
- Combined Score: 0.8756
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
### Training results
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu102
- Datasets 2.1.0
- Tokenizers 0.11.6
|
lamyae/distilroberta-base-finetuned-wikitext2
|
lamyae
| 2022-04-21T12:48:59Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-04-21T11:40:23Z |
---
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: 3.0917
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 9 | 3.3324 |
| No log | 2.0 | 18 | 3.1066 |
| No log | 3.0 | 27 | 3.2930 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.1.0
- Tokenizers 0.12.1
|
okho0653/distilbert-base-uncased-few-shot-sentiment-model
|
okho0653
| 2022-04-21T12:28:05Z | 10 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-21T12:20:11Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-few-shot-sentiment-model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-few-shot-sentiment-model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6819
- Accuracy: 0.75
- F1: 0.8
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.1.0
- Tokenizers 0.12.1
|
patrickvonplaten/data2vec-audio-base-100h-4-gram
|
patrickvonplaten
| 2022-04-21T10:39:14Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"data2vec-audio",
"automatic-speech-recognition",
"speech",
"en",
"dataset:librispeech_asr",
"arxiv:2202.03555",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-04-20T10:28:34Z |
---
language: en
datasets:
- librispeech_asr
tags:
- speech
license: apache-2.0
---
# Data2Vec-Audio-Base-100h
[Facebook's Data2Vec](https://ai.facebook.com/research/data2vec-a-general-framework-for-self-supervised-learning-in-speech-vision-and-language/)
The base model pretrained and fine-tuned on 100 hours of Librispeech on 16kHz sampled speech audio. When using the model
make sure that your speech input is also sampled at 16Khz.
[Paper](https://arxiv.org/abs/2202.03555)
Authors: Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu, Michael Auli
**Abstract**
While the general idea of self-supervised learning is identical across modalities, the actual algorithms and objectives differ widely because they were developed with a single modality in mind. To get us closer to general self-supervised learning, we present data2vec, a framework that uses the same learning method for either speech, NLP or computer vision. The core idea is to predict latent representations of the full input data based on a masked view of the input in a self-distillation setup using a standard Transformer architecture. Instead of predicting modality-specific targets such as words, visual tokens or units of human speech which are local in nature, data2vec predicts contextualized latent representations that contain information from the entire input. Experiments on the major benchmarks of speech recognition, image classification, and natural language understanding demonstrate a new state of the art or competitive performance to predominant approaches.
The original model can be found under https://github.com/pytorch/fairseq/tree/main/examples/data2vec .
# Pre-Training method
For more information, please take a look at the [official paper](https://arxiv.org/abs/2202.03555).
# Usage
To transcribe audio files the model can be used as a standalone acoustic model as follows:
```python
from transformers import Wav2Vec2Processor, Data2VecForCTC
from datasets import load_dataset
import torch
# load model and processor
processor = Wav2Vec2Processor.from_pretrained("facebook/data2vec-audio-base-100h")
model = Data2VecForCTC.from_pretrained("facebook/data2vec-audio-base-100h")
# load dummy dataset and read soundfiles
ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
# tokenize
input_values = processor(ds[0]["audio"]["array"],, return_tensors="pt", padding="longest").input_values # Batch size 1
# retrieve logits
logits = model(input_values).logits
# take argmax and decode
predicted_ids = torch.argmax(logits, dim=-1)
transcription = processor.batch_decode(predicted_ids)
```
|
huggingtweets/route2fi
|
huggingtweets
| 2022-04-21T10:07:42Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-04-21T10:07:34Z |
---
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/1469588644088451073/VEu0DKDG_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">Route 2 FI</div>
<div style="text-align: center; font-size: 14px;">@route2fi</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 Route 2 FI.
| Data | Route 2 FI |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 1 |
| Short tweets | 264 |
| Tweets kept | 2985 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1gjkyb1x/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 @route2fi's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3q0o96ub) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3q0o96ub/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/route2fi')
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)
|
liamcripwell/ctrl44-simp
|
liamcripwell
| 2022-04-21T09:32:59Z | 232 | 4 |
transformers
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-04-21T08:38:41Z |
---
language: en
---
# CTRL44 Simplification model
This is a pretrained version of the controllable simplification model presented in the NAACL 2022 paper "Controllable Sentence Simplification via Operation Classification". It was trained on the IRSD simplification dataset.
A control token is expected at the start of input sequences to dictate which simplification operation should be performed. This can either be done manually or with an operation classifier like [this one](https://huggingface.co/liamcripwell/ctrl44-clf).
Possible control tokens are: "\<ident\>", "\<para\>", "\<ssplit\>", and "\<dsplit\>".
## How to use
Here is how to use this model in PyTorch:
```python
from transformers import BartForConditionalGeneration, AutoTokenizer
model = BartForConditionalGeneration.from_pretrained("liamcripwell/ctrl44-simp")
tokenizer = AutoTokenizer.from_pretrained("liamcripwell/ctrl44-simp")
text = "<para> Barack Hussein Obama II is an American politician who served as the 44th president of the United States from 2009 to 2017."
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, num_beams=10, max_length=128)
```
|
Intel/xlnet-base-cased-mrpc
|
Intel
| 2022-04-21T07:46:07Z | 4 | 1 |
transformers
|
[
"transformers",
"pytorch",
"xlnet",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-21T07:44:55Z |
---
language:
- en
license: mit
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: xlnet-base-cased-mrpc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE MRPC
type: glue
args: mrpc
metrics:
- name: Accuracy
type: accuracy
value: 0.8455882352941176
- name: F1
type: f1
value: 0.8896672504378283
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlnet-base-cased-mrpc
This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7156
- Accuracy: 0.8456
- F1: 0.8897
- Combined Score: 0.8676
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
### Training results
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu102
- Datasets 2.1.0
- Tokenizers 0.11.6
|
Intel/xlm-roberta-base-mrpc
|
Intel
| 2022-04-21T07:08:18Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-21T06:35:48Z |
---
language:
- en
license: mit
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: xlm-roberta-base-mrpc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE MRPC
type: glue
args: mrpc
metrics:
- name: Accuracy
type: accuracy
value: 0.8578431372549019
- name: F1
type: f1
value: 0.901023890784983
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-mrpc
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3703
- Accuracy: 0.8578
- F1: 0.9010
- Combined Score: 0.8794
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
### Training results
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu102
- Datasets 2.1.0
- Tokenizers 0.11.6
|
thanawan/bert-base-uncased-finetuned-humordetection
|
thanawan
| 2022-04-21T06:35:51Z | 28 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-20T18:57:09Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: bert-base-uncased-finetuned-humordetection
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-finetuned-humordetection
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3136
- F1: 0.9586
## 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 | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 375 | 0.1768 | 0.9507 |
| 0.2266 | 2.0 | 750 | 0.1910 | 0.9553 |
| 0.08 | 3.0 | 1125 | 0.2822 | 0.9529 |
| 0.0194 | 4.0 | 1500 | 0.2989 | 0.9560 |
| 0.0194 | 5.0 | 1875 | 0.3136 | 0.9586 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.1.0
- Tokenizers 0.12.1
|
kurianbenoy/blurr_cnnmail_textsumarrisation
|
kurianbenoy
| 2022-04-21T06:28:16Z | 0 | 0 |
fastai
|
[
"fastai",
"summarization",
"license:mit",
"region:us"
] |
summarization
| 2022-04-21T06:11:33Z |
---
license: mit
tags:
- fastai
- summarization
---
## Fine-tuned Text Summarization Model - CNNMail (blurr model)
This model is trained as shown in [the link](https://github.com/kurianbenoy/chaloRR/blob/master/TextSummarisation_Seq2Seq.ipynb).
Most of the code is developed based on [blurr tutorial on modelling with mid-level APIs](https://ohmeow.github.io/blurr/text-modeling-seq2seq-summarization.html#Mid-level-API)
|
vaariis/distilbert-base-uncased-finetuned-emotion
|
vaariis
| 2022-04-21T06:20:25Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-10T10:46:35Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
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-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2218
- Accuracy: 0.9205
- F1: 0.9208
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8262 | 1.0 | 250 | 0.3223 | 0.9005 | 0.8971 |
| 0.2474 | 2.0 | 500 | 0.2218 | 0.9205 | 0.9208 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Tokenizers 0.12.1
|
4m1g0/wav2vec2-large-xls-r-300m-gl-jupyter7
|
4m1g0
| 2022-04-21T05:54:48Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-04-20T22:28:22Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-large-xls-r-300m-gl-jupyter7
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-large-xls-r-300m-gl-jupyter7
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1004
- Wer: 0.0647
## 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: 60
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.8074 | 3.36 | 400 | 0.4882 | 0.5245 |
| 0.2396 | 6.72 | 800 | 0.1335 | 0.1524 |
| 0.0876 | 10.08 | 1200 | 0.1216 | 0.1199 |
| 0.0597 | 13.44 | 1600 | 0.1289 | 0.1241 |
| 0.0449 | 16.8 | 2000 | 0.1164 | 0.1028 |
| 0.0372 | 20.17 | 2400 | 0.1270 | 0.1023 |
| 0.0319 | 23.53 | 2800 | 0.1111 | 0.0966 |
| 0.0286 | 26.89 | 3200 | 0.1142 | 0.0925 |
| 0.0246 | 30.25 | 3600 | 0.1142 | 0.0926 |
| 0.0235 | 33.61 | 4000 | 0.1075 | 0.0836 |
| 0.0181 | 36.97 | 4400 | 0.1083 | 0.0837 |
| 0.0151 | 40.33 | 4800 | 0.1140 | 0.0768 |
| 0.014 | 43.69 | 5200 | 0.1015 | 0.0748 |
| 0.0111 | 47.06 | 5600 | 0.1023 | 0.0702 |
| 0.0093 | 50.42 | 6000 | 0.1028 | 0.0708 |
| 0.0078 | 53.78 | 6400 | 0.0999 | 0.0645 |
| 0.0071 | 57.14 | 6800 | 0.1004 | 0.0647 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.1+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3
|
achyut/patronizing_detection
|
achyut
| 2022-04-21T05:18:01Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-14T17:34:38Z |
This model is fine tuned for Patronizing and Condescending Language Classification task. Have fun.
|
eagles/focus_sum_mT5_minshi
|
eagles
| 2022-04-21T04:23:12Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-04-21T03:26:00Z |
---
tags:
- generated_from_trainer
model-index:
- name: focus_sum_mT5_minshi
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. -->
# focus_sum_mT5_minshi
This model is a fine-tuned version of [csebuetnlp/mT5_multilingual_XLSum](https://huggingface.co/csebuetnlp/mT5_multilingual_XLSum) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0930
## 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: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.268 | 83.33 | 500 | 0.0930 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.1.0
- Tokenizers 0.12.1
|
obokkkk/wav2vec2-base-timit-demo-colab3
|
obokkkk
| 2022-04-21T04:10:35Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-04-21T01:39:21Z |
---
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: 0.4832
- Wer: 0.3419
## 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: 32
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 2.292 | 4.0 | 500 | 0.7903 | 0.6305 |
| 0.5022 | 8.0 | 1000 | 0.4497 | 0.4332 |
| 0.2129 | 12.0 | 1500 | 0.4998 | 0.3940 |
| 0.1251 | 16.0 | 2000 | 0.4728 | 0.3667 |
| 0.0861 | 20.0 | 2500 | 0.4663 | 0.3644 |
| 0.0594 | 24.0 | 3000 | 0.4773 | 0.3497 |
| 0.0446 | 28.0 | 3500 | 0.4832 | 0.3419 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3
|
dlu66061/wav2vec2-base-timit-demo
|
dlu66061
| 2022-04-21T03:16:11Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-04-20T21:55:56Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-timit-demo
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo
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: 0.4094
- Wer: 0.2825
## 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: 32
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.5419 | 3.45 | 500 | 1.2376 | 0.8772 |
| 0.5393 | 6.9 | 1000 | 0.4489 | 0.3894 |
| 0.1916 | 10.34 | 1500 | 0.3777 | 0.3185 |
| 0.1139 | 13.79 | 2000 | 0.4041 | 0.3058 |
| 0.0798 | 17.24 | 2500 | 0.3742 | 0.2988 |
| 0.0602 | 20.69 | 3000 | 0.3751 | 0.2897 |
| 0.0463 | 24.14 | 3500 | 0.4067 | 0.2865 |
| 0.0388 | 27.59 | 4000 | 0.4094 | 0.2825 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu102
- Datasets 1.18.3
- Tokenizers 0.11.6
|
espejelomar/fastai-dummy-learner
|
espejelomar
| 2022-04-21T03:09:00Z | 0 | 0 |
fastai
|
[
"fastai",
"region:us"
] | null | 2022-04-21T03:08:52Z |
---
tags:
- fastai
---
# Amazing!
Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (template below and [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using the 🤗Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)).
3. Join our fastai community on the Hugging Face Discord!
Greetings fellow fastlearner 🤝!
---
# Model card
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
|
huggingtweets/torstenvolk
|
huggingtweets
| 2022-04-21T00:16:11Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-04-21T00:10:29Z |
---
language: en
thumbnail: http://www.huggingtweets.com/torstenvolk/1650500124030/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/1575782906/110930-ENMA-115240-web_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">Torsten Volk</div>
<div style="text-align: center; font-size: 14px;">@torstenvolk</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 Torsten Volk.
| Data | Torsten Volk |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 449 |
| Short tweets | 60 |
| Tweets kept | 2741 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2pgfl6jg/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 @torstenvolk's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1iccl44p) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1iccl44p/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/torstenvolk')
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)
|
hasnainnaeem/mnist_model
|
hasnainnaeem
| 2022-04-20T23:10:09Z | 0 | 0 |
tf-keras
|
[
"tf-keras",
"license:mit",
"region:us"
] | null | 2022-04-05T14:41:48Z |
---
license: mit
---
**Dataset:** MNIST
**Accuracy:** 0.986%
**Model Structure:**
|
brad1141/Bert_v5
|
brad1141
| 2022-04-20T22:23:00Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"longformer",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-04-20T19:15:19Z |
---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Bert_v5
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_v5
This model is a fine-tuned version of [allenai/longformer-base-4096](https://huggingface.co/allenai/longformer-base-4096) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9191
- Precision: 0.7612
- Recall: 0.8007
- F1: 0.5106
- Accuracy: 0.7357
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 7
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 1.0663 | 1.0 | 934 | 0.8636 | 0.6973 | 0.8467 | 0.4082 | 0.7023 |
| 0.8354 | 2.0 | 1868 | 0.8261 | 0.7367 | 0.8086 | 0.4733 | 0.7221 |
| 0.7164 | 3.0 | 2802 | 0.7737 | 0.7572 | 0.7988 | 0.5055 | 0.7347 |
| 0.6149 | 4.0 | 3736 | 0.7542 | 0.7488 | 0.8402 | 0.5176 | 0.7438 |
| 0.5153 | 5.0 | 4670 | 0.8185 | 0.7614 | 0.8123 | 0.5017 | 0.7389 |
| 0.4314 | 6.0 | 5604 | 0.8599 | 0.7543 | 0.8259 | 0.5085 | 0.7395 |
| 0.3689 | 7.0 | 6538 | 0.9191 | 0.7612 | 0.8007 | 0.5106 | 0.7357 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.1.0
- Tokenizers 0.12.1
|
brad1141/Longformer_v5
|
brad1141
| 2022-04-20T19:13:09Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"longformer",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-04-20T14:39:26Z |
---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Longformer_v5
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. -->
# Longformer_v5
This model is a fine-tuned version of [allenai/longformer-base-4096](https://huggingface.co/allenai/longformer-base-4096) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7919
- Precision: 0.8516
- Recall: 0.8678
- F1: 0.6520
- Accuracy: 0.8259
## 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: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 7
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.7744 | 1.0 | 1012 | 0.5785 | 0.8375 | 0.8501 | 0.5798 | 0.8098 |
| 0.5211 | 2.0 | 2024 | 0.5415 | 0.8434 | 0.8801 | 0.6251 | 0.8282 |
| 0.3996 | 3.0 | 3036 | 0.5565 | 0.8500 | 0.8766 | 0.6303 | 0.8274 |
| 0.2964 | 4.0 | 4048 | 0.6017 | 0.8617 | 0.8546 | 0.6415 | 0.8240 |
| 0.2187 | 5.0 | 5060 | 0.6660 | 0.8485 | 0.8718 | 0.6431 | 0.8271 |
| 0.1603 | 6.0 | 6072 | 0.7235 | 0.8493 | 0.8759 | 0.6544 | 0.8290 |
| 0.1208 | 7.0 | 7084 | 0.7919 | 0.8516 | 0.8678 | 0.6520 | 0.8259 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.1.0
- Tokenizers 0.12.1
|
eslamxm/mT5_multilingual_XLSum-finetuned-ar-wikilingua
|
eslamxm
| 2022-04-20T18:31:30Z | 10 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"summarization",
"generated_from_trainer",
"dataset:wiki_lingua",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
summarization
| 2022-04-20T06:33:58Z |
---
tags:
- summarization
- generated_from_trainer
datasets:
- wiki_lingua
model-index:
- name: mT5_multilingual_XLSum-finetuned-ar-wikilingua
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mT5_multilingual_XLSum-finetuned-ar-wikilingua
This model is a fine-tuned version of [csebuetnlp/mT5_multilingual_XLSum](https://huggingface.co/csebuetnlp/mT5_multilingual_XLSum) on the wiki_lingua dataset.
It achieves the following results on the evaluation set:
- Loss: 3.6903
- Rouge-1: 24.47
- Rouge-2: 7.69
- Rouge-l: 20.04
- Gen Len: 39.64
- Bertscore: 72.63
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 250
- num_epochs: 8
- label_smoothing_factor: 0.1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge-1 | Rouge-2 | Rouge-l | Gen Len | Bertscore |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:-------:|:---------:|
| 4.4406 | 1.0 | 5111 | 3.9582 | 22.35 | 6.84 | 18.39 | 34.78 | 71.94 |
| 4.0158 | 2.0 | 10222 | 3.8316 | 22.87 | 7.24 | 18.92 | 34.7 | 71.99 |
| 3.8626 | 3.0 | 15333 | 3.7695 | 23.65 | 7.5 | 19.6 | 35.53 | 72.31 |
| 3.7626 | 4.0 | 20444 | 3.7313 | 24.01 | 7.59 | 19.68 | 38.16 | 72.41 |
| 3.6934 | 5.0 | 25555 | 3.7118 | 24.37 | 7.77 | 19.93 | 39.36 | 72.47 |
| 3.6421 | 6.0 | 30666 | 3.7016 | 24.48 | 7.8 | 20.07 | 38.58 | 72.58 |
| 3.6073 | 7.0 | 35777 | 3.6907 | 24.31 | 7.83 | 20.13 | 38.07 | 72.5 |
| 3.5843 | 8.0 | 40888 | 3.6903 | 24.55 | 7.88 | 20.2 | 38.33 | 72.6 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.1.0
- Tokenizers 0.12.1
|
OWG/convbert-base-spanish
|
OWG
| 2022-04-20T18:06:55Z | 0 | 0 | null |
[
"onnx",
"ConvBERT",
"es",
"dataset:large_spanish_corpus",
"arxiv:2008.02496",
"license:mit",
"region:us"
] | null | 2022-04-20T17:52:31Z |
---
language:
- es
tags:
- ConvBERT
license: mit
datasets:
- large_spanish_corpus
---
# ConvBERT
## Model Description
ConvBERT base pre-trained on large_spanish_corpus. The ConvBERT architecture has been presented in the ["ConvBERT: Improving BERT with Span-based Dynamic Convolution"](https://arxiv.org/abs/2008.02496) paper.
## Original implementation
Follow [this link](https://huggingface.co/mrm8488/convbert-base-spanish) to see the original implementation.
# How to use
Download the model by cloning the repository via:
```bash
git clone https://huggingface.co/OWG/convbert-base-spanish
```
Then you can use the model.
Because it's a `base` model you need to fine-tune it on your specific task, depending on your needs, before using it.
|
sap218/PatientINF
|
sap218
| 2022-04-20T17:28:39Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2022-04-20T17:20:08Z |
---
license: mit
---
PatientINF embedding model, derived from ClinicalBERT and retrained with patient forum conversation. See the GitHub for documentation: https://github.com/sap218/PatientINF
model is intended for my PhD thesis and open source for secondary research, thesis will be available soon.
|
EColi/SB_Classifier
|
EColi
| 2022-04-20T17:27:13Z | 63 | 1 |
generic
|
[
"generic",
"pytorch",
"bert",
"text-classification",
"region:us"
] |
text-classification
| 2022-04-20T01:19:56Z |
---
tags:
- text-classification
- generic
library_name: generic
widget:
- text: 'This video is sponsored by squarespace'
example_title: Sponsor
- text: 'Check out the merch at linustechtips.com'
example_title: Unpaid/self promotion
- text: "Don't forget to like, comment and subscribe"
example_title: Interaction reminder
- text: 'pqh4LfPeCYs,824.695,826.267,826.133,829.876,835.933,927.581'
example_title: Extract text from video
---
|
ahmednasser/DistilBert-FakeNews
|
ahmednasser
| 2022-04-20T16:29:21Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"fake-news",
"en",
"arxiv:1910.01108",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-07T17:40:27Z |
---
language:
- en
tags:
- text-classification
- fake-news
- pytorch
datasets:
- Fake News https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset
metrics:
- Accuracy, AUC
---
## Model description:
[Distilbert](https://arxiv.org/abs/1910.01108) is created with knowledge distillation during the pre-training phase which reduces the size of a BERT model by 40%, while retaining 97% of its language understanding. It's smaller, faster than Bert and any other Bert-based model.
[Distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) finetuned on the fake news dataset with below Hyperparameters
```
learning rate 5e-5,
batch size 32,
num_train_epochs=2,
```
Full code available @ [DistilBert-FakeNews](https://github.com/anasserhussien/DistilBert-FakeNews)
Dataset available @ [Fake News dataset](https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset)
|
espnet/GunnarThor_talromur_h_fastspeech2
|
espnet
| 2022-04-20T15:37:27Z | 1 | 0 |
espnet
|
[
"espnet",
"audio",
"text-to-speech",
"en",
"dataset:talromur",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
text-to-speech
| 2022-04-20T15:37:06Z |
---
tags:
- espnet
- audio
- text-to-speech
language: en
datasets:
- talromur
license: cc-by-4.0
---
## ESPnet2 TTS model
### `espnet/GunnarThor_talromur_h_fastspeech2`
This model was trained by Gunnar Thor using talromur recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 49a284e69308d81c142b89795de255b4ce290c54
pip install -e .
cd egs2/talromur/tts1
./run.sh --skip_data_prep false --skip_train true --download_model espnet/GunnarThor_talromur_h_fastspeech2
```
## TTS config
<details><summary>expand</summary>
```
config: conf/tuning/train_fastspeech2.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/h/tts_train_fastspeech2_raw_phn_none
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: 100
patience: null
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - valid
- loss
- min
- - train
- loss
- min
keep_nbest_models: 5
nbest_averaging_interval: 0
grad_clip: 1.0
grad_clip_type: 2.0
grad_noise: false
accum_grad: 8
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: 800
batch_size: 20
valid_batch_size: null
batch_bins: 2500000
valid_batch_bins: null
train_shape_file:
- exp/h/tts_train_tacotron2_raw_phn_none/decode_use_teacher_forcingtrue_train.loss.ave/stats/train/text_shape.phn
- exp/h/tts_train_tacotron2_raw_phn_none/decode_use_teacher_forcingtrue_train.loss.ave/stats/train/speech_shape
valid_shape_file:
- exp/h/tts_train_tacotron2_raw_phn_none/decode_use_teacher_forcingtrue_train.loss.ave/stats/valid/text_shape.phn
- exp/h/tts_train_tacotron2_raw_phn_none/decode_use_teacher_forcingtrue_train.loss.ave/stats/valid/speech_shape
batch_type: numel
valid_batch_type: null
fold_length:
- 150
- 204800
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_h_phn/text
- text
- text
- - exp/h/tts_train_tacotron2_raw_phn_none/decode_use_teacher_forcingtrue_train.loss.ave/train_h_phn/durations
- durations
- text_int
- - dump/raw/train_h_phn/wav.scp
- speech
- sound
valid_data_path_and_name_and_type:
- - dump/raw/dev_h_phn/text
- text
- text
- - exp/h/tts_train_tacotron2_raw_phn_none/decode_use_teacher_forcingtrue_train.loss.ave/dev_h_phn/durations
- durations
- text_int
- - dump/raw/dev_h_phn/wav.scp
- speech
- sound
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
optim: adam
optim_conf:
lr: 1.0
scheduler: noamlr
scheduler_conf:
model_size: 384
warmup_steps: 4000
token_list:
- <blank>
- <unk>
- ','
- .
- r
- t
- n
- a0
- s
- I0
- D
- l
- Y0
- m
- v
- h
- E1
- k
- a:1
- E:1
- f
- G
- j
- T
- a1
- p
- c
- au:1
- i:1
- O:1
- I:1
- E0
- I1
- r_0
- t_h
- k_h
- Y1
- ei1
- i0
- ou:1
- ei:1
- u:1
- O1
- N
- l_0
- '91'
- ai0
- au1
- ou0
- n_0
- ei0
- O0
- ou1
- ai:1
- '9:1'
- ai1
- i1
- '90'
- au0
- c_h
- x
- 9i:1
- C
- p_h
- u0
- Y:1
- J
- 9i1
- u1
- 9i0
- N_0
- m_0
- J_0
- Oi1
- Yi0
- Yi1
- Oi0
- au:0
- '9:0'
- E:0
- <sos/eos>
odim: null
model_conf: {}
use_preprocessor: true
token_type: phn
bpemodel: null
non_linguistic_symbols: null
cleaner: null
g2p: null
feats_extract: fbank
feats_extract_conf:
n_fft: 1024
hop_length: 256
win_length: null
fs: 22050
fmin: 80
fmax: 7600
n_mels: 80
normalize: global_mvn
normalize_conf:
stats_file: exp/h/tts_train_tacotron2_raw_phn_none/decode_use_teacher_forcingtrue_train.loss.ave/stats/train/feats_stats.npz
tts: fastspeech2
tts_conf:
adim: 384
aheads: 2
elayers: 4
eunits: 1536
dlayers: 4
dunits: 1536
positionwise_layer_type: conv1d
positionwise_conv_kernel_size: 3
duration_predictor_layers: 2
duration_predictor_chans: 256
duration_predictor_kernel_size: 3
postnet_layers: 5
postnet_filts: 5
postnet_chans: 256
use_masking: true
use_scaled_pos_enc: true
encoder_normalize_before: true
decoder_normalize_before: true
reduction_factor: 1
init_type: xavier_uniform
init_enc_alpha: 1.0
init_dec_alpha: 1.0
transformer_enc_dropout_rate: 0.2
transformer_enc_positional_dropout_rate: 0.2
transformer_enc_attn_dropout_rate: 0.2
transformer_dec_dropout_rate: 0.2
transformer_dec_positional_dropout_rate: 0.2
transformer_dec_attn_dropout_rate: 0.2
pitch_predictor_layers: 5
pitch_predictor_chans: 256
pitch_predictor_kernel_size: 5
pitch_predictor_dropout: 0.5
pitch_embed_kernel_size: 1
pitch_embed_dropout: 0.0
stop_gradient_from_pitch_predictor: true
energy_predictor_layers: 2
energy_predictor_chans: 256
energy_predictor_kernel_size: 3
energy_predictor_dropout: 0.5
energy_embed_kernel_size: 1
energy_embed_dropout: 0.0
stop_gradient_from_energy_predictor: false
pitch_extract: dio
pitch_extract_conf:
fs: 22050
n_fft: 1024
hop_length: 256
f0max: 400
f0min: 80
reduction_factor: 1
pitch_normalize: global_mvn
pitch_normalize_conf:
stats_file: exp/h/tts_train_tacotron2_raw_phn_none/decode_use_teacher_forcingtrue_train.loss.ave/stats/train/pitch_stats.npz
energy_extract: energy
energy_extract_conf:
fs: 22050
n_fft: 1024
hop_length: 256
win_length: null
reduction_factor: 1
energy_normalize: global_mvn
energy_normalize_conf:
stats_file: exp/h/tts_train_tacotron2_raw_phn_none/decode_use_teacher_forcingtrue_train.loss.ave/stats/train/energy_stats.npz
required:
- output_dir
- token_list
version: 0.10.7a1
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}
}
@inproceedings{hayashi2020espnet,
title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7654--7658},
year={2020},
organization={IEEE}
}
```
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/GunnarThor_talromur_g_tacotron2
|
espnet
| 2022-04-20T15:36:20Z | 1 | 0 |
espnet
|
[
"espnet",
"audio",
"text-to-speech",
"en",
"dataset:talromur",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
text-to-speech
| 2022-04-20T15:35:25Z |
---
tags:
- espnet
- audio
- text-to-speech
language: en
datasets:
- talromur
license: cc-by-4.0
---
## ESPnet2 TTS model
### `espnet/GunnarThor_talromur_g_tacotron2`
This model was trained by Gunnar Thor using talromur recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 49a284e69308d81c142b89795de255b4ce290c54
pip install -e .
cd egs2/talromur/tts1
./run.sh --skip_data_prep false --skip_train true --download_model espnet/GunnarThor_talromur_g_tacotron2
```
## TTS config
<details><summary>expand</summary>
```
config: ./conf/tuning/train_tacotron2.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/g/tts_train_tacotron2_raw_phn_none
ngpu: 1
seed: 0
num_workers: 1
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: 2
dist_rank: 0
local_rank: 0
dist_master_addr: localhost
dist_master_port: 39151
dist_launcher: null
multiprocessing_distributed: true
unused_parameters: false
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
collect_stats: false
write_collected_feats: false
max_epoch: 100
patience: null
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - valid
- loss
- min
- - train
- loss
- min
keep_nbest_models: 5
nbest_averaging_interval: 0
grad_clip: 1.0
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: 500
batch_size: 20
valid_batch_size: null
batch_bins: 2560000
valid_batch_bins: null
train_shape_file:
- exp/g/tts_stats_raw_phn_none/train/text_shape.phn
- exp/g/tts_stats_raw_phn_none/train/speech_shape
valid_shape_file:
- exp/g/tts_stats_raw_phn_none/valid/text_shape.phn
- exp/g/tts_stats_raw_phn_none/valid/speech_shape
batch_type: numel
valid_batch_type: null
fold_length:
- 150
- 204800
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_g_phn/text
- text
- text
- - dump/raw/train_g_phn/wav.scp
- speech
- sound
valid_data_path_and_name_and_type:
- - dump/raw/dev_g_phn/text
- text
- text
- - dump/raw/dev_g_phn/wav.scp
- speech
- sound
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
optim: adam
optim_conf:
lr: 0.001
eps: 1.0e-06
weight_decay: 0.0
scheduler: null
scheduler_conf: {}
token_list:
- <blank>
- <unk>
- ','
- .
- r
- t
- n
- a0
- s
- I0
- D
- l
- Y0
- m
- v
- h
- E1
- k
- a:1
- E:1
- f
- G
- j
- T
- a1
- p
- c
- au:1
- i:1
- O:1
- I:1
- E0
- I1
- r_0
- t_h
- k_h
- Y1
- ei1
- i0
- ou:1
- ei:1
- u:1
- O1
- N
- l_0
- '91'
- ai0
- au1
- ou0
- n_0
- ei0
- O0
- ou1
- ai:1
- '9:1'
- ai1
- i1
- '90'
- au0
- c_h
- x
- 9i:1
- C
- p_h
- u0
- Y:1
- J
- 9i1
- u1
- 9i0
- N_0
- m_0
- J_0
- Oi1
- Yi0
- Yi1
- Oi0
- au:0
- '9:0'
- E:0
- <sos/eos>
odim: null
model_conf: {}
use_preprocessor: true
token_type: phn
bpemodel: null
non_linguistic_symbols: null
cleaner: null
g2p: null
feats_extract: fbank
feats_extract_conf:
n_fft: 1024
hop_length: 256
win_length: null
fs: 22050
fmin: 80
fmax: 7600
n_mels: 80
normalize: global_mvn
normalize_conf:
stats_file: exp/g/tts_stats_raw_phn_none/train/feats_stats.npz
tts: tacotron2
tts_conf:
embed_dim: 512
elayers: 1
eunits: 512
econv_layers: 3
econv_chans: 512
econv_filts: 5
atype: location
adim: 512
aconv_chans: 32
aconv_filts: 15
cumulate_att_w: true
dlayers: 2
dunits: 1024
prenet_layers: 2
prenet_units: 256
postnet_layers: 5
postnet_chans: 512
postnet_filts: 5
output_activation: null
use_batch_norm: true
use_concate: true
use_residual: false
dropout_rate: 0.5
zoneout_rate: 0.1
reduction_factor: 1
spk_embed_dim: null
use_masking: true
bce_pos_weight: 5.0
use_guided_attn_loss: true
guided_attn_loss_sigma: 0.4
guided_attn_loss_lambda: 1.0
pitch_extract: null
pitch_extract_conf: {}
pitch_normalize: null
pitch_normalize_conf: {}
energy_extract: null
energy_extract_conf: {}
energy_normalize: null
energy_normalize_conf: {}
required:
- output_dir
- token_list
version: 0.10.7a1
distributed: true
```
</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}
}
@inproceedings{hayashi2020espnet,
title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7654--7658},
year={2020},
organization={IEEE}
}
```
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/GunnarThor_talromur_g_fastspeech2
|
espnet
| 2022-04-20T15:35:50Z | 0 | 0 |
espnet
|
[
"espnet",
"audio",
"text-to-speech",
"en",
"dataset:talromur",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
text-to-speech
| 2022-04-20T15:35:34Z |
---
tags:
- espnet
- audio
- text-to-speech
language: en
datasets:
- talromur
license: cc-by-4.0
---
## ESPnet2 TTS model
### `espnet/GunnarThor_talromur_g_fastspeech2`
This model was trained by Gunnar Thor using talromur recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 49a284e69308d81c142b89795de255b4ce290c54
pip install -e .
cd egs2/talromur/tts1
./run.sh --skip_data_prep false --skip_train true --download_model espnet/GunnarThor_talromur_g_fastspeech2
```
## TTS config
<details><summary>expand</summary>
```
config: conf/tuning/train_fastspeech2.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/g/tts_train_fastspeech2_raw_phn_none
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: 100
patience: null
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - valid
- loss
- min
- - train
- loss
- min
keep_nbest_models: 5
nbest_averaging_interval: 0
grad_clip: 1.0
grad_clip_type: 2.0
grad_noise: false
accum_grad: 8
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: 800
batch_size: 20
valid_batch_size: null
batch_bins: 2500000
valid_batch_bins: null
train_shape_file:
- exp/g/tts_train_tacotron2_raw_phn_none/decode_use_teacher_forcingtrue_train.loss.ave/stats/train/text_shape.phn
- exp/g/tts_train_tacotron2_raw_phn_none/decode_use_teacher_forcingtrue_train.loss.ave/stats/train/speech_shape
valid_shape_file:
- exp/g/tts_train_tacotron2_raw_phn_none/decode_use_teacher_forcingtrue_train.loss.ave/stats/valid/text_shape.phn
- exp/g/tts_train_tacotron2_raw_phn_none/decode_use_teacher_forcingtrue_train.loss.ave/stats/valid/speech_shape
batch_type: numel
valid_batch_type: null
fold_length:
- 150
- 204800
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_g_phn/text
- text
- text
- - exp/g/tts_train_tacotron2_raw_phn_none/decode_use_teacher_forcingtrue_train.loss.ave/train_g_phn/durations
- durations
- text_int
- - dump/raw/train_g_phn/wav.scp
- speech
- sound
valid_data_path_and_name_and_type:
- - dump/raw/dev_g_phn/text
- text
- text
- - exp/g/tts_train_tacotron2_raw_phn_none/decode_use_teacher_forcingtrue_train.loss.ave/dev_g_phn/durations
- durations
- text_int
- - dump/raw/dev_g_phn/wav.scp
- speech
- sound
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
optim: adam
optim_conf:
lr: 1.0
scheduler: noamlr
scheduler_conf:
model_size: 384
warmup_steps: 4000
token_list:
- <blank>
- <unk>
- ','
- .
- r
- t
- n
- a0
- s
- I0
- D
- l
- Y0
- m
- v
- h
- E1
- k
- a:1
- E:1
- f
- G
- j
- T
- a1
- p
- c
- au:1
- i:1
- O:1
- I:1
- E0
- I1
- r_0
- t_h
- k_h
- Y1
- ei1
- i0
- ou:1
- ei:1
- u:1
- O1
- N
- l_0
- '91'
- ai0
- au1
- ou0
- n_0
- ei0
- O0
- ou1
- ai:1
- '9:1'
- ai1
- i1
- '90'
- au0
- c_h
- x
- 9i:1
- C
- p_h
- u0
- Y:1
- J
- 9i1
- u1
- 9i0
- N_0
- m_0
- J_0
- Oi1
- Yi0
- Yi1
- Oi0
- au:0
- '9:0'
- E:0
- <sos/eos>
odim: null
model_conf: {}
use_preprocessor: true
token_type: phn
bpemodel: null
non_linguistic_symbols: null
cleaner: null
g2p: null
feats_extract: fbank
feats_extract_conf:
n_fft: 1024
hop_length: 256
win_length: null
fs: 22050
fmin: 80
fmax: 7600
n_mels: 80
normalize: global_mvn
normalize_conf:
stats_file: exp/g/tts_train_tacotron2_raw_phn_none/decode_use_teacher_forcingtrue_train.loss.ave/stats/train/feats_stats.npz
tts: fastspeech2
tts_conf:
adim: 384
aheads: 2
elayers: 4
eunits: 1536
dlayers: 4
dunits: 1536
positionwise_layer_type: conv1d
positionwise_conv_kernel_size: 3
duration_predictor_layers: 2
duration_predictor_chans: 256
duration_predictor_kernel_size: 3
postnet_layers: 5
postnet_filts: 5
postnet_chans: 256
use_masking: true
use_scaled_pos_enc: true
encoder_normalize_before: true
decoder_normalize_before: true
reduction_factor: 1
init_type: xavier_uniform
init_enc_alpha: 1.0
init_dec_alpha: 1.0
transformer_enc_dropout_rate: 0.2
transformer_enc_positional_dropout_rate: 0.2
transformer_enc_attn_dropout_rate: 0.2
transformer_dec_dropout_rate: 0.2
transformer_dec_positional_dropout_rate: 0.2
transformer_dec_attn_dropout_rate: 0.2
pitch_predictor_layers: 5
pitch_predictor_chans: 256
pitch_predictor_kernel_size: 5
pitch_predictor_dropout: 0.5
pitch_embed_kernel_size: 1
pitch_embed_dropout: 0.0
stop_gradient_from_pitch_predictor: true
energy_predictor_layers: 2
energy_predictor_chans: 256
energy_predictor_kernel_size: 3
energy_predictor_dropout: 0.5
energy_embed_kernel_size: 1
energy_embed_dropout: 0.0
stop_gradient_from_energy_predictor: false
pitch_extract: dio
pitch_extract_conf:
fs: 22050
n_fft: 1024
hop_length: 256
f0max: 400
f0min: 80
reduction_factor: 1
pitch_normalize: global_mvn
pitch_normalize_conf:
stats_file: exp/g/tts_train_tacotron2_raw_phn_none/decode_use_teacher_forcingtrue_train.loss.ave/stats/train/pitch_stats.npz
energy_extract: energy
energy_extract_conf:
fs: 22050
n_fft: 1024
hop_length: 256
win_length: null
reduction_factor: 1
energy_normalize: global_mvn
energy_normalize_conf:
stats_file: exp/g/tts_train_tacotron2_raw_phn_none/decode_use_teacher_forcingtrue_train.loss.ave/stats/train/energy_stats.npz
required:
- output_dir
- token_list
version: 0.10.7a1
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}
}
@inproceedings{hayashi2020espnet,
title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7654--7658},
year={2020},
organization={IEEE}
}
```
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}
}
```
|
celinelee/bart-finetuned-conala-3
|
celinelee
| 2022-04-20T15:10:58Z | 4 | 1 |
transformers
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-04-20T02:00:22Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
- bleu
model-index:
- name: bart-finetuned-conala-3
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. -->
# bart-finetuned-conala-3
This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on an CoNaLa.
It achieves the following results on the evaluation set:
- Loss: 1.8253
- Rouge1: 47.4345
- Rouge2: 23.8936
- Rougel: 45.317
- Rougelsum: 45.4339
- Bleu: 0.0657
- Gen Len: 58.0
## Model description
More information needed
## Intended uses & limitations
Code snippet -> NL intent
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:------:|:-------:|
| No log | 0.08 | 50 | 2.7823 | 35.8458 | 12.1898 | 33.7466 | 33.8377 | 0.0041 | 58.0 |
| No log | 0.17 | 100 | 2.4223 | 37.2633 | 13.429 | 34.4943 | 34.5533 | 0.0087 | 58.0 |
| No log | 0.25 | 150 | 2.2696 | 40.6963 | 16.5785 | 38.1213 | 38.16 | 0.0167 | 58.0 |
| No log | 0.34 | 200 | 2.3168 | 41.3324 | 17.292 | 39.0117 | 39.113 | 0.0173 | 58.0 |
| No log | 0.42 | 250 | 2.3187 | 41.1345 | 16.6829 | 38.8514 | 38.891 | 0.0237 | 58.0 |
| No log | 0.5 | 300 | 2.1701 | 41.0145 | 17.5601 | 39.166 | 39.249 | 0.0206 | 58.0 |
| No log | 0.59 | 350 | 2.2035 | 41.7506 | 17.7251 | 39.4856 | 39.5647 | 0.0292 | 58.0 |
| No log | 0.67 | 400 | 2.1006 | 43.0324 | 19.9801 | 40.8704 | 40.9399 | 0.0319 | 58.0 |
| No log | 0.76 | 450 | 2.0563 | 43.2151 | 18.7409 | 40.4183 | 40.502 | 0.0244 | 58.0 |
| 2.4902 | 0.84 | 500 | 2.0468 | 43.2215 | 18.3484 | 40.9498 | 41.0682 | 0.0317 | 58.0 |
| 2.4902 | 0.92 | 550 | 2.0222 | 44.9934 | 19.8389 | 42.4478 | 42.5687 | 0.0372 | 58.0 |
| 2.4902 | 1.01 | 600 | 2.1095 | 43.8293 | 19.5682 | 40.882 | 40.9518 | 0.0311 | 58.0 |
| 2.4902 | 1.09 | 650 | 2.0124 | 43.6928 | 19.6878 | 39.6602 | 39.7368 | 0.0417 | 58.0 |
| 2.4902 | 1.18 | 700 | 2.0027 | 46.2115 | 21.9475 | 43.5869 | 43.6713 | 0.0477 | 58.0 |
| 2.4902 | 1.26 | 750 | 1.9599 | 45.9388 | 22.0368 | 43.4731 | 43.5656 | 0.043 | 58.0 |
| 2.4902 | 1.34 | 800 | 1.9467 | 44.7518 | 20.4755 | 42.489 | 42.6274 | 0.0394 | 58.0 |
| 2.4902 | 1.43 | 850 | 1.9643 | 44.1584 | 20.8833 | 41.8848 | 41.9733 | 0.0441 | 58.0 |
| 2.4902 | 1.51 | 900 | 1.8926 | 47.3789 | 22.9104 | 45.0164 | 45.0822 | 0.0445 | 58.0 |
| 2.4902 | 1.6 | 950 | 1.8855 | 46.8329 | 22.1133 | 44.1788 | 44.2666 | 0.0431 | 58.0 |
| 1.8023 | 1.68 | 1000 | 1.9160 | 47.1319 | 22.9792 | 44.4807 | 44.6103 | 0.0475 | 58.0 |
| 1.8023 | 1.76 | 1050 | 1.8498 | 48.8005 | 24.4785 | 46.4564 | 46.5427 | 0.0576 | 58.0 |
| 1.8023 | 1.85 | 1100 | 1.8611 | 47.8327 | 23.2086 | 45.5999 | 45.6868 | 0.0487 | 58.0 |
| 1.8023 | 1.93 | 1150 | 1.8497 | 47.7267 | 23.2021 | 45.5104 | 45.546 | 0.0512 | 58.0 |
| 1.8023 | 2.02 | 1200 | 1.8335 | 47.1502 | 22.8336 | 44.7614 | 44.7927 | 0.0566 | 58.0 |
| 1.8023 | 2.1 | 1250 | 1.8779 | 46.6645 | 22.9162 | 44.0086 | 44.2021 | 0.0539 | 58.0 |
| 1.8023 | 2.18 | 1300 | 1.8514 | 48.1544 | 24.7977 | 45.949 | 46.0254 | 0.0719 | 58.0 |
| 1.8023 | 2.27 | 1350 | 1.8658 | 46.7655 | 23.4813 | 44.5872 | 44.6907 | 0.069 | 58.0 |
| 1.8023 | 2.35 | 1400 | 1.8400 | 46.2749 | 23.6528 | 44.3149 | 44.4056 | 0.0572 | 58.0 |
| 1.8023 | 2.44 | 1450 | 1.8343 | 46.6169 | 23.8005 | 44.5486 | 44.6125 | 0.0547 | 58.0 |
| 1.3851 | 2.52 | 1500 | 1.8220 | 47.4739 | 24.3457 | 45.4959 | 45.6216 | 0.0662 | 58.0 |
| 1.3851 | 2.61 | 1550 | 1.8333 | 47.6311 | 24.3616 | 45.5904 | 45.6146 | 0.0666 | 58.0 |
| 1.3851 | 2.69 | 1600 | 1.8091 | 47.4633 | 24.0785 | 45.2493 | 45.2845 | 0.0645 | 58.0 |
| 1.3851 | 2.77 | 1650 | 1.8085 | 47.6495 | 23.8386 | 45.5077 | 45.5848 | 0.0639 | 58.0 |
| 1.3851 | 2.86 | 1700 | 1.8377 | 46.9721 | 23.4325 | 44.8386 | 44.9003 | 0.0647 | 58.0 |
| 1.3851 | 2.94 | 1750 | 1.8238 | 47.5266 | 23.9843 | 45.3897 | 45.473 | 0.0653 | 58.0 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.2+cu102
- Datasets 2.1.0
- Tokenizers 0.10.3
|
ktangri/autotrain-financial-sentiment-765323474
|
ktangri
| 2022-04-20T14:35:01Z | 17 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain",
"en",
"dataset:ktangri/autotrain-data-financial-sentiment",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-20T14:34:03Z |
---
tags: autotrain
language: en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- ktangri/autotrain-data-financial-sentiment
co2_eq_emissions: 0.007501354635994803
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 765323474
- CO2 Emissions (in grams): 0.007501354635994803
## Validation Metrics
- Loss: 0.0447433702647686
- Accuracy: 0.9823788546255506
- Macro F1: 0.974405452470854
- Micro F1: 0.9823788546255506
- Weighted F1: 0.9823043153179869
- Macro Precision: 0.978208375548801
- Micro Precision: 0.9823788546255506
- Weighted Precision: 0.9823204968555985
- Macro Recall: 0.9707159078140736
- Micro Recall: 0.9823788546255506
- Weighted Recall: 0.9823788546255506
## 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/ktangri/autotrain-financial-sentiment-765323474
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("ktangri/autotrain-financial-sentiment-765323474", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("ktangri/autotrain-financial-sentiment-765323474", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
```
|
samwell/english_to_twi
|
samwell
| 2022-04-20T13:21:08Z | 0 | 0 |
keras
|
[
"keras",
"tf-keras",
"region:us"
] | null | 2022-04-20T13:12:53Z |
---
library_name: keras
---
## 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': 'RMSprop', 'learning_rate': 0.001, 'decay': 0.0, 'rho': 0.9, 'momentum': 0.0, 'epsilon': 1e-07, 'centered': False}
- training_precision: float32
## Training Metrics
| Epochs | Train Loss | Train Accuracy | Validation Loss | Validation Accuracy |
|--- |--- |--- |--- |--- |
| 1| 2.157| 0.289| 1.828| 0.371|
| 2| 1.795| 0.388| 1.746| 0.409|
| 3| 1.639| 0.436| 1.597| 0.43|
| 4| 1.523| 0.471| 1.54| 0.463|
| 5| 1.427| 0.503| 1.485| 0.479|
## Model Plot
<details>
<summary>View Model Plot</summary>
</details>
|
James-kc-min/AGT_Roberta2
|
James-kc-min
| 2022-04-20T12:51:42Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-20T10:13:46Z |
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AGT_Roberta Copied
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Text Classification
PyTorch
Transformers
apache-2.0
roberta
generated_from_trainer
Eval Results
Infinity Compatible
Model card
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AGT_Roberta
/
README.md
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---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: AGT_Roberta
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. -->
# AGT_Roberta
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-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: 5e-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: 3
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 1.16.0
- Tokenizers 0.12.1
|
dimbyTa/rock-challenge-ViT-two-by-two
|
dimbyTa
| 2022-04-20T11:19:22Z | 63 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-03-31T19:44:47Z |
---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: rock-challenge-ViT-two-by-two
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.9663800001144409
---
# rock-challenge-ViT-two-by-two
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
#### fines
#### large
#### medium
#### pellets
|
James-kc-min/AGT_Roberta
|
James-kc-min
| 2022-04-20T09:39:04Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-20T09:34:51Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: AGT_Roberta
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. -->
# AGT_Roberta
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-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: 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
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 1.16.0
- Tokenizers 0.12.1
|
eagles/focus_sum_gpt2
|
eagles
| 2022-04-20T09:03:21Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-04-20T07:56:28Z |
---
tags:
- generated_from_trainer
model-index:
- name: focus_sum_gpt2
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. -->
# focus_sum_gpt2
This model is a fine-tuned version of [uer/gpt2-chinese-cluecorpussmall](https://huggingface.co/uer/gpt2-chinese-cluecorpussmall) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1917
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 1
- 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
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.5 | 15.15 | 500 | 1.1917 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.1.0
- Tokenizers 0.12.1
|
Souvikcmsa/Roberta_Sentiment_Analysis
|
Souvikcmsa
| 2022-04-20T08:53:33Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain",
"en",
"dataset:Souvikcmsa/autotrain-data-sentimentAnalysis_By_Souvik",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-20T08:50:58Z |
---
tags: autotrain
language: en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- Souvikcmsa/autotrain-data-sentimentAnalysis_By_Souvik
co2_eq_emissions: 4.453029772491864
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 762623422
- CO2 Emissions (in grams): 4.453029772491864
## Validation Metrics
- Loss: 0.40843138098716736
- Accuracy: 0.8302828618968386
- Macro F1: 0.8302447939743022
- Micro F1: 0.8302828618968385
- Weighted F1: 0.8302151855901072
- Macro Precision: 0.8310980209442669
- Micro Precision: 0.8302828618968386
- Weighted Precision: 0.8313262654775467
- Macro Recall: 0.8305699539252172
- Micro Recall: 0.8302828618968386
- Weighted Recall: 0.8302828618968386
## 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/Souvikcmsa/autotrain-sentimentAnalysis_By_Souvik-762623422
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("Souvikcmsa/autotrain-sentimentAnalysis_By_Souvik-762623422", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("Souvikcmsa/autotrain-sentimentAnalysis_By_Souvik-762623422", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
```
|
AswiN037/tamil-Roberta-small
|
AswiN037
| 2022-04-20T08:43:52Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"fill-mask",
"Tamil-Tokenizer",
"Tamil-language-model",
"dataset:oscar",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-04-20T08:29:14Z |
---
language:
- Tamil
tags:
- Tamil-Tokenizer
- Tamil-language-model
license: "apache-2.0"
datasets:
- oscar
---
# tokenizer - BPE 30_522 vocab size
## model - Roberta
trained using MLM
OSCAR dataset
train data size 5000 lines olly
|
demdecuong/vihealthbert-base-word
|
demdecuong
| 2022-04-20T07:55:52Z | 56 | 4 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-04-20T07:49:34Z |
# <a name="introduction"></a> ViHealthBERT: Pre-trained Language Models for Vietnamese in Health Text Mining
ViHealthBERT is the a strong baseline language models for Vietnamese in Healthcare domain.
We empirically investigate our model with different training strategies, achieving state of the art (SOTA) performances on 3 downstream tasks: NER (COVID-19 & ViMQ), Acronym Disambiguation, and Summarization.
We introduce two Vietnamese datasets: the acronym dataset (acrDrAid) and the FAQ summarization dataset in the healthcare domain. Our acrDrAid dataset is annotated with 135 sets of keywords.
The general approaches and experimental results of ViHealthBERT can be found in our LREC-2022 Poster [paper]() (updated soon):
@article{vihealthbert,
title = {{ViHealthBERT: Pre-trained Language Models for Vietnamese in Health Text Mining}},
author = {Minh Phuc Nguyen, Vu Hoang Tran, Vu Hoang, Ta Duc Huy, Trung H. Bui, Steven Q. H. Truong },
journal = {13th Edition of its Language Resources and Evaluation Conference},
year = {2022}
}
### Installation <a name="install2"></a>
- Python 3.6+, and PyTorch >= 1.6
- Install `transformers`:
`pip install transformers==4.2.0`
### Pre-trained models <a name="models2"></a>
Model | #params | Arch. | Tokenizer
---|---|---|---
`demdecuong/vihealthbert-base-word` | 135M | base | Word-level
`demdecuong/vihealthbert-base-syllable` | 135M | base | Syllable-level
### Example usage <a name="usage1"></a>
```python
import torch
from transformers import AutoModel, AutoTokenizer
vihealthbert = AutoModel.from_pretrained("demdecuong/vihealthbert-base-word")
tokenizer = AutoTokenizer.from_pretrained("demdecuong/vihealthbert-base-word")
# INPUT TEXT MUST BE ALREADY WORD-SEGMENTED!
line = "Tôi là sinh_viên trường đại_học Công_nghệ ."
input_ids = torch.tensor([tokenizer.encode(line)])
with torch.no_grad():
features = vihealthbert(input_ids) # Models outputs are now tuples
```
### Example usage for raw text <a name="usage2"></a>
Since ViHealthBERT used the [RDRSegmenter](https://github.com/datquocnguyen/RDRsegmenter) from [VnCoreNLP](https://github.com/vncorenlp/VnCoreNLP) to pre-process the pre-training data.
We highly recommend use the same word-segmenter for ViHealthBERT downstream applications.
#### Installation
```
# Install the vncorenlp python wrapper
pip3 install vncorenlp
# Download VnCoreNLP-1.1.1.jar & its word segmentation component (i.e. RDRSegmenter)
mkdir -p vncorenlp/models/wordsegmenter
wget https://raw.githubusercontent.com/vncorenlp/VnCoreNLP/master/VnCoreNLP-1.1.1.jar
wget https://raw.githubusercontent.com/vncorenlp/VnCoreNLP/master/models/wordsegmenter/vi-vocab
wget https://raw.githubusercontent.com/vncorenlp/VnCoreNLP/master/models/wordsegmenter/wordsegmenter.rdr
mv VnCoreNLP-1.1.1.jar vncorenlp/
mv vi-vocab vncorenlp/models/wordsegmenter/
mv wordsegmenter.rdr vncorenlp/models/wordsegmenter/
```
`VnCoreNLP-1.1.1.jar` (27MB) and folder `models/` must be placed in the same working folder.
#### Example usage
```
# See more details at: https://github.com/vncorenlp/VnCoreNLP
# Load rdrsegmenter from VnCoreNLP
from vncorenlp import VnCoreNLP
rdrsegmenter = VnCoreNLP("/Absolute-path-to/vncorenlp/VnCoreNLP-1.1.1.jar", annotators="wseg", max_heap_size='-Xmx500m')
# Input
text = "Ông Nguyễn Khắc Chúc đang làm việc tại Đại học Quốc gia Hà Nội. Bà Lan, vợ ông Chúc, cũng làm việc tại đây."
# To perform word (and sentence) segmentation
sentences = rdrsegmenter.tokenize(text)
for sentence in sentences:
print(" ".join(sentence))
```
|
ctoraman/RoBERTa-TR-medium-wp-66k
|
ctoraman
| 2022-04-20T07:01:39Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"fill-mask",
"tr",
"dataset:oscar",
"arxiv:2204.08832",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-09T09:15:04Z |
---
language:
- tr
tags:
- roberta
license: cc-by-nc-sa-4.0
datasets:
- oscar
---
# RoBERTa Turkish medium WordPiece 66k (uncased)
Pretrained model on Turkish language using a masked language modeling (MLM) objective. The model is uncased.
The pretrained corpus is OSCAR's Turkish split, but it is further filtered and cleaned.
Model architecture is similar to bert-medium (8 layers, 8 heads, and 512 hidden size). Tokenization algorithm is WordPiece. Vocabulary size is 66.7k.
The details and performance comparisons can be found at this paper:
https://arxiv.org/abs/2204.08832
The following code can be used for model loading and tokenization, example max length (514) can be changed:
```
model = AutoModel.from_pretrained([model_path])
#for sequence classification:
#model = AutoModelForSequenceClassification.from_pretrained([model_path], num_labels=[num_classes])
tokenizer = PreTrainedTokenizerFast(tokenizer_file=[file_path])
tokenizer.mask_token = "[MASK]"
tokenizer.cls_token = "[CLS]"
tokenizer.sep_token = "[SEP]"
tokenizer.pad_token = "[PAD]"
tokenizer.unk_token = "[UNK]"
tokenizer.bos_token = "[CLS]"
tokenizer.eos_token = "[SEP]"
tokenizer.model_max_length = 514
```
### BibTeX entry and citation info
```bibtex
@misc{https://doi.org/10.48550/arxiv.2204.08832,
doi = {10.48550/ARXIV.2204.08832},
url = {https://arxiv.org/abs/2204.08832},
author = {Toraman, Cagri and Yilmaz, Eyup Halit and Şahinuç, Furkan and Ozcelik, Oguzhan},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Impact of Tokenization on Language Models: An Analysis for Turkish},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution Non Commercial Share Alike 4.0 International}
}
```
|
ctoraman/RoBERTa-TR-medium-wp-16k
|
ctoraman
| 2022-04-20T07:00:50Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"fill-mask",
"tr",
"dataset:oscar",
"arxiv:2204.08832",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-08T14:03:17Z |
---
language:
- tr
tags:
- roberta
license: cc-by-nc-sa-4.0
datasets:
- oscar
---
# RoBERTa Turkish medium WordPiece 16k (uncased)
Pretrained model on Turkish language using a masked language modeling (MLM) objective. The model is uncased.
The pretrained corpus is OSCAR's Turkish split, but it is further filtered and cleaned.
Model architecture is similar to bert-medium (8 layers, 8 heads, and 512 hidden size). Tokenization algorithm is WordPiece. Vocabulary size is 16.7k.
The details and performance comparisons can be found at this paper:
https://arxiv.org/abs/2204.08832
The following code can be used for model loading and tokenization, example max length (514) can be changed:
```
model = AutoModel.from_pretrained([model_path])
#for sequence classification:
#model = AutoModelForSequenceClassification.from_pretrained([model_path], num_labels=[num_classes])
tokenizer = PreTrainedTokenizerFast(tokenizer_file=[file_path])
tokenizer.mask_token = "[MASK]"
tokenizer.cls_token = "[CLS]"
tokenizer.sep_token = "[SEP]"
tokenizer.pad_token = "[PAD]"
tokenizer.unk_token = "[UNK]"
tokenizer.bos_token = "[CLS]"
tokenizer.eos_token = "[SEP]"
tokenizer.model_max_length = 514
```
### BibTeX entry and citation info
```bibtex
@misc{https://doi.org/10.48550/arxiv.2204.08832,
doi = {10.48550/ARXIV.2204.08832},
url = {https://arxiv.org/abs/2204.08832},
author = {Toraman, Cagri and Yilmaz, Eyup Halit and Şahinuç, Furkan and Ozcelik, Oguzhan},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Impact of Tokenization on Language Models: An Analysis for Turkish},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution Non Commercial Share Alike 4.0 International}
}
```
|
ctoraman/RoBERTa-TR-medium-word-7k
|
ctoraman
| 2022-04-20T07:00:26Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"fill-mask",
"tr",
"dataset:oscar",
"arxiv:2204.08832",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-09T13:17:25Z |
---
language:
- tr
tags:
- roberta
license: cc-by-nc-sa-4.0
datasets:
- oscar
---
# RoBERTa Turkish medium Word-level 7k (uncased)
Pretrained model on Turkish language using a masked language modeling (MLM) objective. The model is uncased.
The pretrained corpus is OSCAR's Turkish split, but it is further filtered and cleaned.
Model architecture is similar to bert-medium (8 layers, 8 heads, and 512 hidden size). Tokenization algorithm is Word-level, which means text is split by white space. Vocabulary size is 7.5k.
The details and performance comparisons can be found at this paper:
https://arxiv.org/abs/2204.08832
The following code can be used for model loading and tokenization, example max length (514) can be changed:
```
model = AutoModel.from_pretrained([model_path])
#for sequence classification:
#model = AutoModelForSequenceClassification.from_pretrained([model_path], num_labels=[num_classes])
tokenizer = PreTrainedTokenizerFast(tokenizer_file=[file_path])
tokenizer.mask_token = "[MASK]"
tokenizer.cls_token = "[CLS]"
tokenizer.sep_token = "[SEP]"
tokenizer.pad_token = "[PAD]"
tokenizer.unk_token = "[UNK]"
tokenizer.bos_token = "[CLS]"
tokenizer.eos_token = "[SEP]"
tokenizer.model_max_length = 514
```
### BibTeX entry and citation info
```bibtex
@misc{https://doi.org/10.48550/arxiv.2204.08832,
doi = {10.48550/ARXIV.2204.08832},
url = {https://arxiv.org/abs/2204.08832},
author = {Toraman, Cagri and Yilmaz, Eyup Halit and Şahinuç, Furkan and Ozcelik, Oguzhan},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Impact of Tokenization on Language Models: An Analysis for Turkish},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution Non Commercial Share Alike 4.0 International}
}
```
|
ctoraman/RoBERTa-TR-medium-morph-66k
|
ctoraman
| 2022-04-20T06:58:48Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"fill-mask",
"tr",
"dataset:oscar",
"arxiv:2204.08832",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-09T12:47:05Z |
---
language:
- tr
tags:
- roberta
license: cc-by-nc-sa-4.0
datasets:
- oscar
---
# RoBERTa Turkish medium Morph-level 66k (uncased)
Pretrained model on Turkish language using a masked language modeling (MLM) objective. The model is uncased.
The pretrained corpus is OSCAR's Turkish split, but it is further filtered and cleaned.
Model architecture is similar to bert-medium (8 layers, 8 heads, and 512 hidden size). Tokenization algorithm is Morph-level, which means that text is split according to a Turkish morphological analyzer (Zemberek). Vocabulary size is 64.2k.
## Note that this model needs a preprocessing step before running, because the tokenizer file is not a morphological anaylzer. That is, the test dataset can not be split into morphemes with the tokenizer file. The user needs to process any test dataset by a Turkish morphological analyzer (Zemberek in this case) before running evaluation.
The details and performance comparisons can be found at this paper:
https://arxiv.org/abs/2204.08832
The following code can be used for model loading and tokenization, example max length (514) can be changed:
```
model = AutoModel.from_pretrained([model_path])
#for sequence classification:
#model = AutoModelForSequenceClassification.from_pretrained([model_path], num_labels=[num_classes])
tokenizer = PreTrainedTokenizerFast(tokenizer_file=[file_path])
tokenizer.mask_token = "[MASK]"
tokenizer.cls_token = "[CLS]"
tokenizer.sep_token = "[SEP]"
tokenizer.pad_token = "[PAD]"
tokenizer.unk_token = "[UNK]"
tokenizer.bos_token = "[CLS]"
tokenizer.eos_token = "[SEP]"
tokenizer.model_max_length = 514
```
### BibTeX entry and citation info
```bibtex
@misc{https://doi.org/10.48550/arxiv.2204.08832,
doi = {10.48550/ARXIV.2204.08832},
url = {https://arxiv.org/abs/2204.08832},
author = {Toraman, Cagri and Yilmaz, Eyup Halit and Şahinuç, Furkan and Ozcelik, Oguzhan},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Impact of Tokenization on Language Models: An Analysis for Turkish},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution Non Commercial Share Alike 4.0 International}
}
```
|
ctoraman/RoBERTa-TR-medium-bpe-66k
|
ctoraman
| 2022-04-20T06:54:49Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"fill-mask",
"tr",
"dataset:oscar",
"arxiv:2204.08832",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-09T12:10:26Z |
---
language:
- tr
tags:
- roberta
license: cc-by-nc-sa-4.0
datasets:
- oscar
---
# RoBERTa Turkish medium BPE 66k (uncased)
Pretrained model on Turkish language using a masked language modeling (MLM) objective. The model is uncased.
The pretrained corpus is OSCAR's Turkish split, but it is further filtered and cleaned.
Model architecture is similar to bert-medium (8 layers, 8 heads, and 512 hidden size). Tokenization algorithm is BPE. Vocabulary size is 66.7k.
The details and performance comparisons can be found at this paper:
https://arxiv.org/abs/2204.08832
The following code can be used for model loading and tokenization, example max length (514) can be changed:
```
model = AutoModel.from_pretrained([model_path])
#for sequence classification:
#model = AutoModelForSequenceClassification.from_pretrained([model_path], num_labels=[num_classes])
tokenizer = PreTrainedTokenizerFast(tokenizer_file=[file_path])
tokenizer.mask_token = "[MASK]"
tokenizer.cls_token = "[CLS]"
tokenizer.sep_token = "[SEP]"
tokenizer.pad_token = "[PAD]"
tokenizer.unk_token = "[UNK]"
tokenizer.bos_token = "[CLS]"
tokenizer.eos_token = "[SEP]"
tokenizer.model_max_length = 514
```
### BibTeX entry and citation info
```bibtex
@misc{https://doi.org/10.48550/arxiv.2204.08832,
doi = {10.48550/ARXIV.2204.08832},
url = {https://arxiv.org/abs/2204.08832},
author = {Toraman, Cagri and Yilmaz, Eyup Halit and Şahinuç, Furkan and Ozcelik, Oguzhan},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Impact of Tokenization on Language Models: An Analysis for Turkish},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution Non Commercial Share Alike 4.0 International}
}
```
|
ctoraman/RoBERTa-TR-medium-bpe-28k
|
ctoraman
| 2022-04-20T06:48:33Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"fill-mask",
"tr",
"dataset:oscar",
"arxiv:2204.08832",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-09T12:00:46Z |
---
language:
- tr
tags:
- roberta
license: cc-by-nc-sa-4.0
datasets:
- oscar
---
# RoBERTa Turkish medium BPE 28k (uncased)
Pretrained model on Turkish language using a masked language modeling (MLM) objective. The model is uncased.
The pretrained corpus is OSCAR's Turkish split, but it is further filtered and cleaned.
Model architecture is similar to bert-medium (8 layers, 8 heads, and 512 hidden size). Tokenization algorithm is BPE. Vocabulary size is 28.6k.
The details and performance comparisons can be found at this paper:
https://arxiv.org/abs/2204.08832
The following code can be used for model loading and tokenization, example max length (514) can be changed:
```
model = AutoModel.from_pretrained([model_path])
#for sequence classification:
#model = AutoModelForSequenceClassification.from_pretrained([model_path], num_labels=[num_classes])
tokenizer = PreTrainedTokenizerFast(tokenizer_file=[file_path])
tokenizer.mask_token = "[MASK]"
tokenizer.cls_token = "[CLS]"
tokenizer.sep_token = "[SEP]"
tokenizer.pad_token = "[PAD]"
tokenizer.unk_token = "[UNK]"
tokenizer.bos_token = "[CLS]"
tokenizer.eos_token = "[SEP]"
tokenizer.model_max_length = 514
```
### BibTeX entry and citation info
```bibtex
@misc{https://doi.org/10.48550/arxiv.2204.08832,
doi = {10.48550/ARXIV.2204.08832},
url = {https://arxiv.org/abs/2204.08832},
author = {Toraman, Cagri and Yilmaz, Eyup Halit and Şahinuç, Furkan and Ozcelik, Oguzhan},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Impact of Tokenization on Language Models: An Analysis for Turkish},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution Non Commercial Share Alike 4.0 International}
}
```
|
ctoraman/RoBERTa-TR-medium-bpe-16k
|
ctoraman
| 2022-04-20T06:48:03Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"fill-mask",
"tr",
"dataset:oscar",
"arxiv:2204.08832",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-08T13:44:50Z |
---
language:
- tr
tags:
- roberta
license: cc-by-nc-sa-4.0
datasets:
- oscar
---
# RoBERTa Turkish medium BPE 16k (uncased)
Pretrained model on Turkish language using a masked language modeling (MLM) objective. The model is uncased.
The pretrained corpus is OSCAR's Turkish split, but it is further filtered and cleaned.
Model architecture is similar to bert-medium (8 layers, 8 heads, and 512 hidden size). Tokenization algorithm is BPE. Vocabulary size is 16.7k.
The details and performance comparisons can be found at this paper:
https://arxiv.org/abs/2204.08832
The following code can be used for model loading and tokenization, example max length (514) can be changed:
```
model = AutoModel.from_pretrained([model_path])
#for sequence classification:
#model = AutoModelForSequenceClassification.from_pretrained([model_path], num_labels=[num_classes])
tokenizer = PreTrainedTokenizerFast(tokenizer_file=[file_path])
tokenizer.mask_token = "[MASK]"
tokenizer.cls_token = "[CLS]"
tokenizer.sep_token = "[SEP]"
tokenizer.pad_token = "[PAD]"
tokenizer.unk_token = "[UNK]"
tokenizer.bos_token = "[CLS]"
tokenizer.eos_token = "[SEP]"
tokenizer.model_max_length = 514
```
### BibTeX entry and citation info
```bibtex
@misc{https://doi.org/10.48550/arxiv.2204.08832,
doi = {10.48550/ARXIV.2204.08832},
url = {https://arxiv.org/abs/2204.08832},
author = {Toraman, Cagri and Yilmaz, Eyup Halit and Şahinuç, Furkan and Ozcelik, Oguzhan},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Impact of Tokenization on Language Models: An Analysis for Turkish},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution Non Commercial Share Alike 4.0 International}
}
```
|
huggingtweets/elonmusk-iamsrk
|
huggingtweets
| 2022-04-20T04:58:07Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-04-20T04:52:51Z |
---
language: en
thumbnail: http://www.huggingtweets.com/elonmusk-iamsrk/1650430682800/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/1503591435324563456/foUrqiEw_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/1318511011117199362/htNsviXp_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>
<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">Elon Musk & Shah Rukh Khan</div>
<div style="text-align: center; font-size: 14px;">@elonmusk-iamsrk</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 Elon Musk & Shah Rukh Khan.
| Data | Elon Musk | Shah Rukh Khan |
| --- | --- | --- |
| Tweets downloaded | 221 | 3212 |
| Retweets | 14 | 56 |
| Short tweets | 69 | 278 |
| Tweets kept | 138 | 2878 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/39qg1l4s/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 @elonmusk-iamsrk's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/840j96ek) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/840j96ek/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/elonmusk-iamsrk')
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)
|
mdm/DialoGPT-small-Kanye
|
mdm
| 2022-04-20T04:25:18Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-04-16T04:34:24Z |
---
tags:
- conversational
---
# Kanye West AI - DialoGPT Small
Kanye West DialoGPT model built with lyrics from Kaggle (https://www.kaggle.com/datasets/convolutionalnn/kanye-west-lyrics-dataset) and resources from Lynn Zheng
|
proseph/ctrlv-wav2vec2-tokenizer
|
proseph
| 2022-04-20T03:40:35Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-04-20T01:08:37Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: ctrlv-wav2vec2-tokenizer
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. -->
# ctrlv-wav2vec2-tokenizer
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: 0.3967
- Wer: 0.3138
## 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: 32
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.4359 | 3.45 | 500 | 1.3595 | 0.9159 |
| 0.5692 | 6.9 | 1000 | 0.4332 | 0.4036 |
| 0.2198 | 10.34 | 1500 | 0.4074 | 0.3678 |
| 0.1314 | 13.79 | 2000 | 0.3480 | 0.3409 |
| 0.0929 | 17.24 | 2500 | 0.3714 | 0.3346 |
| 0.0692 | 20.69 | 3000 | 0.3977 | 0.3224 |
| 0.0542 | 24.14 | 3500 | 0.4068 | 0.3187 |
| 0.0422 | 27.59 | 4000 | 0.3967 | 0.3138 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3
|
tuhailong/bi_encoder_roberta-wwm-ext
|
tuhailong
| 2022-04-20T02:45:22Z | 10 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"feature-extraction",
"sbert",
"zh",
"dataset:dialogue",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-04-19T11:28:07Z |
---
language: zh
tags:
- sbert
datasets:
- dialogue
---
# Data
train data is similarity sentence data from E-commerce dialogue, about 50w sentence pairs.
## Model
model created by [sentence-tansformers](https://www.sbert.net/index.html)，model struct is bi-encoder
### Usage
```python
>>> from sentence_transformers import SentenceTransformer, util
>>> model = SentenceTransformer("tuhailong/bi_encoder_roberta-wwm-ext", device="cuda:1")
>>> model.max_seq_length=32
>>> sentences = ["今天天气不错", "今天心情不错"]
>>> embeddings1 = model.encode([sentences[0]], convert_to_tensor=True)
>>> embeddings2 = model.encode([sentences[1]], convert_to_tensor=True)
>>> scores = util.cos_sim(embeddings1, embeddings2).cpu().numpy()
>>> print(scores)
```
#### Code
train code from https://github.com/TTurn/bi-encoder
##### PS
Because add the pooling layer and dense layer after model，has folders in model files. So here will
be additional files "1_Pooling-config.json", "2_Dense-config.json" and "2_Dense-pytorch_model.bin".
after download these files, rename them as "1_Pooling/config.json", "2_Dense/config.json" and "2_Dense/pytorch_model.bin".
|
tuhailong/PairSupCon-roberta-wwm-ext
|
tuhailong
| 2022-04-20T02:44:32Z | 9 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"sbert",
"zh",
"dataset:dialogue",
"endpoints_compatible",
"region:us"
] | null | 2022-04-19T13:09:36Z |
---
language: zh
tags:
- sbert
datasets:
- dialogue
---
# Data
train data is similarity sentence data from E-commerce dialogue, about 50w sentence pairs.
## Model
model created by [sentence-tansformers](https://www.sbert.net/index.html)，model struct is bi-encoder
model's train code by [PairSupCon](https://github.com/amazon-research/sentence-representations/tree/main/PairSupCon)
### Usage
[test.py](https://github.com/TTurn/sentence-representations/edit/main/PairSupCon/test.py)
#### Code
train code from https://github.com/TTurn/sentence-representations/tree/main/PairSupCon
|
tuhailong/cross_encoder_roberta-wwm-ext_v2
|
tuhailong
| 2022-04-20T02:41:07Z | 28 | 2 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"cross-encoder",
"zh",
"dataset:dialogue",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-19T11:21:05Z |
---
language: zh
tags:
- cross-encoder
datasets:
- dialogue
---
# Data
train data is similarity sentence data from E-commerce dialogue, about 50w sentence pairs.
## Model
model created by [sentence-tansformers](https://www.sbert.net/index.html)，model struct is cross-encoder, pretrained model is hfl/chinese-roberta-wwm-ext.
This model structure is as same as [tuhailong/cross_encoder_roberta-wwm-ext_v1](https://huggingface.co/tuhailong/cross_encoder_roberta-wwm-ext_v1)，the difference is changing the epoch from 5 to 1, the performance is better in my dataset.
### Usage
```python
>>> from sentence_transformers.cross_encoder import CrossEncoder
>>> model = CrossEncoder(model_save_path, device="cuda", max_length=64)
>>> sentences = ["今天天气不错", "今天心情不错"]
>>> score = model.predict([sentences])
>>> print(score[0])
```
#### Code
train code from https://github.com/TTurn/cross-encoder
|
tuhailong/cross_encoder_electra-180g-large-discriminator
|
tuhailong
| 2022-04-20T02:40:23Z | 12 | 1 |
transformers
|
[
"transformers",
"pytorch",
"electra",
"text-classification",
"cross-encoder",
"zh",
"dataset:dialogue",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-19T13:25:37Z |
---
language: zh
tags:
- cross-encoder
datasets:
- dialogue
---
# Data
train data is similarity sentence data from E-commerce dialogue, about 50w sentence pairs.
## Model
model created by [sentence-tansformers](https://www.sbert.net/index.html)，model struct is cross-encoder，pretrained model is hfl/chinese-electra-180g-large-discriminator.
### Usage
```python
>>> from sentence_transformers.cross_encoder import CrossEncoder
>>> model = CrossEncoder(model_save_path, device="cuda", max_length=64)
>>> sentences = ["今天天气不错", "今天心情不错"]
>>> score = model.predict([sentences])
>>> print(score[0])
```
#### Code
train code from https://github.com/TTurn/cross-encoder
|
ChrisZeng/electra-large-discriminator-nli-efl-tweeteval
|
ChrisZeng
| 2022-04-20T02:05:43Z | 15 | 0 |
transformers
|
[
"transformers",
"pytorch",
"electra",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-19T00:29:30Z |
---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: electra-large-discriminator-nli-efl-tweeteval
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. -->
# electra-large-discriminator-nli-efl-tweeteval
This model is a fine-tuned version of [ynie/electra-large-discriminator-snli_mnli_fever_anli_R1_R2_R3-nli](https://huggingface.co/ynie/electra-large-discriminator-snli_mnli_fever_anli_R1_R2_R3-nli) on the None dataset.
It achieves the following results on the evaluation set:
- Accuracy: 0.7943
- F1: 0.7872
- Loss: 0.3004
## 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-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- 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 | Accuracy | F1 | Validation Loss |
|:-------------:|:-----:|:----:|:--------:|:------:|:---------------:|
| 0.4384 | 1.0 | 163 | 0.7444 | 0.7308 | 0.3962 |
| 0.3447 | 2.0 | 326 | 0.7659 | 0.7552 | 0.3410 |
| 0.3057 | 3.0 | 489 | 0.7750 | 0.7688 | 0.3234 |
| 0.287 | 4.0 | 652 | 0.7857 | 0.7779 | 0.3069 |
| 0.2742 | 5.0 | 815 | 0.7887 | 0.7822 | 0.3030 |
| 0.2676 | 6.0 | 978 | 0.7939 | 0.7851 | 0.2982 |
| 0.2585 | 7.0 | 1141 | 0.7909 | 0.7822 | 0.3002 |
| 0.2526 | 8.0 | 1304 | 0.7943 | 0.7876 | 0.3052 |
| 0.2479 | 9.0 | 1467 | 0.7939 | 0.7847 | 0.2997 |
| 0.2451 | 10.0 | 1630 | 0.7956 | 0.7873 | 0.3014 |
| 0.2397 | 11.0 | 1793 | 0.7943 | 0.7872 | 0.3004 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.12.0.dev20220417
- Datasets 2.1.0
- Tokenizers 0.10.3
|
Aldraz/distilbert-base-uncased-finetuned-emotion
|
Aldraz
| 2022-04-20T02:04:55Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-19T23:26:01Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
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-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2319
- Accuracy: 0.921
- F1: 0.9214
## 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 | 250 | 0.3369 | 0.8985 | 0.8947 |
| No log | 2.0 | 500 | 0.2319 | 0.921 | 0.9214 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.9.1+cpu
- Datasets 2.1.0
- Tokenizers 0.11.6
|
csikasote/xls-r-1b-bemba-10hrs
|
csikasote
| 2022-04-19T22:51:51Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-04-19T13:35:26Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: xls-r-1b-bemba-10hrs
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. -->
# xls-r-1b-bemba-10hrs
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2350
- Wer: 0.3524
## 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: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 2.2547 | 0.54 | 400 | 0.4199 | 0.5888 |
| 0.5422 | 1.07 | 800 | 0.2689 | 0.4360 |
| 0.4154 | 1.61 | 1200 | 0.2342 | 0.4008 |
| 0.4075 | 2.15 | 1600 | 0.2172 | 0.3579 |
| 0.3326 | 2.68 | 2000 | 0.2151 | 0.3603 |
| 0.2837 | 3.22 | 2400 | 0.2117 | 0.3505 |
| 0.2688 | 3.76 | 2800 | 0.2040 | 0.3559 |
| 0.2401 | 4.3 | 3200 | 0.2099 | 0.3445 |
| 0.2176 | 4.83 | 3600 | 0.1973 | 0.3299 |
| 0.1913 | 5.37 | 4000 | 0.2123 | 0.3432 |
| 0.1683 | 5.91 | 4400 | 0.2032 | 0.3358 |
| 0.1445 | 6.44 | 4800 | 0.2350 | 0.3524 |
### Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 2.1.0
- Tokenizers 0.12.1
|
zafercavdar/distilbert-base-turkish-cased-emotion
|
zafercavdar
| 2022-04-19T22:03:18Z | 7 | 8 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"emotion",
"tr",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-19T21:16:33Z |
---
language:
- tr
thumbnail: https://avatars3.githubusercontent.com/u/32437151?s=460&u=4ec59abc8d21d5feea3dab323d23a5860e6996a4&v=4
tags:
- text-classification
- emotion
- pytorch
datasets:
- emotion (Translated to Turkish)
metrics:
- Accuracy, F1 Score
---
# distilbert-base-turkish-cased-emotion
## Model description:
[Distilbert-base-turkish-cased](https://huggingface.co/dbmdz/distilbert-base-turkish-cased) finetuned on the emotion dataset (Translated to Turkish via Google Translate API) using HuggingFace Trainer with below Hyperparameters
```
learning rate 2e-5,
batch size 64,
num_train_epochs=8,
```
## Model Performance Comparision on Emotion Dataset from Twitter:
| Model | Accuracy | F1 Score | Test Sample per Second |
| --- | --- | --- | --- |
| [Distilbert-base-turkish-cased-emotion](https://huggingface.co/zafercavdar/distilbert-base-turkish-cased-emotion) | 83.25 | 83.17 | 232.197 |
## How to Use the model:
```python
from transformers import pipeline
classifier = pipeline("text-classification",
model='zafercavdar/distilbert-base-turkish-cased-emotion',
return_all_scores=True)
prediction = classifier("Bu kütüphaneyi seviyorum, en iyi yanı kolay kullanımı.", )
print(prediction)
"""
Output:
[
[
{'label': 'sadness', 'score': 0.0026786490343511105},
{'label': 'joy', 'score': 0.6600754261016846},
{'label': 'love', 'score': 0.3203163146972656},
{'label': 'anger', 'score': 0.004358913749456406},
{'label': 'fear', 'score': 0.002354539930820465},
{'label': 'surprise', 'score': 0.010216088965535164}
]
]
"""
```
## Dataset:
[Twitter-Sentiment-Analysis](https://huggingface.co/nlp/viewer/?dataset=emotion).
## Eval results
```json
{
'eval_accuracy': 0.8325,
'eval_f1': 0.8317301441160213,
'eval_loss': 0.5021793842315674,
'eval_runtime': 8.6167,
'eval_samples_per_second': 232.108,
'eval_steps_per_second': 3.714
}
```
|
ZZ99/tapt_nbme_deberta_v3_base
|
ZZ99
| 2022-04-19T21:18:00Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"deberta-v2",
"fill-mask",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-04-11T13:02:18Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: test-mlm
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. -->
# test-mlm
This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0870
- Accuracy: 0.7576
## 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: 7e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.18.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 2.0.0
- Tokenizers 0.11.6
|
gbennett/distilbert-base-uncased-finetuned-emotion
|
gbennett
| 2022-04-19T20:26:52Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-19T18:33:45Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9185
- name: F1
type: f1
value: 0.9188211123089982
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2260
- Accuracy: 0.9185
- F1: 0.9188
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8134 | 1.0 | 250 | 0.3117 | 0.908 | 0.9056 |
| 0.2477 | 2.0 | 500 | 0.2260 | 0.9185 | 0.9188 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
huggingtweets/billgates-kellytclements-xychelsea
|
huggingtweets
| 2022-04-19T20:11:34Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-04-19T20:06:34Z |
---
language: en
thumbnail: http://www.huggingtweets.com/billgates-kellytclements-xychelsea/1650398924367/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/1256728742292074496/96By_wwT_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/1414439092373254147/JdS8yLGI_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/1431338485504430082/zQ6S8nOo_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">Chelsea E. Manning & Bill Gates & Kelly T. Clements</div>
<div style="text-align: center; font-size: 14px;">@billgates-kellytclements-xychelsea</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 Chelsea E. Manning & Bill Gates & Kelly T. Clements.
| Data | Chelsea E. Manning | Bill Gates | Kelly T. Clements |
| --- | --- | --- | --- |
| Tweets downloaded | 3248 | 3213 | 1777 |
| Retweets | 15 | 199 | 296 |
| Short tweets | 1219 | 7 | 26 |
| Tweets kept | 2014 | 3007 | 1455 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/37pv1ayu/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 @billgates-kellytclements-xychelsea's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2e303z5q) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2e303z5q/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/billgates-kellytclements-xychelsea')
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)
|
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