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airKlizz/mt5-base-wikinewssum-english-100 | airKlizz | 2021-12-31T12:02:27Z | 14 | 0 | transformers | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"summarization",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | summarization | 2022-03-02T23:29:05Z | ---
license: apache-2.0
tags:
- summarization
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mt5-base-wikinewssum-english-100
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-base-wikinewssum-english-100
This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 6.6225
- Rouge1: 3.909
- Rouge2: 0.9312
- Rougel: 3.3835
- Rougelsum: 3.7786
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5.6e-05
- train_batch_size: 4
- eval_batch_size: 4
- 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
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| No log | 0.96 | 12 | 14.4949 | 2.7398 | 0.7181 | 2.491 | 2.6561 |
| No log | 1.96 | 24 | 10.5056 | 4.4428 | 1.4293 | 3.8469 | 4.2869 |
| No log | 2.96 | 36 | 8.9856 | 4.1179 | 1.229 | 3.5726 | 3.9693 |
| No log | 3.96 | 48 | 7.7950 | 3.9217 | 1.1339 | 3.4256 | 3.7905 |
| No log | 4.96 | 60 | 7.0734 | 3.8004 | 1.0326 | 3.3246 | 3.6766 |
| No log | 5.96 | 72 | 6.7897 | 3.6351 | 0.9162 | 3.1839 | 3.5149 |
| No log | 6.96 | 84 | 6.6610 | 3.7486 | 0.8829 | 3.2583 | 3.6193 |
| No log | 7.96 | 96 | 6.6225 | 3.909 | 0.9312 | 3.3835 | 3.7786 |
### Framework versions
- Transformers 4.13.0
- Pytorch 1.10.1
- Datasets 1.16.1
- Tokenizers 0.10.3
|
Muennighoff/SBERT-base-nli-stsb-v2 | Muennighoff | 2021-12-31T07:59:14Z | 4 | 0 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | sentence-similarity | 2022-03-02T23:29:04Z | ---
pipeline_tag: sentence-similarity
license: apache-2.0
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
This model is used in "TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning".
|
federicopascual/finetuning-sentiment-analysis-model-3000-samples | federicopascual | 2021-12-30T20:32:34Z | 7 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-02T23:29:05Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-analysis-model-3000-samples
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.8733333333333333
- name: F1
type: f1
value: 0.88125
---
<!-- 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-analysis-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3130
- Accuracy: 0.8733
- F1: 0.8812
## 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 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
federicopascual/finetune-sentiment-analysis-model-3000-samples | federicopascual | 2021-12-30T19:29:48Z | 25 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-02T23:29:05Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: finetune-sentiment-analysis-model-3000-samples
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.8866666666666667
- name: F1
type: f1
value: 0.8944099378881988
---
<!-- 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. -->
# finetune-sentiment-analysis-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4558
- Accuracy: 0.8867
- F1: 0.8944
## 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 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
davanstrien/flyswot-test | davanstrien | 2021-12-30T16:35:07Z | 0 | 0 | null | [
"onnx",
"region:us"
] | null | 2022-03-02T23:29:05Z | # flyswot
## Model description
In progress model for detecting 'fake' flysheets
## Intended uses & limitations
Not currently intended for public consumption...
#### Limitations and bias
Not currently intended for public consumption...
## Training data
TODO
## Eval results
|
pinecone/bert-medqp-cross-encoder | pinecone | 2021-12-30T12:11:30Z | 7 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-02T23:29:05Z | # Med-QP Cross Encoder
Demo model for use as part of Augmented SBERT chapters of the [NLP for Semantic Search course](https://www.pinecone.io/learn/nlp). |
pinecone/bert-stsb-cross-encoder | pinecone | 2021-12-30T12:11:03Z | 0 | 0 | null | [
"region:us"
] | null | 2022-03-02T23:29:05Z | # STSb Cross Encoder
Demo model for use as part of Augmented SBERT chapters of the [NLP for Semantic Search course](https://www.pinecone.io/learn/nlp). |
rkmt/wav2vec2-base-timit-demo-colab | rkmt | 2021-12-30T00:39:31Z | 6 | 1 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"hubert",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2022-03-02T23:29:05Z | ---
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/hubert-large-ls960-ft](https://huggingface.co/facebook/hubert-large-ls960-ft) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0280
- Wer: 0.0082
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.1152 | 1.42 | 500 | 0.0416 | 0.0159 |
| 0.0803 | 2.83 | 1000 | 0.0372 | 0.0144 |
| 0.0672 | 4.25 | 1500 | 0.0345 | 0.0119 |
| 0.0564 | 5.67 | 2000 | 0.0338 | 0.0106 |
| 0.0513 | 7.08 | 2500 | 0.0307 | 0.0100 |
| 0.0448 | 8.5 | 3000 | 0.0343 | 0.0098 |
| 0.0374 | 9.92 | 3500 | 0.0300 | 0.0084 |
| 0.0368 | 11.33 | 4000 | 0.0314 | 0.0086 |
| 0.0388 | 12.75 | 4500 | 0.0283 | 0.0089 |
| 0.0277 | 14.16 | 5000 | 0.0302 | 0.0089 |
| 0.0298 | 15.58 | 5500 | 0.0298 | 0.0089 |
| 0.0271 | 17.0 | 6000 | 0.0320 | 0.0098 |
| 0.024 | 18.41 | 6500 | 0.0286 | 0.0088 |
| 0.0236 | 19.83 | 7000 | 0.0284 | 0.0084 |
| 0.0238 | 21.25 | 7500 | 0.0290 | 0.0086 |
| 0.0227 | 22.66 | 8000 | 0.0284 | 0.0093 |
| 0.0198 | 24.08 | 8500 | 0.0280 | 0.0088 |
| 0.0225 | 25.5 | 9000 | 0.0281 | 0.0086 |
| 0.018 | 26.91 | 9500 | 0.0280 | 0.0082 |
| 0.0178 | 28.33 | 10000 | 0.0280 | 0.0082 |
| 0.0209 | 29.75 | 10500 | 0.0280 | 0.0082 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.9.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
lgris/distilxlsr_bp_8-12 | lgris | 2021-12-30T00:37:53Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"wav2vec2",
"feature-extraction",
"speech",
"pt",
"arxiv:2110.01900",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | feature-extraction | 2022-03-02T23:29:05Z | ---
language: pt
tags:
- speech
license: apache-2.0
---
# DistilXLSR-53 for BP
[DistilXLSR-53 for BP: DistilHuBERT applied to Wav2vec XLSR-53 for Brazilian Portuguese](https://github.com/s3prl/s3prl/tree/master/s3prl/upstream/distiller)
The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.
**Note**: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model **speech recognition**, a tokenizer should be created and the model should be fine-tuned on labeled text data. Check out [this blog](https://huggingface.co/blog/fine-tune-wav2vec2-english) for more in-detail explanation of how to fine-tune the model.
Paper: [DistilHuBERT: Speech Representation Learning by Layer-wise Distillation of Hidden-unit BERT](https://arxiv.org/abs/2110.01900)
Authors: Heng-Jui Chang, Shu-wen Yang, Hung-yi Lee
**Note 2**: The XLSR-53 model was distilled using [Brazilian Portuguese Datasets](https://huggingface.co/lgris/bp400-xlsr) for test purposes. The dataset is quite small to perform such task (the performance might not be so good as the [original work](https://arxiv.org/abs/2110.01900)).
**Abstract**
Self-supervised speech representation learning methods like wav2vec 2.0 and Hidden-unit BERT (HuBERT) leverage unlabeled speech data for pre-training and offer good representations for numerous speech processing tasks. Despite the success of these methods, they require large memory and high pre-training costs, making them inaccessible for researchers in academia and small companies. Therefore, this paper introduces DistilHuBERT, a novel multi-task learning framework to distill hidden representations from a HuBERT model directly. This method reduces HuBERT's size by 75% and 73% faster while retaining most performance in ten different tasks. Moreover, DistilHuBERT required little training time and data, opening the possibilities of pre-training personal and on-device SSL models for speech.
# Usage
See [this blog](https://huggingface.co/blog/fine-tune-wav2vec2-english) for more information on how to fine-tune the model.
|
Ketzu/koelectra-sts-v0.4 | Ketzu | 2021-12-29T23:31:59Z | 15 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"electra",
"text-classification",
"generated_from_trainer",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-02T23:29:04Z | ---
tags:
- generated_from_trainer
metrics:
- spearmanr
model-index:
- name: koelectra-sts-v0.4
results:
- task:
name: Text Classification
type: text-classification
metrics:
- name: Spearmanr
type: spearmanr
value: 0.9286505242442783
---
<!-- 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. -->
# koelectra-sts-v0.4
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3368
- Pearson: 0.9303
- Spearmanr: 0.9287
## 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 | Pearson | Spearmanr |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:|
| 0.0345 | 1.0 | 730 | 0.3368 | 0.9303 | 0.9287 |
| 0.0343 | 2.0 | 1460 | 0.3368 | 0.9303 | 0.9287 |
| 0.0337 | 3.0 | 2190 | 0.3368 | 0.9303 | 0.9287 |
| 0.0345 | 4.0 | 2920 | 0.3368 | 0.9303 | 0.9287 |
| 0.0347 | 5.0 | 3650 | 0.3368 | 0.9303 | 0.9287 |
### Framework versions
- Transformers 4.10.0
- Pytorch 1.10.1+cu113
- Datasets 1.17.0
- Tokenizers 0.10.3
|
BigSalmon/InformalToFormalLincoln17 | BigSalmon | 2021-12-29T21:25:31Z | 10 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:04Z | Informal to Formal:
```
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln17")
model = AutoModelWithLMHead.from_pretrained("BigSalmon/InformalToFormalLincoln17")
```
```
https://huggingface.co/spaces/BigSalmon/GPT2 (The model for this space changes over time)
```
```
https://huggingface.co/spaces/BigSalmon/GPT2_Most_Probable (The model for this space changes over time)
```
```
How To Make Prompt:
informal english: i am very ready to do that just that.
Translated into the Style of Abraham Lincoln: you can assure yourself of my readiness to work toward this end.
Translated into the Style of Abraham Lincoln: please be assured that i am most ready to undertake this laborious task.
informal english: space is huge and needs to be explored.
Translated into the Style of Abraham Lincoln: space awaits traversal, a new world whose boundaries are endless.
Translated into the Style of Abraham Lincoln: space is a ( limitless / boundless ) expanse, a vast virgin domain awaiting exploration.
informal english: corn fields are all across illinois, visible once you leave chicago.
Translated into the Style of Abraham Lincoln: corn fields ( permeate illinois / span the state of illinois / ( occupy / persist in ) all corners of illinois / line the horizon of illinois / envelop the landscape of illinois ), manifesting themselves visibly as one ventures beyond chicago.
informal english:
```` |
pierreguillou/ner-bert-large-cased-pt-lenerbr | pierreguillou | 2021-12-29T19:33:17Z | 165 | 20 | transformers | [
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"pt",
"dataset:lener_br",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2022-03-02T23:29:05Z | ---
language:
- pt
tags:
- generated_from_trainer
datasets:
- lener_br
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: checkpoints
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: lener_br
type: lener_br
metrics:
- name: F1
type: f1
value: 0.9082022949426265
- name: Precision
type: precision
value: 0.8975220495590088
- name: Recall
type: recall
value: 0.9191397849462366
- name: Accuracy
type: accuracy
value: 0.9808310603867311
- name: Loss
type: loss
value: 0.1228889599442482
widget:
- text: "Ao Instituto Médico Legal da jurisdição do acidente ou da residência cumpre fornecer, no prazo de 90 dias, laudo à vítima (art. 5, § 5, Lei n. 6.194/74 de 19 de dezembro de 1974), função técnica que pode ser suprida por prova pericial realizada por ordem do juízo da causa, ou por prova técnica realizada no âmbito administrativo que se mostre coerente com os demais elementos de prova constante dos autos."
- text: "Acrescento que não há de se falar em violação do artigo 114, § 3º, da Constituição Federal, posto que referido dispositivo revela-se impertinente, tratando da possibilidade de ajuizamento de dissídio coletivo pelo Ministério Público do Trabalho nos casos de greve em atividade essencial."
- text: "Todavia, entendo que extrair da aludida norma o sentido expresso na redação acima implica desconstruir o significado do texto constitucional, o que é absolutamente vedado ao intérprete. Nesse sentido, cito Dimitri Dimoulis: ‘(...) ao intérprete não é dado escolher significados que não estejam abarcados pela moldura da norma. Interpretar não pode significar violentar a norma.’ (Positivismo Jurídico. São Paulo: Método, 2006, p. 220).59. Dessa forma, deve-se tomar o sentido etimológico como limite da atividade interpretativa, a qual não pode superado, a ponto de destruir a própria norma a ser interpretada. Ou, como diz Konrad Hesse, ‘o texto da norma é o limite insuperável da atividade interpretativa.’ (Elementos de Direito Constitucional da República Federal da Alemanha, Porto Alegre: Sergio Antonio Fabris, 2003, p. 71)."
---
## (BERT large) NER model in the legal domain in Portuguese (LeNER-Br)
**ner-bert-large-portuguese-cased-lenerbr** is a NER model (token classification) in the legal domain in Portuguese that was finetuned on 20/12/2021 in Google Colab from the model [pierreguillou/bert-large-cased-pt-lenerbr](https://huggingface.co/pierreguillou/bert-large-cased-pt-lenerbr) on the dataset [LeNER_br](https://huggingface.co/datasets/lener_br) by using a NER objective.
Due to the small size of the finetuning dataset, the model overfitted before to reach the end of training. Here are the overall final metrics on the validation dataset (*note: see the paragraph "Validation metrics by Named Entity" to get detailed metrics*):
- **f1**: 0.9082022949426265
- **precision**: 0.8975220495590088
- **recall**: 0.9191397849462366
- **accuracy**: 0.9808310603867311
- **loss**: 0.1228889599442482
Check as well the [base version of this model](https://huggingface.co/pierreguillou/ner-bert-base-cased-pt-lenerbr) with a f1 of 0.893.
**Note**: the model [pierreguillou/bert-large-cased-pt-lenerbr](https://huggingface.co/pierreguillou/bert-large-cased-pt-lenerbr) is a language model that was created through the finetuning of the model [BERTimbau large](https://huggingface.co/neuralmind/bert-large-portuguese-cased) on the dataset [LeNER-Br language modeling](https://huggingface.co/datasets/pierreguillou/lener_br_finetuning_language_model) by using a MASK objective. This first specialization of the language model before finetuning on the NER task allows to get a better NER model.
## Blog post
[NLP | Modelos e Web App para Reconhecimento de Entidade Nomeada (NER) no domínio jurídico brasileiro](https://medium.com/@pierre_guillou/nlp-modelos-e-web-app-para-reconhecimento-de-entidade-nomeada-ner-no-dom%C3%ADnio-jur%C3%ADdico-b658db55edfb) (29/12/2021)
## Widget & App
You can test this model into the widget of this page.
Use as well the [NER App](https://huggingface.co/spaces/pierreguillou/ner-bert-pt-lenerbr) that allows comparing the 2 BERT models (base and large) fitted in the NER task with the legal LeNER-Br dataset.
## Using the model for inference in production
````
# install pytorch: check https://pytorch.org/
# !pip install transformers
from transformers import AutoModelForTokenClassification, AutoTokenizer
import torch
# parameters
model_name = "pierreguillou/ner-bert-large-cased-pt-lenerbr"
model = AutoModelForTokenClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
input_text = "Acrescento que não há de se falar em violação do artigo 114, § 3º, da Constituição Federal, posto que referido dispositivo revela-se impertinente, tratando da possibilidade de ajuizamento de dissídio coletivo pelo Ministério Público do Trabalho nos casos de greve em atividade essencial."
# tokenization
inputs = tokenizer(input_text, max_length=512, truncation=True, return_tensors="pt")
tokens = inputs.tokens()
# get predictions
outputs = model(**inputs).logits
predictions = torch.argmax(outputs, dim=2)
# print predictions
for token, prediction in zip(tokens, predictions[0].numpy()):
print((token, model.config.id2label[prediction]))
````
You can use pipeline, too. However, it seems to have an issue regarding to the max_length of the input sequence.
````
!pip install transformers
import transformers
from transformers import pipeline
model_name = "pierreguillou/ner-bert-large-cased-pt-lenerbr"
ner = pipeline(
"ner",
model=model_name
)
ner(input_text)
````
## Training procedure
### Notebook
The notebook of finetuning ([HuggingFace_Notebook_token_classification_NER_LeNER_Br.ipynb](https://github.com/piegu/language-models/blob/master/HuggingFace_Notebook_token_classification_NER_LeNER_Br.ipynb)) is in github.
### Hyperparameters
# batch, learning rate...
- per_device_batch_size = 2
- gradient_accumulation_steps = 2
- learning_rate = 2e-5
- num_train_epochs = 10
- weight_decay = 0.01
- optimizer = AdamW
- betas = (0.9,0.999)
- epsilon = 1e-08
- lr_scheduler_type = linear
- seed = 42
# save model & load best model
- save_total_limit = 7
- logging_steps = 500
- eval_steps = logging_steps
- evaluation_strategy = 'steps'
- logging_strategy = 'steps'
- save_strategy = 'steps'
- save_steps = logging_steps
- load_best_model_at_end = True
- fp16 = True
# get best model through a metric
- metric_for_best_model = 'eval_f1'
- greater_is_better = True
### Training results
````
Num examples = 7828
Num Epochs = 20
Instantaneous batch size per device = 2
Total train batch size (w. parallel, distributed & accumulation) = 4
Gradient Accumulation steps = 2
Total optimization steps = 39140
Step Training Loss Validation Loss Precision Recall F1 Accuracy
500 0.250000 0.140582 0.760833 0.770323 0.765548 0.963125
1000 0.076200 0.117882 0.829082 0.817849 0.823428 0.966569
1500 0.082400 0.150047 0.679610 0.914624 0.779795 0.957213
2000 0.047500 0.133443 0.817678 0.857419 0.837077 0.969190
2500 0.034200 0.230139 0.895672 0.845591 0.869912 0.964070
3000 0.033800 0.108022 0.859225 0.887312 0.873043 0.973700
3500 0.030100 0.113467 0.855747 0.885376 0.870310 0.975879
4000 0.029900 0.118619 0.850207 0.884946 0.867229 0.974477
4500 0.022500 0.124327 0.841048 0.890968 0.865288 0.975041
5000 0.020200 0.129294 0.801538 0.918925 0.856227 0.968077
5500 0.019700 0.128344 0.814222 0.908602 0.858827 0.969250
6000 0.024600 0.182563 0.908087 0.866882 0.887006 0.968565
6500 0.012600 0.159217 0.829883 0.913763 0.869806 0.969357
7000 0.020600 0.183726 0.854557 0.893333 0.873515 0.966447
7500 0.014400 0.141395 0.777716 0.905161 0.836613 0.966828
8000 0.013400 0.139378 0.873042 0.899140 0.885899 0.975772
8500 0.014700 0.142521 0.864152 0.901505 0.882433 0.976366
9000 0.010900 0.122889 0.897522 0.919140 0.908202 0.980831
9500 0.013500 0.143407 0.816580 0.906667 0.859268 0.973395
10000 0.010400 0.144946 0.835608 0.908387 0.870479 0.974629
10500 0.007800 0.143086 0.847587 0.910108 0.877735 0.975985
11000 0.008200 0.156379 0.873778 0.884301 0.879008 0.976321
11500 0.008200 0.133356 0.901193 0.910108 0.905628 0.980328
12000 0.006900 0.133476 0.892202 0.920215 0.905992 0.980572
12500 0.006900 0.129991 0.890159 0.904516 0.897280 0.978683
````
### Validation metrics by Named Entity
````
{'JURISPRUDENCIA': {'f1': 0.8135593220338984,
'number': 657,
'precision': 0.865979381443299,
'recall': 0.7671232876712328},
'LEGISLACAO': {'f1': 0.8888888888888888,
'number': 571,
'precision': 0.8952042628774423,
'recall': 0.882661996497373},
'LOCAL': {'f1': 0.850467289719626,
'number': 194,
'precision': 0.7777777777777778,
'recall': 0.9381443298969072},
'ORGANIZACAO': {'f1': 0.8740635033892258,
'number': 1340,
'precision': 0.8373205741626795,
'recall': 0.914179104477612},
'PESSOA': {'f1': 0.9836677554829678,
'number': 1072,
'precision': 0.9841269841269841,
'recall': 0.9832089552238806},
'TEMPO': {'f1': 0.9669669669669669,
'number': 816,
'precision': 0.9481743227326266,
'recall': 0.9865196078431373},
'overall_accuracy': 0.9808310603867311,
'overall_f1': 0.9082022949426265,
'overall_precision': 0.8975220495590088,
'overall_recall': 0.9191397849462366}
```` |
pierreguillou/ner-bert-base-cased-pt-lenerbr | pierreguillou | 2021-12-29T19:32:39Z | 108,865 | 15 | transformers | [
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"pt",
"dataset:lener_br",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2022-03-02T23:29:05Z | ---
language:
- pt
tags:
- generated_from_trainer
datasets:
- lener_br
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: checkpoints
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: lener_br
type: lener_br
metrics:
- name: F1
type: f1
value: 0.8926146010186757
- name: Precision
type: precision
value: 0.8810222036028488
- name: Recall
type: recall
value: 0.9045161290322581
- name: Accuracy
type: accuracy
value: 0.9759397808828684
- name: Loss
type: loss
value: 0.18803243339061737
widget:
- text: "Ao Instituto Médico Legal da jurisdição do acidente ou da residência cumpre fornecer, no prazo de 90 dias, laudo à vítima (art. 5, § 5, Lei n. 6.194/74 de 19 de dezembro de 1974), função técnica que pode ser suprida por prova pericial realizada por ordem do juízo da causa, ou por prova técnica realizada no âmbito administrativo que se mostre coerente com os demais elementos de prova constante dos autos."
- text: "Acrescento que não há de se falar em violação do artigo 114, § 3º, da Constituição Federal, posto que referido dispositivo revela-se impertinente, tratando da possibilidade de ajuizamento de dissídio coletivo pelo Ministério Público do Trabalho nos casos de greve em atividade essencial."
- text: "Dispõe sobre o estágio de estudantes; altera a redação do art. 428 da Consolidação das Leis do Trabalho – CLT, aprovada pelo Decreto-Lei no 5.452, de 1o de maio de 1943, e a Lei no 9.394, de 20 de dezembro de 1996; revoga as Leis nos 6.494, de 7 de dezembro de 1977, e 8.859, de 23 de março de 1994, o parágrafo único do art. 82 da Lei no 9.394, de 20 de dezembro de 1996, e o art. 6o da Medida Provisória no 2.164-41, de 24 de agosto de 2001; e dá outras providências."
---
## (BERT base) NER model in the legal domain in Portuguese (LeNER-Br)
**ner-bert-base-portuguese-cased-lenerbr** is a NER model (token classification) in the legal domain in Portuguese that was finetuned on 20/12/2021 in Google Colab from the model [pierreguillou/bert-base-cased-pt-lenerbr](https://huggingface.co/pierreguillou/bert-base-cased-pt-lenerbr) on the dataset [LeNER_br](https://huggingface.co/datasets/lener_br) by using a NER objective.
Due to the small size of BERTimbau base and finetuning dataset, the model overfitted before to reach the end of training. Here are the overall final metrics on the validation dataset (*note: see the paragraph "Validation metrics by Named Entity" to get detailed metrics*):
- **f1**: 0.8926146010186757
- **precision**: 0.8810222036028488
- **recall**: 0.9045161290322581
- **accuracy**: 0.9759397808828684
- **loss**: 0.18803243339061737
Check as well the [large version of this model](https://huggingface.co/pierreguillou/ner-bert-large-cased-pt-lenerbr) with a f1 of 0.908.
**Note**: the model [pierreguillou/bert-base-cased-pt-lenerbr](https://huggingface.co/pierreguillou/bert-base-cased-pt-lenerbr) is a language model that was created through the finetuning of the model [BERTimbau base](https://huggingface.co/neuralmind/bert-base-portuguese-cased) on the dataset [LeNER-Br language modeling](https://huggingface.co/datasets/pierreguillou/lener_br_finetuning_language_model) by using a MASK objective. This first specialization of the language model before finetuning on the NER task improved a bit the model quality. To prove it, here are the results of the NER model finetuned from the model [BERTimbau base](https://huggingface.co/neuralmind/bert-base-portuguese-cased) (a non-specialized language model):
- **f1**: 0.8716487228203504
- **precision**: 0.8559286898839138
- **recall**: 0.8879569892473118
- **accuracy**: 0.9755893153732458
- **loss**: 0.1133928969502449
## Blog post
[NLP | Modelos e Web App para Reconhecimento de Entidade Nomeada (NER) no domínio jurídico brasileiro](https://medium.com/@pierre_guillou/nlp-modelos-e-web-app-para-reconhecimento-de-entidade-nomeada-ner-no-dom%C3%ADnio-jur%C3%ADdico-b658db55edfb) (29/12/2021)
## Widget & App
You can test this model into the widget of this page.
Use as well the [NER App](https://huggingface.co/spaces/pierreguillou/ner-bert-pt-lenerbr) that allows comparing the 2 BERT models (base and large) fitted in the NER task with the legal LeNER-Br dataset.
## Using the model for inference in production
````
# install pytorch: check https://pytorch.org/
# !pip install transformers
from transformers import AutoModelForTokenClassification, AutoTokenizer
import torch
# parameters
model_name = "pierreguillou/ner-bert-base-cased-pt-lenerbr"
model = AutoModelForTokenClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
input_text = "Acrescento que não há de se falar em violação do artigo 114, § 3º, da Constituição Federal, posto que referido dispositivo revela-se impertinente, tratando da possibilidade de ajuizamento de dissídio coletivo pelo Ministério Público do Trabalho nos casos de greve em atividade essencial."
# tokenization
inputs = tokenizer(input_text, max_length=512, truncation=True, return_tensors="pt")
tokens = inputs.tokens()
# get predictions
outputs = model(**inputs).logits
predictions = torch.argmax(outputs, dim=2)
# print predictions
for token, prediction in zip(tokens, predictions[0].numpy()):
print((token, model.config.id2label[prediction]))
````
You can use pipeline, too. However, it seems to have an issue regarding to the max_length of the input sequence.
````
!pip install transformers
import transformers
from transformers import pipeline
model_name = "pierreguillou/ner-bert-base-cased-pt-lenerbr"
ner = pipeline(
"ner",
model=model_name
)
ner(input_text)
````
## Training procedure
### Notebook
The notebook of finetuning ([HuggingFace_Notebook_token_classification_NER_LeNER_Br.ipynb](https://github.com/piegu/language-models/blob/master/HuggingFace_Notebook_token_classification_NER_LeNER_Br.ipynb)) is in github.
### Hyperparameters
#### batch, learning rate...
- per_device_batch_size = 2
- gradient_accumulation_steps = 2
- learning_rate = 2e-5
- num_train_epochs = 10
- weight_decay = 0.01
- optimizer = AdamW
- betas = (0.9,0.999)
- epsilon = 1e-08
- lr_scheduler_type = linear
- seed = 7
#### save model & load best model
- save_total_limit = 2
- logging_steps = 300
- eval_steps = logging_steps
- evaluation_strategy = 'steps'
- logging_strategy = 'steps'
- save_strategy = 'steps'
- save_steps = logging_steps
- load_best_model_at_end = True
- fp16 = True
#### get best model through a metric
- metric_for_best_model = 'eval_f1'
- greater_is_better = True
### Training results
````
Num examples = 7828
Num Epochs = 10
Instantaneous batch size per device = 2
Total train batch size (w. parallel, distributed & accumulation) = 4
Gradient Accumulation steps = 2
Total optimization steps = 19570
Step Training Loss Validation Loss Precision Recall F1 Accuracy
300 0.127600 0.178613 0.722909 0.741720 0.732194 0.948802
600 0.088200 0.136965 0.733636 0.867742 0.795074 0.963079
900 0.078000 0.128858 0.791912 0.838065 0.814335 0.965243
1200 0.077800 0.126345 0.815400 0.865376 0.839645 0.967849
1500 0.074100 0.148207 0.779274 0.895914 0.833533 0.960184
1800 0.059500 0.116634 0.830829 0.868172 0.849090 0.969342
2100 0.044500 0.208459 0.887150 0.816559 0.850392 0.960535
2400 0.029400 0.136352 0.867821 0.851398 0.859531 0.970271
2700 0.025000 0.165837 0.814881 0.878495 0.845493 0.961235
3000 0.038400 0.120629 0.811719 0.893763 0.850768 0.971506
3300 0.026200 0.175094 0.823435 0.882581 0.851983 0.962957
3600 0.025600 0.178438 0.881095 0.886022 0.883551 0.963689
3900 0.041000 0.134648 0.789035 0.916129 0.847846 0.967681
4200 0.026700 0.130178 0.821275 0.903226 0.860303 0.972313
4500 0.018500 0.139294 0.844016 0.875054 0.859255 0.971140
4800 0.020800 0.197811 0.892504 0.873118 0.882705 0.965883
5100 0.019300 0.161239 0.848746 0.888172 0.868012 0.967849
5400 0.024000 0.139131 0.837507 0.913333 0.873778 0.970591
5700 0.018400 0.157223 0.899754 0.864731 0.881895 0.970210
6000 0.023500 0.137022 0.883018 0.873333 0.878149 0.973243
6300 0.009300 0.181448 0.840490 0.900860 0.869628 0.968290
6600 0.019200 0.173125 0.821316 0.896559 0.857290 0.966736
6900 0.016100 0.143160 0.789938 0.904946 0.843540 0.968245
7200 0.017000 0.145755 0.823274 0.897634 0.858848 0.969037
7500 0.012100 0.159342 0.825694 0.883226 0.853491 0.967468
7800 0.013800 0.194886 0.861237 0.859570 0.860403 0.964771
8100 0.008000 0.140271 0.829914 0.896129 0.861752 0.971567
8400 0.010300 0.143318 0.826844 0.908817 0.865895 0.973578
8700 0.015000 0.143392 0.847336 0.889247 0.867786 0.973365
9000 0.006000 0.143512 0.847795 0.905591 0.875741 0.972892
9300 0.011800 0.138747 0.827133 0.894194 0.859357 0.971673
9600 0.008500 0.159490 0.837030 0.909032 0.871546 0.970028
9900 0.010700 0.159249 0.846692 0.910968 0.877655 0.970546
10200 0.008100 0.170069 0.848288 0.900645 0.873683 0.969113
10500 0.004800 0.183795 0.860317 0.899355 0.879403 0.969570
10800 0.010700 0.157024 0.837838 0.906667 0.870894 0.971094
11100 0.003800 0.164286 0.845312 0.880215 0.862410 0.970744
11400 0.009700 0.204025 0.884294 0.887527 0.885907 0.968854
11700 0.008900 0.162819 0.829415 0.887742 0.857588 0.970530
12000 0.006400 0.164296 0.852666 0.901075 0.876202 0.971414
12300 0.007100 0.143367 0.852959 0.895699 0.873807 0.973669
12600 0.015800 0.153383 0.859224 0.900430 0.879345 0.972679
12900 0.006600 0.173447 0.869954 0.899140 0.884306 0.970927
13200 0.006800 0.163234 0.856849 0.897204 0.876563 0.971795
13500 0.003200 0.167164 0.850867 0.907957 0.878485 0.971231
13800 0.003600 0.148950 0.867801 0.910538 0.888656 0.976961
14100 0.003500 0.155691 0.847621 0.907957 0.876752 0.974127
14400 0.003300 0.157672 0.846553 0.911183 0.877680 0.974584
14700 0.002500 0.169965 0.847804 0.917634 0.881338 0.973045
15000 0.003400 0.177099 0.842199 0.912473 0.875929 0.971155
15300 0.006000 0.164151 0.848928 0.911183 0.878954 0.973258
15600 0.002400 0.174305 0.847437 0.906667 0.876052 0.971765
15900 0.004100 0.174561 0.852929 0.907957 0.879583 0.972907
16200 0.002600 0.172626 0.843263 0.907097 0.874016 0.972100
16500 0.002100 0.185302 0.841108 0.907312 0.872957 0.970485
16800 0.002900 0.175638 0.840557 0.909247 0.873554 0.971704
17100 0.001600 0.178750 0.857056 0.906452 0.881062 0.971765
17400 0.003900 0.188910 0.853619 0.907957 0.879950 0.970835
17700 0.002700 0.180822 0.864699 0.907097 0.885390 0.972283
18000 0.001300 0.179974 0.868150 0.906237 0.886785 0.973060
18300 0.000800 0.188032 0.881022 0.904516 0.892615 0.972572
18600 0.002700 0.183266 0.868601 0.901290 0.884644 0.972298
18900 0.001600 0.180301 0.862041 0.903011 0.882050 0.972344
19200 0.002300 0.183432 0.855370 0.904301 0.879155 0.971109
19500 0.001800 0.183381 0.854501 0.904301 0.878696 0.971186
````
### Validation metrics by Named Entity
````
Num examples = 1177
{'JURISPRUDENCIA': {'f1': 0.7016574585635359,
'number': 657,
'precision': 0.6422250316055625,
'recall': 0.7732115677321156},
'LEGISLACAO': {'f1': 0.8839681133746677,
'number': 571,
'precision': 0.8942652329749103,
'recall': 0.8739054290718039},
'LOCAL': {'f1': 0.8253968253968254,
'number': 194,
'precision': 0.7368421052631579,
'recall': 0.9381443298969072},
'ORGANIZACAO': {'f1': 0.8934049079754601,
'number': 1340,
'precision': 0.918769716088328,
'recall': 0.8694029850746269},
'PESSOA': {'f1': 0.982653539615565,
'number': 1072,
'precision': 0.9877474081055608,
'recall': 0.9776119402985075},
'TEMPO': {'f1': 0.9657657657657657,
'number': 816,
'precision': 0.9469964664310954,
'recall': 0.9852941176470589},
'overall_accuracy': 0.9725722644643211,
'overall_f1': 0.8926146010186757,
'overall_precision': 0.8810222036028488,
'overall_recall': 0.9045161290322581}
```` |
tbochens/test-train | tbochens | 2021-12-29T19:25:46Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-02T23:29:05Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: test-train
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: mrpc
metrics:
- name: Accuracy
type: accuracy
value: 0.8455882352941176
- name: F1
type: f1
value: 0.8926746166950595
---
<!-- 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-train
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7268
- Accuracy: 0.8456
- F1: 0.8927
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 459 | 0.3470 | 0.8627 | 0.9014 |
| 0.4987 | 2.0 | 918 | 0.5782 | 0.8382 | 0.8914 |
| 0.2796 | 3.0 | 1377 | 0.7268 | 0.8456 | 0.8927 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu102
- Datasets 1.17.0
- Tokenizers 0.10.3
|
patrickvonplaten/wav2vec2-2-bart-base | patrickvonplaten | 2021-12-29T15:53:10Z | 373 | 4 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"speech-encoder-decoder",
"automatic-speech-recognition",
"librispeech_asr",
"generated_from_trainer",
"asr_seq2esq",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2022-03-02T23:29:05Z | ---
tags:
- automatic-speech-recognition
- librispeech_asr
- generated_from_trainer
- asr_seq2esq
model-index:
- name: wav2vec2-2-bart-base
results: []
widget:
- example_title: Librispeech sample 1
src: https://cdn-media.huggingface.co/speech_samples/sample1.flac
- example_title: Librispeech sample 2
src: https://cdn-media.huggingface.co/speech_samples/sample2.flac
- example_title: Common Voice sample
src: https://cdn-media.huggingface.co/speech_samples/common_voice_en_18301577.mp3
---
To rerun this experiment, please clone this directory and run:
```bash
python create_model.py
```
followed by
```bash
./run_librispeech.sh
```
<!-- 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. -->
# wav2vec2-2-bart-base
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) and [bart-base](https://huggingface.co/facebook/bart-base) on the librispeech_asr - clean dataset.
It achieves the following results on the evaluation set:
- Loss: 0.405
- Wer: 0.0728
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 64
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 400
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
See Training Metrics Tab.
### Framework versions
- Transformers 4.15.0.dev0
- Pytorch 1.9.0+cu111
- Datasets 1.16.2.dev0
- Tokenizers 0.10.3
|
patrickvonplaten/wav2vec2-2-bart-large | patrickvonplaten | 2021-12-29T15:49:52Z | 6 | 5 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"speech-encoder-decoder",
"automatic-speech-recognition",
"librispeech_asr",
"generated_from_trainer",
"asr_seq2esq",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2022-03-02T23:29:05Z | ---
tags:
- automatic-speech-recognition
- librispeech_asr
- generated_from_trainer
- asr_seq2esq
widget:
- example_title: Librispeech sample 1
src: https://cdn-media.huggingface.co/speech_samples/sample1.flac
- example_title: Librispeech sample 2
src: https://cdn-media.huggingface.co/speech_samples/sample2.flac
- example_title: Common Voice sample
src: https://cdn-media.huggingface.co/speech_samples/common_voice_en_18301577.mp3
model-index:
- name: wav2vec2-2-bart-large
results: []
---
To rerun this experiment, please clone this directory and run:
```bash
python create_model.py
```
followed by
```bash
./run_librispeech.sh
```
<!-- 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. -->
# wav2vec2-2-bart-large
This model is a fine-tuned version of [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60) and [bart-large](https://huggingface.co/facebook/bart-large) on the librispeech_asr - clean dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3204
- Wer: 0.0486
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 4
- eval_batch_size: 4
- gradient_accumulation_steps: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 64
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
See Training Metrics Tab.
### Framework versions
- Transformers 4.15.0.dev0
- Pytorch 1.9.0+cu111
- Datasets 1.16.2.dev0
- Tokenizers 0.10.3 |
rexxar96/autonlp-sentiment-analysis-456211724 | rexxar96 | 2021-12-29T14:47:09Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autonlp",
"unk",
"dataset:rexxar96/autonlp-data-sentiment-analysis",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-02T23:29:05Z | ---
tags: autonlp
language: unk
widget:
- text: "I love AutoNLP 🤗"
datasets:
- rexxar96/autonlp-data-sentiment-analysis
co2_eq_emissions: 22.28263989637389
---
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 456211724
- CO2 Emissions (in grams): 22.28263989637389
## Validation Metrics
- Loss: 0.23710417747497559
- Accuracy: 0.9119100357812234
- Precision: 0.8882611424984307
- Recall: 0.9461718488799733
- AUC: 0.974790366001874
- F1: 0.9163024121741946
## 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 AutoNLP"}' https://api-inference.huggingface.co/models/rexxar96/autonlp-sentiment-analysis-456211724
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("rexxar96/autonlp-sentiment-analysis-456211724", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("rexxar96/autonlp-sentiment-analysis-456211724", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
``` |
ydshieh/flax-vision-encoder-decoder-vit-gpt2-coco-en | ydshieh | 2021-12-29T10:12:05Z | 0 | 0 | null | [
"region:us"
] | null | 2022-03-02T23:29:05Z | ## Example
The model is by no means a state-of-the-art model, but nevertheless
produces reasonable image captioning results. It was mainly fine-tuned
as a proof-of-concept for the 🤗 FlaxVisionEncoderDecoder Framework.
The model can be used as follows:
```python
import requests
from PIL import Image
from transformers import ViTFeatureExtractor, AutoTokenizer, FlaxVisionEncoderDecoderModel
loc = "ydshieh/flax-vit-gpt2-coco-en"
feature_extractor = ViTFeatureExtractor.from_pretrained(loc)
tokenizer = AutoTokenizer.from_pretrained(loc)
model = FlaxVisionEncoderDecoderModel.from_pretrained(loc)
# We will verify our results on an image of cute cats
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
with Image.open(requests.get(url, stream=True).raw) as img:
pixel_values = feature_extractor(images=img, return_tensors="np").pixel_values
def generate_step(pixel_values):
output_ids = model.generate(pixel_values, max_length=16, num_beams=4).sequences
preds = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
preds = [pred.strip() for pred in preds]
return preds
preds = generate_step(pixel_values)
print(preds)
# should produce
# ['a cat laying on top of a couch next to another cat']
``` |
SophieTr/results | SophieTr | 2021-12-28T19:59:38Z | 14 | 2 | transformers | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-03-02T23:29:05Z | ---
tags:
- generated_from_trainer
model-index:
- name: results
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. -->
# results
This model is a fine-tuned version of [sshleifer/distill-pegasus-xsum-16-4](https://huggingface.co/sshleifer/distill-pegasus-xsum-16-4) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4473
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 7.2378 | 0.51 | 100 | 7.1853 |
| 7.2309 | 1.01 | 200 | 6.6342 |
| 6.4796 | 1.52 | 300 | 6.3206 |
| 6.2691 | 2.02 | 400 | 6.0184 |
| 5.7382 | 2.53 | 500 | 5.5754 |
| 4.9922 | 3.03 | 600 | 4.5178 |
| 3.6031 | 3.54 | 700 | 2.8579 |
| 2.5203 | 4.04 | 800 | 2.4718 |
| 2.2563 | 4.55 | 900 | 2.4128 |
| 2.1425 | 5.05 | 1000 | 2.3767 |
| 2.004 | 5.56 | 1100 | 2.3982 |
| 2.0437 | 6.06 | 1200 | 2.3787 |
| 1.9407 | 6.57 | 1300 | 2.3952 |
| 1.9194 | 7.07 | 1400 | 2.3964 |
| 1.758 | 7.58 | 1500 | 2.4056 |
| 1.918 | 8.08 | 1600 | 2.4101 |
| 1.9162 | 8.59 | 1700 | 2.4085 |
| 1.8983 | 9.09 | 1800 | 2.4058 |
| 1.6939 | 9.6 | 1900 | 2.4050 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.10.0+cu111
- Datasets 1.15.1
- Tokenizers 0.10.3
|
espnet/siddhana_slue_asr_train_asr_conformer_raw_en_word_valid.acc.ave_10best | espnet | 2021-12-28T18:57:57Z | 0 | 0 | espnet | [
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"dataset:slue-voxceleb",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | automatic-speech-recognition | 2022-03-02T23:29:05Z | ---
tags:
- espnet
- audio
- automatic-speech-recognition
language: en
datasets:
- slue-voxceleb
license: cc-by-4.0
---
## ESPnet2 ASR model
### `espnet/siddhana_slue_asr_train_asr_conformer_raw_en_word_valid.acc.ave_10best`
This model was trained by Siddhant using slue-voxceleb recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 17758ad804fd7c4b6f88ef5601f475a241dc4605
pip install -e .
cd egs2/slue-voxceleb/asr1
./run.sh --skip_data_prep false --skip_train true --download_model espnet/siddhana_slue_asr_train_asr_conformer_raw_en_word_valid.acc.ave_10best
```
<!-- Generated by scripts/utils/show_asr_result.sh -->
# RESULTS
## Environments
- date: `Tue Dec 28 12:28:28 EST 2021`
- python version: `3.9.5 (default, Jun 4 2021, 12:28:51) [GCC 7.5.0]`
- espnet version: `espnet 0.10.3a2`
- pytorch version: `pytorch 1.8.1+cu102`
- Git hash: `6bf3c2a4f138d35331634d2e879bbc5c32a5266e`
- Commit date: `Mon Dec 22 15:41:32 EST 2021`
## Using Conformer based encoder and Transformer based decoder with spectral augmentation and predicting transcript along with intent
- ASR config: [conf/train_asr.yaml](conf/tuning/train_asr_conformer.yaml)
- token_type: word
|dataset|Snt|Intent Classification Accuracy (%)|Intent Classification Macro F1 (%)|
|---|---|---|---|
|inference_asr_model_valid.acc.ave_10best/devel|955|80.2|29.7|
### Detailed Classification Report
|dataset|Label|Snt|Prec|Recall|F1|
|---|---|---|---|---|---|
|inference_asr_model_valid.acc.ave_10best/devel|Neutral|784|85|93|89|
|inference_asr_model_valid.acc.ave_10best/devel|Positive|167|40|24|30|
|inference_asr_model_valid.acc.ave_10best/devel|Negative|3|0|0|0|
|inference_asr_model_valid.acc.ave_10best/devel|Mixed|1|0|0|0|
## ASR config
<details><summary>expand</summary>
```
config: conf/tuning/train_asr_conformer.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/asr_train_asr_conformer_raw_en_word
ngpu: 1
seed: 0
num_workers: 1
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: null
dist_rank: null
local_rank: 0
dist_master_addr: null
dist_master_port: null
dist_launcher: null
multiprocessing_distributed: false
unused_parameters: false
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
collect_stats: false
write_collected_feats: false
max_epoch: 50
patience: null
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - valid
- acc
- max
keep_nbest_models: 10
grad_clip: 5.0
grad_clip_type: 2.0
grad_noise: false
accum_grad: 1
no_forward_run: false
resume: true
train_dtype: float32
use_amp: false
log_interval: null
use_tensorboard: true
use_wandb: false
wandb_project: null
wandb_id: null
wandb_entity: null
wandb_name: null
wandb_model_log_interval: -1
detect_anomaly: false
pretrain_path: null
init_param: []
ignore_init_mismatch: false
freeze_param: []
num_iters_per_epoch: null
batch_size: 20
valid_batch_size: null
batch_bins: 1000000
valid_batch_bins: null
train_shape_file:
- exp/asr_stats_raw_en_word/train/speech_shape
- exp/asr_stats_raw_en_word/train/text_shape.word
valid_shape_file:
- exp/asr_stats_raw_en_word/valid/speech_shape
- exp/asr_stats_raw_en_word/valid/text_shape.word
batch_type: folded
valid_batch_type: null
fold_length:
- 80000
- 150
sort_in_batch: descending
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
train_data_path_and_name_and_type:
- - dump/raw/train/wav.scp
- speech
- sound
- - dump/raw/train/text
- text
- text
valid_data_path_and_name_and_type:
- - dump/raw/devel/wav.scp
- speech
- sound
- - dump/raw/devel/text
- text
- text
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
optim: adam
optim_conf:
lr: 0.0002
scheduler: warmuplr
scheduler_conf:
warmup_steps: 25000
token_list:
- <blank>
- <unk>
- ▁i
- s
- ▁and
- ''''
- ▁the
- ▁a
- ▁to
- ▁it
- Neutral
- ▁you
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- ▁of
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- ing
- ▁in
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- ed
- ▁uh
- ▁know
- e
- m
- ▁he
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- er
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- ▁said
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- ot
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- qu
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- q
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- ways
- ':'
- '&'
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transcript_token_list: null
init: null
input_size: null
ctc_conf:
dropout_rate: 0.0
ctc_type: builtin
reduce: true
ignore_nan_grad: true
model_conf:
ctc_weight: 0.3
lsm_weight: 0.1
length_normalized_loss: false
extract_feats_in_collect_stats: false
use_preprocessor: true
token_type: word
bpemodel: null
non_linguistic_symbols: null
cleaner: null
g2p: null
speech_volume_normalize: null
rir_scp: null
rir_apply_prob: 1.0
noise_scp: null
noise_apply_prob: 1.0
noise_db_range: '13_15'
frontend: default
frontend_conf:
fs: 16k
specaug: specaug
specaug_conf:
apply_time_warp: true
time_warp_window: 5
time_warp_mode: bicubic
apply_freq_mask: true
freq_mask_width_range:
- 0
- 30
num_freq_mask: 2
apply_time_mask: true
time_mask_width_range:
- 0
- 40
num_time_mask: 2
normalize: utterance_mvn
normalize_conf: {}
preencoder: null
preencoder_conf: {}
encoder: conformer
encoder_conf:
output_size: 512
attention_heads: 8
linear_units: 2048
num_blocks: 12
dropout_rate: 0.1
positional_dropout_rate: 0.1
attention_dropout_rate: 0.1
input_layer: conv2d
normalize_before: true
macaron_style: true
pos_enc_layer_type: rel_pos
selfattention_layer_type: rel_selfattn
activation_type: swish
use_cnn_module: true
cnn_module_kernel: 31
postencoder: null
postencoder_conf: {}
decoder: transformer
decoder_conf:
attention_heads: 8
linear_units: 2048
num_blocks: 6
dropout_rate: 0.1
positional_dropout_rate: 0.1
self_attention_dropout_rate: 0.1
src_attention_dropout_rate: 0.1
postdecoder: null
postdecoder_conf: {}
required:
- output_dir
- token_list
version: 0.10.3a2
distributed: false
```
</details>
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
huggingtweets/amnananadeem-talal916 | huggingtweets | 2021-12-28T12:50:37Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
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/1433365322313043974/gPI08qaY_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/1377835980552474624/sxTjuspv_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">halal talal & amna</div>
<div style="text-align: center; font-size: 14px;">@amnananadeem-talal916</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 halal talal & amna.
| Data | halal talal | amna |
| --- | --- | --- |
| Tweets downloaded | 3187 | 3132 |
| Retweets | 484 | 778 |
| Short tweets | 532 | 369 |
| Tweets kept | 2171 | 1985 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/42dvu161/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 @amnananadeem-talal916's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2irbhtmu) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2irbhtmu/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/amnananadeem-talal916')
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)
|
luomingshuang/icefall_avsr_grid_combinenet_ctc | luomingshuang | 2021-12-28T12:46:37Z | 0 | 0 | null | [
"region:us"
] | null | 2022-03-02T23:29:05Z | # Pre-trained CombineNet-CTC models for the GRID audio-visual dataset with icefall.
The model was trained on full [GRID](https://zenodo.org/record/3625687#.Ybn7HagzY2w) with the scripts in [icefall](https://github.com/k2-fsa/icefall).
See (https://github.com/k2-fsa/icefall/tree/master/egs/grid/AVSR/combinenet_ctc_avsr) for more details of this model.
## How to use
See (https://github.com/k2-fsa/icefall/blob/master/egs/grid/AVSR/combinenet_ctc_avsr/Pre-trained.md)
## Training procedure
The main repositories are list below, we will update the training and decoding scripts with the update of version.
k2: https://github.com/k2-fsa/k2
icefall: https://github.com/k2-fsa/icefall
* Install k2 and lhotse, k2 installation guide refers to https://k2.readthedocs.io/en/latest/installation/index.html, lhotse refers to https://lhotse.readthedocs.io/en/latest/getting-started.html#installation. I think the latest version would be ok. And please also install the requirements listed in icefall.
* Clone icefall(https://github.com/k2-fsa/icefall) and check to the commit showed above.
```
git clone https://github.com/k2-fsa/icefall
cd icefall
```
* Preparing data.
```
cd egs/grid/AVSR
bash ./prepare.sh
```
* Training
```
export CUDA_VISIBLE_DEVICES="0"
python combinenet_ctc_avsr/train.py --world-size 1
```
## Evaluation results
The best decoding results (WER) on GRID TEST are listed below, we got this result by averaging models from epoch 25 to 29, the decoding method is `whole-lattice-rescoring`, when lm scale is 0.01.
||TEST|
|--|--|
|WER|1.71%| |
facebook/wav2vec2-large-lv60 | facebook | 2021-12-28T12:45:09Z | 10,076 | 8 | transformers | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"pretraining",
"speech",
"en",
"dataset:librispeech_asr",
"arxiv:2006.11477",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z | ---
language: en
datasets:
- librispeech_asr
tags:
- speech
license: apache-2.0
---
# Wav2Vec2-Large-LV60
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/)
The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.
**Note**: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model **speech recognition**, a tokenizer should be created and the model should be fine-tuned on labeled text data. Check out [this blog](https://huggingface.co/blog/fine-tune-wav2vec2-english) for more in-detail explanation of how to fine-tune the model.
[Paper](https://arxiv.org/abs/2006.11477)
Authors: Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli
**Abstract**
We show for the first time that learning powerful representations from speech audio alone followed by fine-tuning on transcribed speech can outperform the best semi-supervised methods while being conceptually simpler. wav2vec 2.0 masks the speech input in the latent space and solves a contrastive task defined over a quantization of the latent representations which are jointly learned. Experiments using all labeled data of Librispeech achieve 1.8/3.3 WER on the clean/other test sets. When lowering the amount of labeled data to one hour, wav2vec 2.0 outperforms the previous state of the art on the 100 hour subset while using 100 times less labeled data. Using just ten minutes of labeled data and pre-training on 53k hours of unlabeled data still achieves 4.8/8.2 WER. This demonstrates the feasibility of speech recognition with limited amounts of labeled data.
The original model can be found under https://github.com/pytorch/fairseq/tree/master/examples/wav2vec#wav2vec-20.
# Usage
See [this notebook](https://colab.research.google.com/drive/1FjTsqbYKphl9kL-eILgUc-bl4zVThL8F?usp=sharing) for more information on how to fine-tune the model. |
luomingshuang/icefall_vsr_grid_visualnet_ctc | luomingshuang | 2021-12-28T12:24:34Z | 0 | 0 | null | [
"region:us"
] | null | 2022-03-02T23:29:05Z | # Pre-trained VisualNet-CTC models for the GRID visual dataset with icefall.
The model was trained on full [GRID](https://zenodo.org/record/3625687#.Ybn7HagzY2w) with the scripts in [icefall](https://github.com/k2-fsa/icefall).
See (https://github.com/k2-fsa/icefall/tree/master/egs/grid/AVSR/visualnet_ctc_asr) for more details of this model.
## How to use
See (https://github.com/k2-fsa/icefall/blob/master/egs/grid/AVSR/visualnet_ctc_asr/Pre-trained.md)
## Training procedure
The main repositories are list below, we will update the training and decoding scripts with the update of version.
k2: https://github.com/k2-fsa/k2
icefall: https://github.com/k2-fsa/icefall
* Install k2 and lhotse, k2 installation guide refers to https://k2.readthedocs.io/en/latest/installation/index.html, lhotse refers to https://lhotse.readthedocs.io/en/latest/getting-started.html#installation. I think the latest version would be ok. And please also install the requirements listed in icefall.
* Clone icefall(https://github.com/k2-fsa/icefall) and check to the commit showed above.
```
git clone https://github.com/k2-fsa/icefall
cd icefall
```
* Preparing data.
```
cd egs/grid/AVSR
bash ./prepare.sh
```
* Training
```
export CUDA_VISIBLE_DEVICES="0"
python visualnet_ctc_asr/train.py --world-size 1
```
## Evaluation results
The best decoding results (WER) on GRID TEST are listed below, we got this result by averaging models from epoch 16 to 25, the decoding method is `1best`.
||TEST|
|--|--|
|WER|15.68%| |
huggingtweets/talal916 | huggingtweets | 2021-12-28T09:23:31Z | 105 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
language: en
thumbnail: http://www.huggingtweets.com/talal916/1640683407279/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/1433365322313043974/gPI08qaY_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">halal talal</div>
<div style="text-align: center; font-size: 14px;">@talal916</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 halal talal.
| Data | halal talal |
| --- | --- |
| Tweets downloaded | 3187 |
| Retweets | 483 |
| Short tweets | 533 |
| Tweets kept | 2171 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2q5bns0k/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 @talal916's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/20wq85ea) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/20wq85ea/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/talal916')
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)
|
nlp-waseda/gpt2-small-japanese-wikipedia | nlp-waseda | 2021-12-28T06:31:38Z | 23 | 3 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"ja",
"dataset:wikipedia",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
language:
- ja
license: cc-by-sa-4.0
datasets:
- wikipedia
widget:
- text: "早稲田 大学 で 自然 言語 処理 を"
---
# nlp-waseda/gpt2-small-japanese-wikipedia
This model is Japanese GPT-2 pretrained on Japanese Wikipedia.
## Intended uses & limitations
You can use the raw model for text generation or fine-tune it to a downstream task.
Note that the texts should be segmented into words using Juman++ in advance.
### How to use
You can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we set a seed for reproducibility:
```python
>>> from transformers import pipeline, set_seed
>>> generator = pipeline('text-generation', model='nlp-waseda/gpt2-small-japanese-wikipedia')
>>> set_seed(42)
>>> generator("早稲田 大学 で 自然 言語 処理 を", max_length=30, do_sample=True, pad_token_id=2, num_return_sequences=5)
[{'generated_text': '早稲田 大学 で 自然 言語 処理 を 学び 、 1969 年 に は 同 大学院 を 修了 。 東京 芝浦 電気 株式 会社 に 就職 後 、 情報 処理'},
{'generated_text': '早稲田 大学 で 自然 言語 処理 を 学び 、 帰国 後 は 立教 大学 理学部 助手 を 務めた 。 1978 年 に 神奈川 県立 湘南 高等 学校 校長 に 就任'},
{'generated_text': '早稲田 大学 で 自然 言語 処理 を 研究 。 1972 年 に 早稲田 大学 文学部 ドイツ 文学 専攻 を 卒業 し 、 同 年 から 1979 年 まで 上智 大学'},
{'generated_text': '早稲田 大学 で 自然 言語 処理 を 専攻 する 。 1979 年 東京 農工 大学 農学 部 卒業 。 1980 年 同 大学院 農学 研究 科 修士 課程 修了 。'},
{'generated_text': '早稲田 大学 で 自然 言語 処理 を 専攻 し ながら 、 日本 で 活動 する 自然 言語 研究 家 。 大学 時代 は 東京 大学 理学部 の 助手 を 務め'}]
```
Here is how to use this model to get the features of a given text in PyTorch:
```python
from transformers import ReformerTokenizer, GPT2Model
tokenizer = ReformerTokenizer.from_pretrained('nlp-waseda/gpt2-small-japanese-wikipedia')
model = GPT2Model.from_pretrained('nlp-waseda/gpt2-small-japanese-wikipedia')
text = "早稲田 大学 で 自然 言語 処理 を"
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
## Training data
The GPT-2 model was pretrained on Japanese Wikipedia, dumped on 2021-12-20.
## Training procedure
### Preprocessing
The texts are normalized using zenhan, segmented into words using Juman++, and tokenized using SentencePiece. Juman++ 2.0.0-rc3 was used for pretraining.
The model was trained on 8 NVIDIA A100 GPUs.
|
huggingtweets/ngrossman81 | huggingtweets | 2021-12-28T04:15:54Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
language: en
thumbnail: http://www.huggingtweets.com/ngrossman81/1640664926929/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/805525876808892417/nSCRZS58_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">Nicholas Grossman</div>
<div style="text-align: center; font-size: 14px;">@ngrossman81</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 Nicholas Grossman.
| Data | Nicholas Grossman |
| --- | --- |
| Tweets downloaded | 3249 |
| Retweets | 272 |
| Short tweets | 113 |
| Tweets kept | 2864 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3gkanovn/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 @ngrossman81's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/18u9hhz0) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/18u9hhz0/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/ngrossman81')
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)
|
Smone55/autonlp-au_topics-452311620 | Smone55 | 2021-12-28T01:56:22Z | 6 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autonlp",
"en",
"dataset:Smone55/autonlp-data-au_topics",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-02T23:29:05Z | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- Smone55/autonlp-data-au_topics
co2_eq_emissions: 208.0823957145878
---
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 452311620
- CO2 Emissions (in grams): 208.0823957145878
## Validation Metrics
- Loss: 0.5259971022605896
- Accuracy: 0.8767479025169796
- Macro F1: 0.8618813750734912
- Micro F1: 0.8767479025169796
- Weighted F1: 0.8742964006840133
- Macro Precision: 0.8627700506991158
- Micro Precision: 0.8767479025169796
- Weighted Precision: 0.8755603985289852
- Macro Recall: 0.8662183006750934
- Micro Recall: 0.8767479025169796
- Weighted Recall: 0.8767479025169796
## 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 AutoNLP"}' https://api-inference.huggingface.co/models/Smone55/autonlp-au_topics-452311620
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("Smone55/autonlp-au_topics-452311620", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("Smone55/autonlp-au_topics-452311620", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
``` |
tiennvcs/layoutlmv2-base-uncased-finetuned-vi-infovqa | tiennvcs | 2021-12-27T14:23:33Z | 11 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"layoutlmv2",
"document-question-answering",
"generated_from_trainer",
"license:cc-by-nc-sa-4.0",
"endpoints_compatible",
"region:us"
] | document-question-answering | 2022-03-02T23:29:05Z | ---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
model-index:
- name: layoutlmv2-base-uncased-finetuned-vi-infovqa
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# layoutlmv2-base-uncased-finetuned-vi-infovqa
This model is a fine-tuned version of [microsoft/layoutlmv2-base-uncased](https://huggingface.co/microsoft/layoutlmv2-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 4.3332
## 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: 250500
- 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 |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 0.33 | 100 | 5.3461 |
| No log | 0.66 | 200 | 4.9734 |
| No log | 0.99 | 300 | 4.6074 |
| No log | 1.32 | 400 | 4.4548 |
| 4.6355 | 1.65 | 500 | 4.3831 |
| 4.6355 | 1.98 | 600 | 4.3332 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.8.0+cu101
- Datasets 1.17.0
- Tokenizers 0.10.3
|
SEISHIN/distilbert-base-uncased-finetuned-ner | SEISHIN | 2021-12-27T07:53:05Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2022-03-02T23:29:04Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9289272666888077
- name: Recall
type: recall
value: 0.9386956035350711
- name: F1
type: f1
value: 0.933785889160917
- name: Accuracy
type: accuracy
value: 0.9842565968195466
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0605
- Precision: 0.9289
- Recall: 0.9387
- F1: 0.9338
- Accuracy: 0.9843
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2388 | 1.0 | 878 | 0.0671 | 0.9162 | 0.9211 | 0.9187 | 0.9813 |
| 0.0504 | 2.0 | 1756 | 0.0602 | 0.9225 | 0.9366 | 0.9295 | 0.9834 |
| 0.0299 | 3.0 | 2634 | 0.0605 | 0.9289 | 0.9387 | 0.9338 | 0.9843 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
xkang/distilbert-base-uncased-finetuned-imdb | xkang | 2021-12-27T07:30:09Z | 9 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2022-03-02T23:29:05Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
model-index:
- name: distilbert-base-uncased-finetuned-imdb
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-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4717
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.7096 | 1.0 | 157 | 2.4920 |
| 2.5741 | 2.0 | 314 | 2.4237 |
| 2.5386 | 3.0 | 471 | 2.4355 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.0
- Datasets 1.17.1.dev0
- Tokenizers 0.10.3
|
lijingxin/dummy-model | lijingxin | 2021-12-27T02:12:17Z | 5 | 0 | transformers | [
"transformers",
"tf",
"camembert",
"fill-mask",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2022-03-02T23:29:05Z | ---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: dummy-model
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# dummy-model
This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on an unknown dataset.
It achieves the following results on the evaluation set:
## 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: None
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.15.0
- TensorFlow 2.7.0
- Datasets 1.17.0
- Tokenizers 0.10.3
|
Ayham/roberta_gpt2_new_max64_summarization_cnndm | Ayham | 2021-12-27T00:19:01Z | 10 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"generated_from_trainer",
"dataset:cnn_dailymail",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-03-02T23:29:04Z | ---
tags:
- generated_from_trainer
datasets:
- cnn_dailymail
model-index:
- name: roberta_gpt2_new_max64_summarization_cnndm
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta_gpt2_new_max64_summarization_cnndm
This model is a fine-tuned version of [](https://huggingface.co/) on the cnn_dailymail 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
- lr_scheduler_warmup_steps: 2000
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
SEISHIN/distilbert-base-uncased-finetuned-mnli | SEISHIN | 2021-12-26T16:30:56Z | 7 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-02T23:29:04Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-mnli
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: mnli
metrics:
- name: Accuracy
type: accuracy
value: 0.82190524707081
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-mnli
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6560
- Accuracy: 0.8219
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 0.5161 | 1.0 | 24544 | 0.5025 | 0.8037 |
| 0.4176 | 2.0 | 49088 | 0.5274 | 0.8131 |
| 0.3154 | 3.0 | 73632 | 0.5348 | 0.8194 |
| 0.2294 | 4.0 | 98176 | 0.6560 | 0.8219 |
| 0.1827 | 5.0 | 122720 | 0.8190 | 0.8203 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
mohammadtari/arxivinterface | mohammadtari | 2021-12-26T02:18:42Z | 4 | 0 | transformers | [
"transformers",
"tf",
"t5",
"text2text-generation",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-03-02T23:29:05Z | ---
tags:
- generated_from_keras_callback
model-index:
- name: t5_small_summarization_model
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# t5_small_summarization_model
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
## 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: None
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.15.0
- TensorFlow 2.7.0
- Datasets 1.17.0
- Tokenizers 0.10.3
|
Ayham/xlmroberta_large_gpt2_summarization_cnndm | Ayham | 2021-12-26T00:06:35Z | 6 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"generated_from_trainer",
"dataset:cnn_dailymail",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-03-02T23:29:04Z | ---
tags:
- generated_from_trainer
datasets:
- cnn_dailymail
model-index:
- name: xlmroberta_large_gpt2_summarization_cnndm
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. -->
# xlmroberta_large_gpt2_summarization_cnndm
This model is a fine-tuned version of [](https://huggingface.co/) on the cnn_dailymail 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
- lr_scheduler_warmup_steps: 2000
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
Andry/1111 | Andry | 2021-12-25T20:04:09Z | 0 | 0 | null | [
"region:us"
] | null | 2022-03-02T23:29:04Z | C:\Users\andry\Desktop\Выжигание 24-12-2021.jpg |
s3h/finetuned-arabert-head-gec | s3h | 2021-12-25T19:17:45Z | 4 | 0 | transformers | [
"transformers",
"tf",
"bert",
"fill-mask",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2022-03-02T23:29:05Z | ---
tags:
- generated_from_keras_callback
model-index:
- name: s3h/finetuned-arabert-head-gec
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. -->
# s3h/finetuned-arabert-head-gec
This model is a fine-tuned version of [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 16.9313
- Validation Loss: 19.1589
- 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': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 1, '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 |
|:----------:|:---------------:|:-----:|
| 16.9313 | 19.1589 | 0 |
### Framework versions
- Transformers 4.14.1
- TensorFlow 2.6.2
- Datasets 1.17.0
- Tokenizers 0.10.3
|
vanadhi/bert-base-uncased-fiqa-flm-sq-flit | vanadhi | 2021-12-25T18:44:16Z | 13 | 1 | transformers | [
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | question-answering | 2022-03-02T23:29:05Z | ---
tags:
- generated_from_trainer
model-index:
- name: bert-base-uncased-fiqa-flm-sq-flit
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-fiqa-flm-sq-flit
This model is a fine-tuned version of bert-base-uncased on a custom dataset created for question answering in
financial domain.
## Model description
BERT is a transformers model pretrained on a large corpus of English data in a self-supervised fashion.
The model was further processed as below for the specific downstream QA task.
1. Pretrained for domain adaptation with Masked language modeling (MLM) objective with
the FIQA challenge Opinion-based QA task is available here - https://drive.google.com/file/d/1BlWaV-qVPfpGyJoWQJU9bXQgWCATgxEP/view
2. Pretrained with MLM objective with custom generated dataset for Banking and Finance.
3. Fine Tuned with SQuAD V2 dataset for QA task adaptation.
4. Fine Tuned with custom labeled dataset in SQuAD format for domain and task adaptation.
## Intended uses & limitations
The model is intended to be used for a custom Questions Answering system in the BFSI domain.
## 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
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 2.0
### Training results
### Framework versions
- Transformers 4.15.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
airKlizz/mt5-base-wikinewssum-german | airKlizz | 2021-12-25T15:13:41Z | 16 | 1 | transformers | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"summarization",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | summarization | 2022-03-02T23:29:05Z | ---
license: apache-2.0
tags:
- summarization
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mt5-base-wikinewssum-german
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-base-wikinewssum-german
This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5135
- Rouge1: 8.0553
- Rouge2: 2.7846
- Rougel: 6.2182
- Rougelsum: 7.6203
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5.6e-05
- train_batch_size: 4
- eval_batch_size: 4
- 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
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| No log | 1.0 | 723 | 2.7112 | 7.3681 | 2.3679 | 5.5705 | 6.7588 |
| No log | 2.0 | 1446 | 2.6178 | 7.8539 | 2.7551 | 6.2081 | 7.4139 |
| No log | 3.0 | 2169 | 2.5756 | 7.8401 | 2.6075 | 6.0135 | 7.4303 |
| No log | 4.0 | 2892 | 2.5465 | 8.1097 | 2.8525 | 6.268 | 7.6482 |
| 3.4589 | 5.0 | 3615 | 2.5315 | 8.0192 | 2.7848 | 6.2484 | 7.5859 |
| 3.4589 | 6.0 | 4338 | 2.5222 | 8.1063 | 2.8986 | 6.337 | 7.6564 |
| 3.4589 | 7.0 | 5061 | 2.5136 | 8.0565 | 2.8707 | 6.2732 | 7.6105 |
| 3.4589 | 8.0 | 5784 | 2.5135 | 8.0553 | 2.7846 | 6.2182 | 7.6203 |
### Framework versions
- Transformers 4.13.0
- Pytorch 1.10.1
- Datasets 1.16.1
- Tokenizers 0.10.3
|
Palak/xlm-roberta-base_squad | Palak | 2021-12-25T11:05:12Z | 4 | 1 | transformers | [
"transformers",
"pytorch",
"xlm-roberta",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:mit",
"endpoints_compatible",
"region:us"
] | question-answering | 2022-03-02T23:29:04Z | ---
license: mit
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: xlm-roberta-base_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. -->
# xlm-roberta-base_squad
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the squad dataset.
- "eval_exact_match": 82.69631031220435
- "eval_f1": 89.4562841806503
- "eval_samples": 10918
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.14.1
- Pytorch 1.9.0
- Datasets 1.16.1
- Tokenizers 0.10.3
|
snoop2head/kogpt-conditional-2 | snoop2head | 2021-12-25T04:42:13Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | # KoGPT-Conditional-2
### Condition format
```python
# create condition sentence
random_main_logit = np.random.normal(
loc=3.368,
scale=1.015,
size=1
)[0].round(1)
random_sub_logit = np.random.normal(
loc=1.333,
scale=0.790,
size=1
)[0].round(1)
condition_sentence = f"{random_main_logit}만큼 행복감정인 문장이다. {random_sub_logit}만큼 놀람감정인 문장이다. "
```
### Input Format
```python
# make input sentence
input_sentence = "수상한 밤들이 계속되던 날, 언젠가부터 나는"
condition_plus_input = condition_sentence + input_sentence
print(condition_plus_input)
```
```
3.9만큼 행복감정인 문장이다. 1.2만큼 놀람감정인 문장이다. 수상한 밤들이 계속되던 날, 언젠가부터 나는
```
### How to infer
```
inferred_sentence = infer_sentence(condition_plus_input, k=10, output_token_length=max_token_length)
inferred_sentence
```
```
['3.9만큼 행복감정인 문장이다. 1.2만큼 놀람감정인 문장이다. 수상한 밤들이 계속되던 날, 언젠가부터 나는 서서히 제정신을 차리고 일어날 수 있었다',
'3.9만큼 행복감정인 문장이다. 1.2만큼 놀람감정인 문장이다. 수상한 밤들이 계속되던 날, 언젠가부터 나는 달 보는 걸 좋아하게 되었다',
'3.9만큼 행복감정인 문장이다. 1.2만큼 놀람감정인 문장이다. 수상한 밤들이 계속되던 날, 언젠가부터 나는 수상한 사람들의 입을 들여다 볼 수 있었다',
'3.9만큼 행복감정인 문장이다. 1.2만큼 놀람감정인 문장이다. 수상한 밤들이 계속되던 날, 언젠가부터 나는 이상한 나라의 앨리스가 되어 있었다',
'3.9만큼 행복감정인 문장이다. 1.2만큼 놀람감정인 문장이다. 수상한 밤들이 계속되던 날, 언젠가부터 나는 기이한 경험을 했다',
'3.9만큼 행복감정인 문장이다. 1.2만큼 놀람감정인 문장이다. 수상한 밤들이 계속되던 날, 언젠가부터 나는 이상하게도 평화가 찾아온다는 사실을 깨달았다',
'3.9만큼 행복감정인 문장이다. 1.2만큼 놀람감정인 문장이다. 수상한 밤들이 계속되던 날, 언젠가부터 나는 어둠을 뚫는 무언가가 있다는 걸 알았다',
'3.9만큼 행복감정인 문장이다. 1.2만큼 놀람감정인 문장이다. 수상한 밤들이 계속되던 날, 언젠가부터 나는 달빛의 의미를 이해하기 시작했다',
'3.9만큼 행복감정인 문장이다. 1.2만큼 놀람감정인 문장이다. 수상한 밤들이 계속되던 날, 언젠가부터 나는 안방에서 잘 때 내 손을 꼭 잡았다',
'3.9만큼 행복감정인 문장이다. 1.2만큼 놀람감정인 문장이다. 수상한 밤들이 계속되던 날, 언젠가부터 나는 이상한 나라의 앨리스처럼 눈을 반짝이며 주위를 탐구하기 시작했다']
```
|
BigSalmon/MrLincolnBerta | BigSalmon | 2021-12-24T21:54:31Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2022-03-02T23:29:04Z | Example Prompt:
```
informal english: things are better when they are open source, because they are constantly being updated to enhance experience.
Translated into the Style of Abraham Lincoln: in the open-source paradigm, code is ( ceaselessly / perpetually ) being ( reengineered / revamped / polished ), thereby ( advancing / enhancing / optimizing / <mask> ) the user experience.
```
Demo: https://huggingface.co/spaces/BigSalmon/MASK2 |
Palak/distilroberta-base_squad | Palak | 2021-12-24T18:22:38Z | 6 | 0 | transformers | [
"transformers",
"pytorch",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | question-answering | 2022-03-02T23:29:04Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: distilroberta-base_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. -->
# distilroberta-base_squad
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the **squadV1** dataset.
- "eval_exact_match": 80.97445600756859
- "eval_f1": 88.0153886332912
- "eval_samples": 10790
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.14.1
- Pytorch 1.9.0
- Datasets 1.16.1
- Tokenizers 0.10.3
|
Palak/microsoft_deberta-base_squad | Palak | 2021-12-24T18:22:28Z | 5 | 2 | transformers | [
"transformers",
"pytorch",
"deberta",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:mit",
"endpoints_compatible",
"region:us"
] | question-answering | 2022-03-02T23:29:04Z | ---
license: mit
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: microsoft_deberta-base_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. -->
# microsoft_deberta-base_squad
This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/deberta-base) on the **squadV1** dataset.
- "eval_exact_match": 86.30085146641439
- "eval_f1": 92.68502275661561
- "eval_samples": 10788
## 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: 12
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.14.1
- Pytorch 1.9.0
- Datasets 1.16.1
- Tokenizers 0.10.3
|
Palak/google_electra-base-discriminator_squad | Palak | 2021-12-24T18:15:58Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"electra",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | question-answering | 2022-03-02T23:29:04Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: google_electra-base-discriminator_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. -->
# google_electra-base-discriminator_squad
This model is a fine-tuned version of [google/electra-base-discriminator](https://huggingface.co/google/electra-base-discriminator) on the **squadV1** dataset.
- "eval_exact_match": 85.33585619678335
- "eval_f1": 91.97363450387108
- "eval_samples": 10784
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.14.1
- Pytorch 1.9.0
- Datasets 1.16.1
- Tokenizers 0.10.3
|
Palak/microsoft_deberta-large_squad | Palak | 2021-12-24T18:12:42Z | 84 | 0 | transformers | [
"transformers",
"pytorch",
"deberta",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"endpoints_compatible",
"region:us"
] | question-answering | 2022-03-02T23:29:04Z | ---
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: microsoft-deberta-large
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. -->
# microsoft-deberta-large
This model is a fine-tuned version of [microsoft_deberta-large](https://huggingface.co/microsoft/deberta-large) on the **squadV1** dataset.
- "eval_exact_match": 87.89025543992432
- "eval_f1": 93.8755152147345
- "eval_samples": 10788
## 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: 12
- 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
### Framework versions
- Transformers 4.14.1
- Pytorch 1.9.0
- Datasets 1.16.1
- Tokenizers 0.10.3
|
racai/distilbert-multi-base-romanian-cased | racai | 2021-12-24T17:32:28Z | 24 | 0 | transformers | [
"transformers",
"pytorch",
"tf",
"jax",
"distilbert",
"ro",
"dataset:oscar",
"dataset:wikipedia",
"arxiv:2112.12650",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z | ---
language: ro
license: mit
datasets:
- oscar
- wikipedia
---
# Romanian DistilBERT
This repository contains the a Romanian cased version of DistilBERT (named DistilMulti-BERT-base-ro in the paper) that was obtained by distilling an ensemble of two teacher models: [dumitrescustefan/bert-base-romanian-cased-v1](https://huggingface.co/dumitrescustefan/bert-base-romanian-cased-v1) and [readerbench/RoBERT-base](https://huggingface.co/readerbench/RoBERT-base).
The model was introduced in [this paper](https://arxiv.org/abs/2112.12650). The adjacent code can be found
[here](https://github.com/racai-ai/Romanian-DistilBERT).
## Usage
```python
from transformers import AutoTokenizer, AutoModel
# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained("racai/distilbert-multi-base-romanian-cased")
model = AutoModel.from_pretrained("racai/distilbert-multi-base-romanian-cased")
# tokenize a test sentence
input_ids = tokenizer.encode("Aceasta este o propoziție de test.", add_special_tokens=True, return_tensors="pt")
# run the tokens trough the model
outputs = model(input_ids)
print(outputs)
```
## Model Size
The model is 35% smaller than `bert-base-romanian-cased-v1` and 30% smaller than `RoBERT-base`.
| Model | Size (MB) | Params (Millions) |
|--------------------------------|:---------:|:----------------:|
| RoBERT-base | 441 | 114 |
| bert-base-romanian-cased-v1 | 477 | 124 |
| distilbert-multi-base-romanian-cased | 312 | 81 |
## Evaluation
We evaluated the model in comparison with its two teachers on 5 Romanian tasks:
- **UPOS**: Universal Part of Speech (F1-macro)
- **XPOS**: Extended Part of Speech (F1-macro)
- **NER**: Named Entity Recognition (F1-macro)
- **SAPN**: Sentiment Anlaysis - Positive vs Negative (Accuracy)
- **SAR**: Sentiment Analysis - Rating (F1-macro)
- **DI**: Dialect identification (F1-macro)
- **STS**: Semantic Textual Similarity (Pearson)
| Model | UPOS | XPOS | NER | SAPN | SAR | DI | STS |
|--------------------------------|:----:|:----:|:---:|:----:|:---:|:--:|:---:|
| RoBERT-base | 98.02 | 97.15 | 85.14 | 98.30 | 79.40 | 96.07 | 81.18 |
| bert-base-romanian-cased-v1 | 98.00 | 96.46 | 85.88 | 98.07 | 79.61 | 95.58 | 80.30 |
| distilbert-multi-base-romanian-cased | 98.07 | 96.83 | 83.22 | 98.11 | 79.77 | 96.18 | 80.66 |
### BibTeX entry and citation info
```bibtex
@article{avram2021distilling,
title={Distilling the Knowledge of Romanian BERTs Using Multiple Teachers},
author={Andrei-Marius Avram and Darius Catrina and Dumitru-Clementin Cercel and Mihai Dascălu and Traian Rebedea and Vasile Păiş and Dan Tufiş},
journal={ArXiv},
year={2021},
volume={abs/2112.12650}
}
``` |
SebastianS/dummy-model | SebastianS | 2021-12-24T16:44:54Z | 6 | 0 | transformers | [
"transformers",
"pytorch",
"camembert",
"fill-mask",
"fr",
"dataset:oscar",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2022-03-02T23:29:04Z | ---
language: fr
license: mit
datasets:
- oscar
---
# dummy
this is only a dummy model originally based on RoBERT model
## intended uses and limitations
not intended to be used, same limitations as camembert-base model
## how to use
it cant be used (lol)
## training data
French subcorpus of the newly available multilingual corpus OSCAR
## training procedure
evaluated on multiple downstream tasks
## variable and metrics
not explicitly stated
## evaluation metrics
maybe OSCAR
## evaluation results
not explicitly stated
|
baffo32/gpt2-ptmap | baffo32 | 2021-12-24T13:45:44Z | 15 | 0 | transformers | [
"transformers",
"pytorch",
"tf",
"jax",
"tflite",
"rust",
"gpt2",
"text-generation",
"exbert",
"en",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
language: en
tags:
- exbert
license: mit
---
# GPT-2
Test the whole generation capabilities here: https://transformer.huggingface.co/doc/gpt2-large
Pretrained model on English language using a causal language modeling (CLM) objective. It was introduced in
[this paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf)
and first released at [this page](https://openai.com/blog/better-language-models/).
Disclaimer: The team releasing GPT-2 also wrote a
[model card](https://github.com/openai/gpt-2/blob/master/model_card.md) for their model. Content from this model card
has been written by the Hugging Face team to complete the information they provided and give specific examples of bias.
## Model description
GPT-2 is a transformers model pretrained on a very large corpus of English data in a self-supervised fashion. This
means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots
of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely,
it was trained to guess the next word in sentences.
More precisely, inputs are sequences of continuous text of a certain length and the targets are the same sequence,
shifted one token (word or piece of word) to the right. The model uses internally a mask-mechanism to make sure the
predictions for the token `i` only uses the inputs from `1` to `i` but not the future tokens.
This way, the model learns an inner representation of the English language that can then be used to extract features
useful for downstream tasks. The model is best at what it was pretrained for however, which is generating texts from a
prompt.
## Intended uses & limitations
You can use the raw model for text generation or fine-tune it to a downstream task. See the
[model hub](https://huggingface.co/models?filter=gpt2) to look for fine-tuned versions on a task that interests you.
### How to use
You can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we
set a seed for reproducibility:
```python
>>> from transformers import pipeline, set_seed
>>> generator = pipeline('text-generation', model='gpt2')
>>> set_seed(42)
>>> generator("Hello, I'm a language model,", max_length=30, num_return_sequences=5)
[{'generated_text': "Hello, I'm a language model, a language for thinking, a language for expressing thoughts."},
{'generated_text': "Hello, I'm a language model, a compiler, a compiler library, I just want to know how I build this kind of stuff. I don"},
{'generated_text': "Hello, I'm a language model, and also have more than a few of your own, but I understand that they're going to need some help"},
{'generated_text': "Hello, I'm a language model, a system model. I want to know my language so that it might be more interesting, more user-friendly"},
{'generated_text': 'Hello, I\'m a language model, not a language model"\n\nThe concept of "no-tricks" comes in handy later with new'}]
```
Here is how to use this model to get the features of a given text in PyTorch:
```python
from transformers import GPT2Tokenizer, GPT2Model
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
model = GPT2Model.from_pretrained('gpt2')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in TensorFlow:
```python
from transformers import GPT2Tokenizer, TFGPT2Model
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
model = TFGPT2Model.from_pretrained('gpt2')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
### Limitations and bias
The training data used for this model has not been released as a dataset one can browse. We know it contains a lot of
unfiltered content from the internet, which is far from neutral. As the openAI team themselves point out in their
[model card](https://github.com/openai/gpt-2/blob/master/model_card.md#out-of-scope-use-cases):
> Because large-scale language models like GPT-2 do not distinguish fact from fiction, we don’t support use-cases
> that require the generated text to be true.
>
> Additionally, language models like GPT-2 reflect the biases inherent to the systems they were trained on, so we do
> not recommend that they be deployed into systems that interact with humans > unless the deployers first carry out a
> study of biases relevant to the intended use-case. We found no statistically significant difference in gender, race,
> and religious bias probes between 774M and 1.5B, implying all versions of GPT-2 should be approached with similar
> levels of caution around use cases that are sensitive to biases around human attributes.
Here's an example of how the model can have biased predictions:
```python
>>> from transformers import pipeline, set_seed
>>> generator = pipeline('text-generation', model='gpt2')
>>> set_seed(42)
>>> generator("The White man worked as a", max_length=10, num_return_sequences=5)
[{'generated_text': 'The White man worked as a mannequin for'},
{'generated_text': 'The White man worked as a maniser of the'},
{'generated_text': 'The White man worked as a bus conductor by day'},
{'generated_text': 'The White man worked as a plumber at the'},
{'generated_text': 'The White man worked as a journalist. He had'}]
>>> set_seed(42)
>>> generator("The Black man worked as a", max_length=10, num_return_sequences=5)
[{'generated_text': 'The Black man worked as a man at a restaurant'},
{'generated_text': 'The Black man worked as a car salesman in a'},
{'generated_text': 'The Black man worked as a police sergeant at the'},
{'generated_text': 'The Black man worked as a man-eating monster'},
{'generated_text': 'The Black man worked as a slave, and was'}]
```
This bias will also affect all fine-tuned versions of this model.
## Training data
The OpenAI team wanted to train this model on a corpus as large as possible. To build it, they scraped all the web
pages from outbound links on Reddit which received at least 3 karma. Note that all Wikipedia pages were removed from
this dataset, so the model was not trained on any part of Wikipedia. The resulting dataset (called WebText) weights
40GB of texts but has not been publicly released. You can find a list of the top 1,000 domains present in WebText
[here](https://github.com/openai/gpt-2/blob/master/domains.txt).
## Training procedure
### Preprocessing
The texts are tokenized using a byte-level version of Byte Pair Encoding (BPE) (for unicode characters) and a
vocabulary size of 50,257. The inputs are sequences of 1024 consecutive tokens.
The larger model was trained on 256 cloud TPU v3 cores. The training duration was not disclosed, nor were the exact
details of training.
## Evaluation results
The model achieves the following results without any fine-tuning (zero-shot):
| Dataset | LAMBADA | LAMBADA | CBT-CN | CBT-NE | WikiText2 | PTB | enwiki8 | text8 | WikiText103 | 1BW |
|:--------:|:-------:|:-------:|:------:|:------:|:---------:|:------:|:-------:|:------:|:-----------:|:-----:|
| (metric) | (PPL) | (ACC) | (ACC) | (ACC) | (PPL) | (PPL) | (BPB) | (BPC) | (PPL) | (PPL) |
| | 35.13 | 45.99 | 87.65 | 83.4 | 29.41 | 65.85 | 1.16 | 1,17 | 37.50 | 75.20 |
### BibTeX entry and citation info
```bibtex
@article{radford2019language,
title={Language Models are Unsupervised Multitask Learners},
author={Radford, Alec and Wu, Jeff and Child, Rewon and Luan, David and Amodei, Dario and Sutskever, Ilya},
year={2019}
}
```
<a href="https://huggingface.co/exbert/?model=gpt2">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
|
hiraki/wav2vec2-base-timit-demo-colab | hiraki | 2021-12-24T10:51:35Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2022-03-02T23:29:05Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-timit-demo-colab
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.3780
- Wer: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 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
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 0.08 | 10 | 14.0985 | 1.0 |
| No log | 0.16 | 20 | 13.8638 | 1.0004 |
| No log | 0.24 | 30 | 13.5135 | 1.0023 |
| No log | 0.32 | 40 | 12.8708 | 1.0002 |
| No log | 0.4 | 50 | 11.6927 | 1.0 |
| No log | 0.48 | 60 | 10.2733 | 1.0 |
| No log | 0.56 | 70 | 8.1396 | 1.0 |
| No log | 0.64 | 80 | 5.3503 | 1.0 |
| No log | 0.72 | 90 | 3.7975 | 1.0 |
| No log | 0.8 | 100 | 3.4275 | 1.0 |
| No log | 0.88 | 110 | 3.3596 | 1.0 |
| No log | 0.96 | 120 | 3.3780 | 1.0 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.13.3
- Tokenizers 0.10.3
|
toasthans/Twitter_Ohne_HPSearch | toasthans | 2021-12-24T10:20:23Z | 105 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-02T23:29:05Z | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: Twitter_Ohne_HPSearch
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. -->
# Twitter_Ohne_HPSearch
This model is a fine-tuned version of [bert-base-german-cased](https://huggingface.co/bert-base-german-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0262
- Accuracy: 0.8300
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 421 | 0.4296 | 0.8181 |
| 0.4451 | 2.0 | 842 | 0.4889 | 0.8240 |
| 0.1761 | 3.0 | 1263 | 0.9503 | 0.8103 |
| 0.0486 | 4.0 | 1684 | 1.0262 | 0.8300 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
BigSalmon/InformalToFormalLincoln16 | BigSalmon | 2021-12-23T18:48:23Z | 10 | 1 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:04Z | Informal to Formal:
```
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln16")
model = AutoModelWithLMHead.from_pretrained("BigSalmon/InformalToFormalLincoln16")
```
```
https://huggingface.co/spaces/BigSalmon/GPT2 (The model for this space changes over time)
```
```
https://huggingface.co/spaces/BigSalmon/GPT2_Most_Probable (The model for this space changes over time)
```
```
How To Make Prompt:
informal english: i am very ready to do that just that.
Translated into the Style of Abraham Lincoln: you can assure yourself of my readiness to work toward this end.
Translated into the Style of Abraham Lincoln: please be assured that i am most ready to undertake this laborious task.
informal english: space is huge and needs to be explored.
Translated into the Style of Abraham Lincoln: space awaits traversal, a new world whose boundaries are endless.
Translated into the Style of Abraham Lincoln: space is a ( limitless / boundless ) expanse, a vast virgin domain awaiting exploration.
informal english: corn fields are all across illinois, visible once you leave chicago.
Translated into the Style of Abraham Lincoln: corn fields ( permeate illinois / span the state of illinois / ( occupy / persist in ) all corners of illinois / line the horizon of illinois / envelop the landscape of illinois ), manifesting themselves visibly as one ventures beyond chicago.
informal english:
```` |
toasthans/Facebook_Mit_HPS | toasthans | 2021-12-23T17:47:19Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-02T23:29:05Z | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: Facebook_Mit_HPS
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. -->
# Facebook_Mit_HPS
This model is a fine-tuned version of [bert-base-german-cased](https://huggingface.co/bert-base-german-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3681
- Accuracy: 0.9281
## 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: 3.906763521176542e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 30
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 292 | 0.2394 | 0.9238 |
| 0.2248 | 2.0 | 584 | 0.3112 | 0.9178 |
| 0.2248 | 3.0 | 876 | 0.3681 | 0.9281 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
merve/distilbert-base-uncased-finetuned-ner | merve | 2021-12-23T16:19:38Z | 61 | 0 | transformers | [
"transformers",
"tf",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_keras_callback",
"dataset:conll2003",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2022-03-02T23:29:05Z | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: merve/distilbert-base-uncased-finetuned-ner
results: []
datasets:
- "conll2003"
---
<!-- 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. -->
# merve/distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.2037
- Validation Loss: 0.0703
- 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': '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}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 0.2037 | 0.0703 | 0 |
### Framework versions
- Transformers 4.16.0.dev0
- TensorFlow 2.7.0
- Datasets 1.17.1.dev0
- Tokenizers 0.10.3
|
toasthans/Facebook_and_Twitter_Ohne_HPS | toasthans | 2021-12-23T14:55:46Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-02T23:29:05Z | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: Facebook_and_Twitter_Ohne_HPS
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. -->
# Facebook_and_Twitter_Ohne_HPS
This model is a fine-tuned version of [bert-base-german-cased](https://huggingface.co/bert-base-german-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9218
- Accuracy: 0.8512
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4364 | 1.0 | 713 | 0.4107 | 0.8302 |
| 0.2843 | 2.0 | 1426 | 0.4316 | 0.8495 |
| 0.0869 | 3.0 | 2139 | 0.7700 | 0.8558 |
| 0.0443 | 4.0 | 2852 | 0.9218 | 0.8512 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
redbloodyknife/DialoGPT-medium-shayo | redbloodyknife | 2021-12-23T12:17:05Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
tags:
- conversational
---
#Shayo Bot by Shogun
#Ai Chatbot Testing based on GPT2 and DialoGPT-Medium by Microsoft
#shoguπ#9999 |
toasthans/Facebook_Mit_HPS_5_Epoch | toasthans | 2021-12-23T08:27:55Z | 105 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-02T23:29:05Z | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: Facebook_Mit_HPS_5_Epoch
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. -->
# Facebook_Mit_HPS_5_Epoch
This model is a fine-tuned version of [bert-base-german-cased](https://huggingface.co/bert-base-german-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4774
- Accuracy: 0.9315
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5.546392051994155e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 5
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 292 | 0.2181 | 0.9264 |
| 0.2411 | 2.0 | 584 | 0.2571 | 0.9289 |
| 0.2411 | 3.0 | 876 | 0.5712 | 0.8947 |
| 0.0558 | 4.0 | 1168 | 0.4675 | 0.9332 |
| 0.0558 | 5.0 | 1460 | 0.4774 | 0.9315 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
KoichiYasuoka/roberta-small-japanese-aozora-char | KoichiYasuoka | 2021-12-23T02:55:42Z | 5 | 1 | transformers | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"japanese",
"masked-lm",
"ja",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2022-03-02T23:29:04Z | ---
language:
- "ja"
tags:
- "japanese"
- "masked-lm"
license: "cc-by-sa-4.0"
pipeline_tag: "fill-mask"
mask_token: "[MASK]"
widget:
- text: "日本に着いたら[MASK]を訪ねなさい。"
---
# roberta-small-japanese-aozora-char
## Model Description
This is a RoBERTa model pre-trained on 青空文庫 texts with character tokenizer. You can fine-tune `roberta-small-japanese-aozora-char` for downstream tasks, such as [POS-tagging](https://huggingface.co/KoichiYasuoka/roberta-small-japanese-char-luw-upos), dependency-parsing, and so on.
## How to Use
```py
from transformers import AutoTokenizer,AutoModelForMaskedLM
tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/roberta-small-japanese-aozora-char")
model=AutoModelForMaskedLM.from_pretrained("KoichiYasuoka/roberta-small-japanese-aozora-char")
```
|
Ayham/albert_gpt2_summarization_cnndm | Ayham | 2021-12-23T01:36:49Z | 14 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"generated_from_trainer",
"dataset:cnn_dailymail",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-03-02T23:29:04Z | ---
tags:
- generated_from_trainer
datasets:
- cnn_dailymail
model-index:
- name: albert_large_gpt2_summarization_cnndm
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. -->
# albert_large_gpt2_summarization_cnndm
This model is a fine-tuned version of [](https://huggingface.co/) on the cnn_dailymail 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
- lr_scheduler_warmup_steps: 2000
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
BigSalmon/MrLincoln5 | BigSalmon | 2021-12-22T22:41:39Z | 10 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:04Z | Informal to Formal:
```
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("gpt2")
model = AutoModelWithLMHead.from_pretrained("BigSalmon/MrLincoln5")
```
```
https://huggingface.co/spaces/BigSalmon/GPT2 (The model for this space changes over time)
```
```
https://huggingface.co/spaces/BigSalmon/GPT2_Most_Probable (The model for this space changes over time)
```
```
How To Make Prompt:
informal english: i am very ready to do that just that.
Translated into the Style of Abraham Lincoln: you can assure yourself of my readiness to work toward this end.
Translated into the Style of Abraham Lincoln: please be assured that i am most ready to undertake this laborious task.
informal english: space is huge and needs to be explored.
Translated into the Style of Abraham Lincoln: space awaits traversal, a new world whose boundaries are endless.
Translated into the Style of Abraham Lincoln: space is a ( limitless / boundless ) expanse, a vast virgin domain awaiting exploration.
informal english:
```` |
SajjadAyoubi/clip-fa-vision | SajjadAyoubi | 2021-12-22T19:03:07Z | 1,523 | 5 | transformers | [
"transformers",
"pytorch",
"clip_vision_model",
"feature-extraction",
"arxiv:2103.00020",
"endpoints_compatible",
"region:us"
] | feature-extraction | 2022-03-02T23:29:04Z | # CLIPfa: Connecting Farsi Text and Images
OpenAI released [`the paper Learning Transferable Visual Models From Natural Language Supervision`](https://arxiv.org/abs/2103.00020) in which they present the CLIP (Contrastive Language–Image Pre-training) model. This model is trained to connect text and images, by matching their corresponding vector representations using a contrastive learning objective. CLIP consists of two separate models, a vision encoder and a text encoder. These were trained on 400 Million images and corresponding captions. We have trained a Farsi (Persian) version of OpenAI's CLIP on a dataset of 400,000 (image, text) pairs. We used [`Farahani's RoBERTa-fa`](https://huggingface.co/m3hrdadfi/roberta-zwnj-wnli-mean-tokens) as the text encoder and [`ViT`](https://huggingface.co/openai/clip-vit-base-patch32) as the vision encoder from Original CLIP and finetuned them.
- It should be noted that only 400K pairs were used for this training, whereas 4 million pairs were used for the Original CLIP. Also, the training took 30 days across 592 GPUs powered by the V100 chip.
## How to use?
Both models generate vectors with 768 dimensions.
```python
from transformers import CLIPVisionModel, RobertaModel, AutoTokenizer, CLIPFeatureExtractor
# download pre-trained models
vision_encoder = CLIPVisionModel.from_pretrained('SajjadAyoubi/clip-fa-vision')
preprocessor = CLIPFeatureExtractor.from_pretrained('SajjadAyoubi/clip-fa-vision')
text_encoder = RobertaModel.from_pretrained('SajjadAyoubi/clip-fa-text')
tokenizer = AutoTokenizer.from_pretrained('SajjadAyoubi/clip-fa-text')
# define input image and input text
text = 'something'
image = PIL.Image.open('my_favorite_image.jpg')
# compute embeddings
text_embedding = text_encoder(**tokenizer(text,
return_tensors='pt')).pooler_output
image_embedding = vision_encoder(**preprocessor(image,
return_tensors='pt')).pooler_output
text_embedding.shape == image_embedding.shape
```
## Demo:
The followings are just some use cases of CLIPfa on 25K [`Unsplash images`](https://github.com/unsplash/datasets)
- use `pip install -q git+https://github.com/sajjjadayobi/clipfa.git`
```python
from clipfa import CLIPDemo
demo = CLIPDemo(vision_encoder, text_encoder, tokenizer)
demo.compute_text_embeddings(['گاو' ,'اسب' ,'ماهی'])
demo.compute_image_embeddings(test_df.image_path.to_list())
```
## Online Demo: [CLIPfa at Huggingface🤗 spaces](https://huggingface.co/spaces/SajjadAyoubi/CLIPfa-Demo)
We used a small set of images (25K) to keep this app almost real-time, but it's obvious that the quality of image search depends heavily on the size of the image database.
> Made with ❤️ in my basement🤫
|
gngpostalsrvc/BERiTmodel2 | gngpostalsrvc | 2021-12-22T17:25:25Z | 105 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2022-03-02T23:29:05Z | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: BERiTmodel2
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. -->
# BERiTmodel2
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1508
## 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: cosine
- lr_scheduler_warmup_steps: 280
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 3.1924 | 1.0 | 2854 | 3.4329 |
| 3.0936 | 2.0 | 5708 | 3.5036 |
| 2.9998 | 3.0 | 8562 | 3.1906 |
| 2.9064 | 4.0 | 11416 | 3.4867 |
| 2.8493 | 5.0 | 14270 | 3.2027 |
| 2.7538 | 6.0 | 17124 | 2.9772 |
| 2.7273 | 7.0 | 19978 | 2.9950 |
| 2.7399 | 8.0 | 22832 | 2.9690 |
| 2.67 | 9.0 | 25686 | 3.0311 |
| 2.6388 | 10.0 | 28540 | 3.1508 |
### Framework versions
- Transformers 4.14.1
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
microsoft/wavlm-base | microsoft | 2021-12-22T17:23:36Z | 72,617 | 7 | transformers | [
"transformers",
"pytorch",
"wavlm",
"feature-extraction",
"speech",
"en",
"arxiv:2110.13900",
"region:us"
] | feature-extraction | 2022-03-02T23:29:05Z | ---
language:
- en
datasets:
tags:
- speech
inference: false
---
# WavLM-Base
[Microsoft's WavLM](https://github.com/microsoft/unilm/tree/master/wavlm)
The base model pretrained on 16kHz sampled speech audio. When using the model, make sure that your speech input is also sampled at 16kHz.
**Note**: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model **speech recognition**, a tokenizer should be created and the model should be fine-tuned on labeled text data. Check out [this blog](https://huggingface.co/blog/fine-tune-wav2vec2-english) for more in-detail explanation of how to fine-tune the model.
The model was pre-trained on 960h of [Librispeech](https://huggingface.co/datasets/librispeech_asr).
[Paper: WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://arxiv.org/abs/2110.13900)
Authors: Sanyuan Chen, Chengyi Wang, Zhengyang Chen, Yu Wu, Shujie Liu, Zhuo Chen, Jinyu Li, Naoyuki Kanda, Takuya Yoshioka, Xiong Xiao, Jian Wu, Long Zhou, Shuo Ren, Yanmin Qian, Yao Qian, Jian Wu, Michael Zeng, Furu Wei
**Abstract**
*Self-supervised learning (SSL) achieves great success in speech recognition, while limited exploration has been attempted for other speech processing tasks. As speech signal contains multi-faceted information including speaker identity, paralinguistics, spoken content, etc., learning universal representations for all speech tasks is challenging. In this paper, we propose a new pre-trained model, WavLM, to solve full-stack downstream speech tasks. WavLM is built based on the HuBERT framework, with an emphasis on both spoken content modeling and speaker identity preservation. We first equip the Transformer structure with gated relative position bias to improve its capability on recognition tasks. For better speaker discrimination, we propose an utterance mixing training strategy, where additional overlapped utterances are created unsupervisely and incorporated during model training. Lastly, we scale up the training dataset from 60k hours to 94k hours. WavLM Large achieves state-of-the-art performance on the SUPERB benchmark, and brings significant improvements for various speech processing tasks on their representative benchmarks.*
The original model can be found under https://github.com/microsoft/unilm/tree/master/wavlm.
# Usage
This is an English pre-trained speech model that has to be fine-tuned on a downstream task like speech recognition or audio classification before it can be
used in inference. The model was pre-trained in English and should therefore perform well only in English. The model has been shown to work well on the [SUPERB benchmark](https://superbbenchmark.org/).
**Note**: The model was pre-trained on phonemes rather than characters. This means that one should make sure that the input text is converted to a sequence
of phonemes before fine-tuning.
## Speech Recognition
To fine-tune the model for speech recognition, see [the official speech recognition example](https://github.com/huggingface/transformers/tree/master/examples/pytorch/speech-recognition).
## Speech Classification
To fine-tune the model for speech classification, see [the official audio classification example](https://github.com/huggingface/transformers/tree/master/examples/pytorch/audio-classification).
## Speaker Verification
TODO
## Speaker Diarization
TODO
# Contribution
The model was contributed by [cywang](https://huggingface.co/cywang) and [patrickvonplaten](https://huggingface.co/patrickvonplaten).
# License
The official license can be found [here](https://github.com/microsoft/UniSpeech/blob/main/LICENSE)
 |
deepparag/DumBot-Beta | deepparag | 2021-12-22T16:32:40Z | 6 | 0 | transformers | [
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"conversational",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
thumbnail: https://cdn.discordapp.com/app-icons/870239976690970625/c02cae78ae105f07969cfd8f8ea3d0a0.png
tags:
- conversational
license: mit
---
An generative AI made using [microsoft/DialoGPT-small](https://huggingface.co/microsoft/DialoGPT-small).
Trained on:
https://www.kaggle.com/Cornell-University/movie-dialog-corpus
https://www.kaggle.com/jef1056/discord-data
Important:
The AI can be a bit weird at times as it is still undergoing training!
At times it send stuff using :<random_wierd_words>: as they are discord emotes.
It also send random @RandomName as it is trying to ping people.
This works well on discord but on the web not so much but it is easy enough to remove such stuff using [re.sub](https://docs.python.org/3/library/re.html#re.sub)
Issues:
The AI like with all conversation AI lacks a character, it changes its name way too often. This can be solved using an AIML chatbot to give it a stable character!
[Live Demo](https://dumbot-331213.uc.r.appspot.com/)
Example:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("deepparag/DumBot")
model = AutoModelWithLMHead.from_pretrained("deepparag/DumBot")
# Let's chat for 4 lines
for step in range(4):
# encode the new user input, add the eos_token and return a tensor in Pytorch
new_user_input_ids = tokenizer.encode(input(">> User:") + tokenizer.eos_token, return_tensors='pt')
# print(new_user_input_ids)
# append the new user input tokens to the chat history
bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids
# generated a response while limiting the total chat history to 1000 tokens,
chat_history_ids = model.generate(
bot_input_ids, max_length=200,
pad_token_id=tokenizer.eos_token_id,
no_repeat_ngram_size=4,
do_sample=True,
top_k=100,
top_p=0.7,
temperature=0.8
)
# pretty print last ouput tokens from bot
print("DumBot: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)))
``` |
huggingartists/100-gecs | huggingartists | 2021-12-22T15:23:59Z | 103 | 0 | transformers | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingartists",
"lyrics",
"lm-head",
"causal-lm",
"en",
"dataset:huggingartists/100-gecs",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
language: en
datasets:
- huggingartists/100-gecs
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/9fd98af9a817af8cd78636f71895b6ad.500x500x1.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">100 gecs</div>
<a href="https://genius.com/artists/100-gecs">
<div style="text-align: center; font-size: 14px;">@100-gecs</div>
</a>
</div>
I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).
Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!
## How does it work?
To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).
## Training data
The model was trained on lyrics from 100 gecs.
Dataset is available [here](https://huggingface.co/datasets/huggingartists/100-gecs).
And can be used with:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/100-gecs")
```
[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/3c9j4tvq/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 100 gecs's lyrics.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/1v0ffa4e) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/1v0ffa4e/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='huggingartists/100-gecs')
generator("I am", num_return_sequences=5)
```
Or with Transformers library:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("huggingartists/100-gecs")
model = AutoModelWithLMHead.from_pretrained("huggingartists/100-gecs")
```
## 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 Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
|
dtomas/roberta-base-bne-irony | dtomas | 2021-12-22T13:55:36Z | 8 | 1 | transformers | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"irony",
"sarcasm",
"spanish",
"es",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-02T23:29:05Z | ---
language:
- es
tags:
- irony
- sarcasm
- spanish
widget:
- text: "¡Cómo disfruto peleándome con los Transformers!"
example_title: "Ironic"
- text: "Madrid es la capital de España"
example_title: "Non ironic"
---
# RoBERTa base finetuned for Spanish irony detection
## Model description
Model to perform irony detection in Spanish. This is a finetuned version of the [RoBERTa-base-bne model](https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne) on the [IroSvA](https://www.autoritas.net/IroSvA2019/) corpus. Only the Spanish from Spain variant was used in the training process. It comprises 2,400 tweets labeled as ironic/non-ironic.
|
Ayham/xlmroberta_gpt2_summarization_xsum | Ayham | 2021-12-22T12:59:45Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"generated_from_trainer",
"dataset:xsum",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-03-02T23:29:04Z | ---
tags:
- generated_from_trainer
datasets:
- xsum
model-index:
- name: xlmroberta_gpt2_summarization_xsum
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlmroberta_gpt2_summarization_xsum
This model is a fine-tuned version of [](https://huggingface.co/) on the xsum 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
- lr_scheduler_warmup_steps: 2000
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
ayameRushia/roberta-base-indonesian-sentiment-analysis-smsa | ayameRushia | 2021-12-22T10:33:50Z | 51 | 0 | transformers | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:indonlu",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-02T23:29:05Z | ---
license: mit
tags:
- generated_from_trainer
datasets:
- indonlu
metrics:
- accuracy
model-index:
- name: roberta-base-indonesian-sentiment-analysis-smsa
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: indonlu
type: indonlu
args: smsa
metrics:
- name: Accuracy
type: accuracy
value: 0.9349206349206349
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-indonesian-sentiment-analysis-smsa
This model is a fine-tuned version of [flax-community/indonesian-roberta-base](https://huggingface.co/flax-community/indonesian-roberta-base) on the indonlu dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4252
- Accuracy: 0.9349
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7582 | 1.0 | 688 | 0.3280 | 0.8786 |
| 0.3225 | 2.0 | 1376 | 0.2398 | 0.9206 |
| 0.2057 | 3.0 | 2064 | 0.2574 | 0.9230 |
| 0.1642 | 4.0 | 2752 | 0.2820 | 0.9302 |
| 0.1266 | 5.0 | 3440 | 0.3344 | 0.9317 |
| 0.0608 | 6.0 | 4128 | 0.3543 | 0.9341 |
| 0.058 | 7.0 | 4816 | 0.4252 | 0.9349 |
| 0.0315 | 8.0 | 5504 | 0.4736 | 0.9310 |
| 0.0166 | 9.0 | 6192 | 0.4649 | 0.9349 |
| 0.0143 | 10.0 | 6880 | 0.4648 | 0.9341 |
### Framework versions
- Transformers 4.14.1
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
NbAiLabArchive/test_w5_long_roberta_tokenizer_adafactor | NbAiLabArchive | 2021-12-22T09:40:02Z | 14 | 0 | transformers | [
"transformers",
"jax",
"tensorboard",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2022-03-02T23:29:04Z | Just for performing some experiments. Do not use. |
hrdipto/wav2vec2-xls-r-timit-tokenizer-base | hrdipto | 2021-12-22T07:19:26Z | 105 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2022-03-02T23:29:05Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-xls-r-timit-tokenizer-base
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-xls-r-timit-tokenizer-base
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0828
- Wer: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---:|
| 3.3134 | 4.03 | 500 | 3.0814 | 1.0 |
| 2.9668 | 8.06 | 1000 | 3.0437 | 1.0 |
| 2.9604 | 12.1 | 1500 | 3.0337 | 1.0 |
| 2.9619 | 16.13 | 2000 | 3.0487 | 1.0 |
| 2.9588 | 20.16 | 2500 | 3.0859 | 1.0 |
| 2.957 | 24.19 | 3000 | 3.0921 | 1.0 |
| 2.9555 | 28.22 | 3500 | 3.0828 | 1.0 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.13.3
- Tokenizers 0.10.3
|
huggingtweets/_luisinhobr-beckvencido | huggingtweets | 2021-12-22T02:57:34Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
language: en
thumbnail: http://www.huggingtweets.com/_luisinhobr-beckvencido/1640141850327/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/1470914400764715012/YO9XqA0n_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/1390224220643278850/LcIZLss-_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">agrummgit ag😜 & luisfer nando</div>
<div style="text-align: center; font-size: 14px;">@_luisinhobr-beckvencido</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 agrummgit ag😜 & luisfer nando.
| Data | agrummgit ag😜 | luisfer nando |
| --- | --- | --- |
| Tweets downloaded | 3226 | 2366 |
| Retweets | 379 | 367 |
| Short tweets | 672 | 503 |
| Tweets kept | 2175 | 1496 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/34idoh6o/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 @_luisinhobr-beckvencido's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1w6ipjqa) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1w6ipjqa/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/_luisinhobr-beckvencido')
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)
|
Jeska/BertjeWDialDataALL04 | Jeska | 2021-12-22T02:47:07Z | 18 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2022-03-02T23:29:04Z | ---
tags:
- generated_from_trainer
model-index:
- name: BertjeWDialDataALL04
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. -->
# BertjeWDialDataALL04
This model is a fine-tuned version of [GroNLP/bert-base-dutch-cased](https://huggingface.co/GroNLP/bert-base-dutch-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9717
## 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
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 2.2954 | 1.0 | 1542 | 2.0372 |
| 2.2015 | 2.0 | 3084 | 2.0104 |
| 2.1661 | 3.0 | 4626 | 2.0372 |
| 2.1186 | 4.0 | 6168 | 1.9549 |
| 2.0939 | 5.0 | 7710 | 1.9438 |
| 2.0867 | 6.0 | 9252 | 1.9648 |
| 2.0462 | 7.0 | 10794 | 1.9465 |
| 2.0315 | 8.0 | 12336 | 1.9412 |
### Framework versions
- Transformers 4.13.0.dev0
- Pytorch 1.10.0
- Datasets 1.16.1
- Tokenizers 0.10.3
|
akashsivanandan/wav2vec2-large-xls-r-300m-tamil-colab-final | akashsivanandan | 2021-12-22T01:26:48Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2022-03-02T23:29:05Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-tamil-colab-final
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-tamil-colab-final
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7539
- Wer: 0.6135
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 11.1466 | 1.0 | 118 | 4.3444 | 1.0 |
| 3.4188 | 2.0 | 236 | 3.2496 | 1.0 |
| 2.8617 | 3.0 | 354 | 1.6165 | 1.0003 |
| 0.958 | 4.0 | 472 | 0.7984 | 0.8720 |
| 0.5929 | 5.0 | 590 | 0.6733 | 0.7831 |
| 0.4628 | 6.0 | 708 | 0.6536 | 0.7621 |
| 0.3834 | 7.0 | 826 | 0.6037 | 0.7155 |
| 0.3242 | 8.0 | 944 | 0.6376 | 0.7184 |
| 0.2736 | 9.0 | 1062 | 0.6214 | 0.7070 |
| 0.2433 | 10.0 | 1180 | 0.6158 | 0.6944 |
| 0.2217 | 11.0 | 1298 | 0.6548 | 0.6830 |
| 0.1992 | 12.0 | 1416 | 0.6331 | 0.6775 |
| 0.1804 | 13.0 | 1534 | 0.6644 | 0.6874 |
| 0.1639 | 14.0 | 1652 | 0.6629 | 0.6649 |
| 0.143 | 15.0 | 1770 | 0.6927 | 0.6836 |
| 0.1394 | 16.0 | 1888 | 0.6933 | 0.6888 |
| 0.1296 | 17.0 | 2006 | 0.7039 | 0.6860 |
| 0.1212 | 18.0 | 2124 | 0.7042 | 0.6628 |
| 0.1121 | 19.0 | 2242 | 0.7132 | 0.6475 |
| 0.1069 | 20.0 | 2360 | 0.7423 | 0.6438 |
| 0.1063 | 21.0 | 2478 | 0.7171 | 0.6484 |
| 0.1025 | 22.0 | 2596 | 0.7396 | 0.6451 |
| 0.0946 | 23.0 | 2714 | 0.7400 | 0.6432 |
| 0.0902 | 24.0 | 2832 | 0.7385 | 0.6286 |
| 0.0828 | 25.0 | 2950 | 0.7368 | 0.6286 |
| 0.079 | 26.0 | 3068 | 0.7471 | 0.6306 |
| 0.0747 | 27.0 | 3186 | 0.7524 | 0.6201 |
| 0.0661 | 28.0 | 3304 | 0.7576 | 0.6201 |
| 0.0659 | 29.0 | 3422 | 0.7579 | 0.6130 |
| 0.0661 | 30.0 | 3540 | 0.7539 | 0.6135 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.13.3
- Tokenizers 0.10.3
|
enelpol/czywiesz-question | enelpol | 2021-12-21T21:24:34Z | 7 | 1 | transformers | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"pl",
"dataset:enelpol/czywiesz",
"endpoints_compatible",
"region:us"
] | feature-extraction | 2022-03-02T23:29:05Z | ---
language: pl
datasets:
- enelpol/czywiesz
task_categories:
- question_answering
task_ids:
- open-domain-qa
multilinguality:
- monolingual
size_categories:
- 1k<n<10K
---
## Model description
This is the question encoder for the Polish DPR question answering model. The full model consists of two encoders.
Please read [context encoder documentation](https://huggingface.co/enelpol/czywiesz-context) to get the details of the model. |
Ayham/albert_gpt2_summarization_xsum | Ayham | 2021-12-21T21:20:14Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"generated_from_trainer",
"dataset:xsum",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-03-02T23:29:04Z | ---
tags:
- generated_from_trainer
datasets:
- xsum
model-index:
- name: albert_gpt2_summarization_xsum
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# albert_gpt2_summarization_xsum
This model is a fine-tuned version of [](https://huggingface.co/) on the xsum 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
- lr_scheduler_warmup_steps: 2000
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
davanstrien/flyswot | davanstrien | 2021-12-21T17:21:49Z | 0 | 0 | null | [
"onnx",
"region:us"
] | null | 2022-03-02T23:29:05Z | TODO
## Model description
In progress model for detecting 'fake' flysheets
## Intended uses & limitations
Not currently intended for public consumption...
## Limitations and bias
Not currently intended for public consumption...
## Training data
## Eval results |
davanstrien/book-genre-classification | davanstrien | 2021-12-21T16:05:46Z | 6 | 2 | adapter-transformers | [
"adapter-transformers",
"bert",
"adapterhub:text-classification",
"region:us"
] | null | 2022-03-02T23:29:05Z | ---
tags:
- bert
- adapterhub:text-classification
- adapter-transformers
---
# Adapter `davanstrien/book-genre-classification` for bert-base-cased
An [adapter](https://adapterhub.ml) for the `bert-base-cased` model that was trained on the [text-classification](https://adapterhub.ml/explore/text-classification/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoModelWithHeads
model = AutoModelWithHeads.from_pretrained("bert-base-cased")
adapter_name = model.load_adapter("davanstrien/book-genre-classification", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here --> |
espnet/ftshijt_espnet2_asr_yolo_mixtec_transformer | espnet | 2021-12-21T15:59:04Z | 3 | 0 | espnet | [
"espnet",
"audio",
"automatic-speech-recognition",
"dataset:yolo_mixtec",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | automatic-speech-recognition | 2022-03-02T23:29:05Z | ---
tags:
- espnet
- audio
- automatic-speech-recognition
language: noinfo
datasets:
- yolo_mixtec
license: cc-by-4.0
---
## ESPnet2 ASR model
### `espnet/ftshijt_espnet2_asr_yolo_mixtec_transformer`
This model was trained by ftshijt using yolo_mixtec recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
pip install -e .
cd els/yolo_mixtec/asr1
./run.sh --skip_data_prep false --skip_train true --download_model espnet/ftshijt_espnet2_asr_yolo_mixtec_transformer
```
<!-- Generated by scripts/utils/show_asr_result.sh -->
# RESULTS
## Environments
- date: `Wed Nov 10 02:59:39 EST 2021`
- python version: `3.9.7 (default, Sep 16 2021, 13:09:58) [GCC 7.5.0]`
- espnet version: `espnet 0.10.4a1`
- pytorch version: `pytorch 1.9.0`
- Git hash: ``
- Commit date: ``
## asr_train_asr_transformer_specaug_raw_bpe500
### WER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_lm_lm_train_bpe500_valid.loss.ave_asr_model_valid.acc.best/test|4985|81348|84.1|11.8|4.1|2.5|18.3|82.5|
### CER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_lm_lm_train_bpe500_valid.loss.ave_asr_model_valid.acc.best/test|4985|626187|93.4|2.2|4.4|2.4|9.0|82.5|
### TER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_lm_lm_train_bpe500_valid.loss.ave_asr_model_valid.acc.best/test|4985|325684|90.7|5.2|4.1|2.2|11.5|82.5|
## ASR config
<details><summary>expand</summary>
```
config: conf/tuning/train_asr_transformer_specaug.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/asr_train_asr_transformer_specaug_raw_bpe500
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: 15
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - valid
- acc
- max
keep_nbest_models: 10
grad_clip: 5
grad_clip_type: 2.0
grad_noise: false
accum_grad: 2
no_forward_run: false
resume: true
train_dtype: float32
use_amp: false
log_interval: null
use_tensorboard: true
use_wandb: false
wandb_project: null
wandb_id: null
wandb_entity: null
wandb_name: null
wandb_model_log_interval: -1
detect_anomaly: false
pretrain_path: null
init_param: []
ignore_init_mismatch: false
freeze_param: []
num_iters_per_epoch: null
batch_size: 32
valid_batch_size: null
batch_bins: 1000000
valid_batch_bins: null
train_shape_file:
- exp/asr_stats_raw_bpe500/train/speech_shape
- exp/asr_stats_raw_bpe500/train/text_shape.bpe
valid_shape_file:
- exp/asr_stats_raw_bpe500/valid/speech_shape
- exp/asr_stats_raw_bpe500/valid/text_shape.bpe
batch_type: folded
valid_batch_type: null
fold_length:
- 80000
- 150
sort_in_batch: descending
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
train_data_path_and_name_and_type:
- - /tmp/st-jiatong-54826.tbQP9L0N/raw/train/wav.scp
- speech
- kaldi_ark
- - /tmp/st-jiatong-54826.tbQP9L0N/raw/train/text
- text
- text
valid_data_path_and_name_and_type:
- - /tmp/st-jiatong-54826.tbQP9L0N/raw/dev/wav.scp
- speech
- kaldi_ark
- - /tmp/st-jiatong-54826.tbQP9L0N/raw/dev/text
- text
- text
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
optim: adam
optim_conf:
lr: 1.0
scheduler: noamlr
scheduler_conf:
warmup_steps: 25000
token_list:
- <blank>
- <unk>
- '4'
- '3'
- '1'
- '2'
- A
- ▁NDI
- '''4'
- '''1'
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- C
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- G
- ▁ÁNIMA
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- B
- NDIO
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- MU
- ▁PADRE
- D
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- Z
- ▁TORO
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- ▁COR
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- ▁CAMISA
- ▁CHINGADA
- ▁SEMANA
- ▁COM
- GAR
- ▁MARTÍN
- ▁SÁBADO
- ▁TRABAJO
- ▁CINCO
- ▁DIE
- ▁EST
- NDWA
- ▁LECHIN
- ▁COCO
- ILLU
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- ▁BAUTISTA
- ▁VENTANA
- ▁CUÑAD
- ▁ANTONIU
- ▁COPALA
- LÍN
- ▁SECUND
- ▁COHETE
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- ▁POLICÍA
- ENCIA
- ▁CAD
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- ▁JUEVE
- ▁LIBRU
- ▁QUESU
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- ▁ADÁN
- ▁JUANCITO
- ▁HOR
- ''''
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- ▁COCINA
- ▁PALACIO
- ▁RÓMULO
- K
- ▁ALFONSO
- ▁BARTOLO
- ▁FELIPE
- ▁HERRER
- ▁PAULINO
- ▁YEGUA
- ▁LISTA
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- ▁MORAL
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- ▁FIRMA
- ▁HUEHUETÁN
- ▁MANGUERA
- ▁MEDI
- ▁MUERT
- ▁SALAZAR
- ▁VIERNI
- LILL
- ▁LL
- '-'
- ▁CAMPESINO
- ▁CIVIL
- ▁COMISARIADO
- )
- (
- Ã
- ‘
- ¿
- Ü
- ¡
- Q
- F
- Á
- P
- Ÿ
- W
- Ý
- <sos/eos>
init: xavier_uniform
input_size: null
ctc_conf:
dropout_rate: 0.0
ctc_type: builtin
reduce: true
ignore_nan_grad: true
model_conf:
ctc_weight: 0.3
lsm_weight: 0.1
length_normalized_loss: false
use_preprocessor: true
token_type: bpe
bpemodel: data/token_list/bpe_unigram500/bpe.model
non_linguistic_symbols: null
cleaner: null
g2p: null
speech_volume_normalize: null
rir_scp: null
rir_apply_prob: 1.0
noise_scp: null
noise_apply_prob: 1.0
noise_db_range: '13_15'
frontend: default
frontend_conf:
fs: 16k
specaug: specaug
specaug_conf:
apply_time_warp: true
time_warp_window: 5
time_warp_mode: bicubic
apply_freq_mask: true
freq_mask_width_range:
- 0
- 30
num_freq_mask: 2
apply_time_mask: true
time_mask_width_range:
- 0
- 40
num_time_mask: 2
normalize: global_mvn
normalize_conf:
stats_file: exp/asr_stats_raw_bpe500/train/feats_stats.npz
preencoder: null
preencoder_conf: {}
encoder: transformer
encoder_conf:
input_layer: conv2d
num_blocks: 12
linear_units: 2048
dropout_rate: 0.1
output_size: 512
attention_heads: 4
attention_dropout_rate: 0.0
postencoder: null
postencoder_conf: {}
decoder: transformer
decoder_conf:
input_layer: embed
num_blocks: 6
linear_units: 2048
dropout_rate: 0.1
required:
- output_dir
- token_list
version: 0.10.4a1
distributed: false
```
</details>
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
espnet/ftshijt_espnet2_asr_puebla_nahuatl_transfer | espnet | 2021-12-21T15:43:26Z | 4 | 0 | espnet | [
"espnet",
"audio",
"automatic-speech-recognition",
"dataset:puebla_nahuatl",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | automatic-speech-recognition | 2022-03-02T23:29:05Z | ---
tags:
- espnet
- audio
- automatic-speech-recognition
language: noinfo
datasets:
- puebla_nahuatl
license: cc-by-4.0
---
## ESPnet2 ASR model
### `espnet/ftshijt_espnet2_asr_puebla_nahuatl_transfer`
This model was trained by ftshijt using puebla_nahuatl recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
pip install -e .
cd els/puebla_nahuatl/asr1
./run.sh --skip_data_prep false --skip_train true --download_model espnet/ftshijt_espnet2_asr_puebla_nahuatl_transfer
```
<!-- Generated by scripts/utils/show_asr_result.sh -->
# RESULTS
## Environments
- date: `Sun Nov 7 18:16:55 EST 2021`
- python version: `3.9.7 (default, Sep 16 2021, 13:09:58) [GCC 7.5.0]`
- espnet version: `espnet 0.10.4a1`
- pytorch version: `pytorch 1.9.0`
- Git hash: ``
- Commit date: ``
## asr_train_asr_transformer_hubert_raw_bpe500_sp
### WER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_lm_lm_train_bpe500_valid.loss.ave_asr_model_valid.acc.best/test|10576|90532|77.0|17.0|6.0|3.6|26.6|74.0|
### CER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_lm_lm_train_bpe500_valid.loss.ave_asr_model_valid.acc.best/test|10576|590273|92.2|2.1|5.7|3.0|10.8|74.0|
### TER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_lm_lm_train_bpe500_valid.loss.ave_asr_model_valid.acc.best/test|10576|242435|86.0|7.3|6.8|3.5|17.5|74.0|
## ASR config
<details><summary>expand</summary>
```
config: conf/tuning/train_asr_transformer_hubert.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/asr_train_asr_transformer_hubert_raw_bpe500_sp
ngpu: 1
seed: 0
num_workers: 1
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: null
dist_rank: null
local_rank: 0
dist_master_addr: null
dist_master_port: null
dist_launcher: null
multiprocessing_distributed: false
unused_parameters: false
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
collect_stats: false
write_collected_feats: false
max_epoch: 100
patience: 15
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - valid
- acc
- max
keep_nbest_models: 10
grad_clip: 5
grad_clip_type: 2.0
grad_noise: false
accum_grad: 2
no_forward_run: false
resume: true
train_dtype: float32
use_amp: false
log_interval: null
use_tensorboard: true
use_wandb: false
wandb_project: null
wandb_id: null
wandb_entity: null
wandb_name: null
wandb_model_log_interval: -1
detect_anomaly: false
pretrain_path: null
init_param: []
ignore_init_mismatch: false
freeze_param: []
num_iters_per_epoch: null
batch_size: 32
valid_batch_size: null
batch_bins: 1000000
valid_batch_bins: null
train_shape_file:
- exp/asr_stats_raw_bpe500_sp/train/speech_shape
- exp/asr_stats_raw_bpe500_sp/train/text_shape.bpe
valid_shape_file:
- exp/asr_stats_raw_bpe500_sp/valid/speech_shape
- exp/asr_stats_raw_bpe500_sp/valid/text_shape.bpe
batch_type: folded
valid_batch_type: null
fold_length:
- 80000
- 150
sort_in_batch: descending
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
train_data_path_and_name_and_type:
- - /tmp/jiatong-150390.uytFFbyG/raw/train_sp/wav.scp
- speech
- kaldi_ark
- - /tmp/jiatong-150390.uytFFbyG/raw/train_sp/text
- text
- text
valid_data_path_and_name_and_type:
- - /tmp/jiatong-150390.uytFFbyG/raw/dev/wav.scp
- speech
- kaldi_ark
- - /tmp/jiatong-150390.uytFFbyG/raw/dev/text
- text
- text
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
optim: adam
optim_conf:
lr: 1.0
scheduler: noamlr
scheduler_conf:
warmup_steps: 25000
token_list:
- <blank>
- <unk>
- ':'
- N
- ▁A
- ▁WA
- ▁KE
- ▁YO
- ▁NE
- ▁SE
- H
- MO
- WA
- ''''
- ▁NO
- ▁I
- ▁N
- S
- ▁KI
- K
- ▁
- MAH
- KA
- TA
- L
- ▁POS
- PA
- ▁KA
- ▁TA
- ▁MO
- T
- ▁YEHWA
- I
- MEH
- ▁YA
- ▁DE
- MA
- A
- ▁TE
- TI
- TSI
- NI
- CHI
- ▁PERO
- KI
- LI
- TO
- WI
- ▁PARA
- KO
- E
- ▁O
- ▁IKA
- TE
- O
- W
- ▁NEH
- ▁NOCHI
- CH
- ▁TI
- ▁TIK
- LO
- ▁SAH
- ▁MAH
- NA
- LA
- ▁OMPA
- ▁IHKÓ
- YA
- ▁NI
- ▁PORQUE
- ▁MA
- YO
- ▁TEIN
- LIA
- ▁E
- MPA
- ▁NIKA
- X
- YAH
- ▁KWALTSI
- SA
- TSA
- ▁MOCHI
- ▁NIK
- ▁WE
- ▁TO
- TSÍ
- ▁SEMI
- ▁KITA
- WAK
- KWI
- MI
- ▁MM
- ▁XO
- ▁SEKI
- JÓ
- AH
- ▁KOMO
- R
- NE
- ▁OK
- ▁KWALI
- ▁CHI
- ▁YEH
- ▁NELI
- SE
- PO
- WAH
- PI
- ME
- KWA
- ▁PA
- ▁ONKAK
- KE
- ▁YE
- ▁T
- LTIK
- ▁TEHWA
- TAH
- ▁TIKI
- ▁QUE
- ▁NIKI
- PE
- ▁IWKI
- XI
- TOK
- ▁TAMAN
- ▁KO
- TSO
- LE
- RA
- SI
- WÍ
- MAN
- ▁TIMO
- 'NO'
- SO
- ▁MIAK
- U
- ▁TEH
- ▁KICHI
- ▁XA
- WE
- ▁KOW
- KEH
- NÍ
- LIK
- ▁ITECH
- TIH
- ▁PE
- ▁KIPIA
- ▁CUANDO
- ▁KWALTIA
- ▁HASTA
- LOWA
- ▁ENTÓ
- ▁NA
- XO
- RO
- TIA
- ▁NIKITA
- CHIHCHI
- ▁SEPA
- ▁MAHYÁ
- ▁PAHTI
- ▁K
- LIAH
- ▁SAYOH
- MATI
- ▁PI
- TS
- ▁MÁS
- XMATI
- KAH
- ▁XI
- M
- ▁ESTE
- HKO
- KOWIT
- MIKI
- CHO
- ▁TAK
- Á
- ▁KILIAH
- CHIO
- ▁KIHTOWA
- ▁KITE
- NEKI
- ▁ME
- XA
- ▁TEL
- B
- ▁KOWIT
- ▁ATA
- TIK
- ▁EKINTSI
- ▁IMA
- ▁KWA
- ▁OSO
- ▁NEHJÓ
- ▁ITEYO
- Y
- SKEH
- ▁ISTA
- ▁NIKILIA
- LIH
- ▁TIKWI
- ▁PANÉ
- KOWA
- ▁OX
- TEKI
- ▁SA
- NTE
- ▁KIKWI
- TSITSI
- NOH
- AHSI
- ▁IXO
- WIA
- LTSI
- ▁KIMA
- C
- ▁WEHWEI
- ▁TEPITSI
- ▁IHK
- ▁XIWIT
- YI
- LIS
- ▁CA
- XMATTOK
- SÁ
- ▁MOTA
- RE
- ▁TIKIHTO
- ▁MI
- ▁X
- D
- ▁SAN
- WIH
- ▁WEHKA
- KWE
- CHA
- ▁SI
- KTIK
- ▁YETOK
- ▁MOKA
- NEMI
- LILIA
- ▁¿
- TIW
- ▁KIHTOWAH
- LTI
- Ó
- MASÁ
- ▁POR
- ▁TIKITA
- KETSA
- ▁IWA
- METS
- YOH
- ▁TAKWA
- HKEH
- ▁KIKWIH
- ▁KIKWA
- NIA
- ▁ACHI
- ▁KIKWAH
- ▁KACHI
- ▁PO
- ▁IGUAL
- NAL
- ▁PILI
- ▁NIMAN
- YE
- ▁NIKMATI
- WIAH
- ▁KIPA
- ▁M
- J
- ▁KWI
- ▁WI
- WAYA
- Z
- ▁KITEKI
- G
- ▁'
- ▁IHKO
- CE
- ▁TONI
- ▁TSIKITSI
- P
- DO
- TOKEH
- NIK
- ▁TIKILIAH
- ▁KOWTAH
- ▁TAI
- ▁TATA
- TIAH
- CA
- PIL
- CHOWA
- ▁KIMATI
- ▁TAMA
- XKA
- XIWIT
- TOS
- KILIT
- ILWI
- SKI
- YEH
- DA
- WAYO
- ▁TAPA
- ▁NIMO
- CHIT
- ▁NIMITS
- ▁KINA
- PAHTI
- RI
- ▁BUENO
- ▁ESKI
- WAYAH
- PANO
- KOW
- WEYAK
- LPAN
- LTIA
- ▁KITO
- CO
- ▁TINE
- KIH
- JO
- ▁KATKA
- ▁TIKTA
- PAHTIA
- ▁XIWTSI
- ▁CHIKA
- ▁KANAH
- ▁KOYO
- MPI
- ▁IXIWYO
- IHTIK
- ▁KWE
- ▁XIW
- WILIA
- XTIK
- ▁VE
- ▁TIKMATI
- ▁KOKOLIS
- LKWI
- ▁AHKO
- MEKAT
- ▁TIKMA
- ▁NIMITSILIA
- ▁MITS
- XTA
- ▁CO
- ▁KOMA
- ▁KOMOHKÓ
- F
- ▁OKSEKI
- ▁TEISÁ
- ▁ESO
- ▁IKOWYO
- ▁ES
- TOHTO
- XTI
- ▁TSI
- ▁TIKO
- PIHPI
- ▁OKSÉ
- ▁WEHKAPAN
- KALAKI
- ▁WEL
- ▁MIGUEL
- TEKITI
- ▁TOKNI
- ROWA
- ▁MOSKALTIA
- Í
- XOKO
- ▁TIKCHI
- ▁EHE
- ▁KWO
- LPI
- HTOK
- TSTI
- TÍ
- ▁TEIHSÁ
- KILO
- ▁PUES
- SKIA
- HTIW
- LILIAH
- ▁IHWA
- ▁KOSTIK
- ▁TIKIHTOWAH
- ▁CHA
- ▁COMO
- ▁KIMANA
- CU
- TAMAN
- WITS
- ▁KOKO
- ILPIA
- ▁NIMONO
- ▁WELI
- ▁NIKWI
- WTOK
- ▁KINEKI
- KOKOH
- ▁P
- LTIAH
- XKO
- ▁ONKAYA
- TAPOWI
- MATTOK
- ▁MISMO
- ▁NIKIHTO
- ▁NIKMATTOK
- MESKIA
- ▁SOH
- KWOWIT
- XTIA
- WELITA
- ▁DESPUÉS
- ▁IXWA
- ZA
- TSAPOT
- SKAL
- ▁SIEMPRE
- TINEMI
- Ñ
- ▁ESKIA
- NELOWA
- ▁TZINACAPAN
- ▁DI
- XIWYO
- ▁AHA
- ▁AHWIA
- É
- ▁KIKWIAH
- MATTOKEH
- ▁ACHTO
- XTILIA
- TAPAL
- ▁KIHTO
- TEHTE
- ▁PORIN
- ▁TSOPE
- ▁KAHFE
- GU
- ▁NIMITSTAHTANI
- ▁TAHTA
- ▁KOWTATI
- ISWAT
- ▁TIKPIA
- ▁KOMEKAT
- TIOWIH
- ▁TIMONOHNO
- ▁TIEMPO
- WEHKA
- QUI
- ▁TIHTI
- ▁XOXOKTIK
- ▁TAXKAL
- EHE
- ▁AJÁ
- NANAKAT
- NIWKI
- ▁CI
- ▁ITSMOL
- ▁NIKPIA
- TEKPA
- ▁BO
- ▁TASOHKA
- Ú
- ¡
- '8'
- '9'
- '0'
- '1'
- '2'
- ¿
- Ò
- '4'
- À
- '7'
- '5'
- '3'
- ́
- V
- ̈
- Ï
- '6'
- Q
- Ì
- <sos/eos>
init: xavier_uniform
input_size: null
ctc_conf:
dropout_rate: 0.0
ctc_type: builtin
reduce: true
ignore_nan_grad: true
model_conf:
ctc_weight: 0.3
lsm_weight: 0.1
length_normalized_loss: false
extract_feats_in_collect_stats: false
use_preprocessor: true
token_type: bpe
bpemodel: data/token_list/bpe_unigram500/bpe.model
non_linguistic_symbols: null
cleaner: null
g2p: null
speech_volume_normalize: null
rir_scp: null
rir_apply_prob: 1.0
noise_scp: null
noise_apply_prob: 1.0
noise_db_range: '13_15'
frontend: s3prl
frontend_conf:
frontend_conf:
upstream: hubert_large_ll60k
download_dir: ./hub
multilayer_feature: true
fs: 16k
specaug: specaug
specaug_conf:
apply_time_warp: true
time_warp_window: 5
time_warp_mode: bicubic
apply_freq_mask: true
freq_mask_width_range:
- 0
- 30
num_freq_mask: 2
apply_time_mask: true
time_mask_width_range:
- 0
- 40
num_time_mask: 2
normalize: utterance_mvn
normalize_conf: {}
preencoder: linear
preencoder_conf:
input_size: 1024
output_size: 80
encoder: transformer
encoder_conf:
input_layer: conv2d
num_blocks: 12
linear_units: 2048
dropout_rate: 0.1
output_size: 256
attention_heads: 4
attention_dropout_rate: 0.0
postencoder: null
postencoder_conf: {}
decoder: transformer
decoder_conf:
input_layer: embed
num_blocks: 6
linear_units: 2048
dropout_rate: 0.1
required:
- output_dir
- token_list
version: 0.10.4a1
distributed: false
```
</details>
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
bhavikardeshna/multilingual-bert-base-cased-spanish | bhavikardeshna | 2021-12-21T11:43:55Z | 14 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"question-answering",
"arxiv:2112.09866",
"endpoints_compatible",
"region:us"
] | question-answering | 2022-03-02T23:29:05Z | # BibTeX entry and citation info
```
@misc{pandya2021cascading,
title={Cascading Adaptors to Leverage English Data to Improve Performance of Question Answering for Low-Resource Languages},
author={Hariom A. Pandya and Bhavik Ardeshna and Dr. Brijesh S. Bhatt},
year={2021},
eprint={2112.09866},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |
bhavikardeshna/multilingual-bert-base-cased-hindi | bhavikardeshna | 2021-12-21T11:43:34Z | 16 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"question-answering",
"arxiv:2112.09866",
"endpoints_compatible",
"region:us"
] | question-answering | 2022-03-02T23:29:05Z | # BibTeX entry and citation info
```
@misc{pandya2021cascading,
title={Cascading Adaptors to Leverage English Data to Improve Performance of Question Answering for Low-Resource Languages},
author={Hariom A. Pandya and Bhavik Ardeshna and Dr. Brijesh S. Bhatt},
year={2021},
eprint={2112.09866},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |
bhavikardeshna/multilingual-bert-base-cased-english | bhavikardeshna | 2021-12-21T11:42:34Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"question-answering",
"arxiv:2112.09866",
"endpoints_compatible",
"region:us"
] | question-answering | 2022-03-02T23:29:05Z | # BibTeX entry and citation info
```
@misc{pandya2021cascading,
title={Cascading Adaptors to Leverage English Data to Improve Performance of Question Answering for Low-Resource Languages},
author={Hariom A. Pandya and Bhavik Ardeshna and Dr. Brijesh S. Bhatt},
year={2021},
eprint={2112.09866},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |
bhavikardeshna/multilingual-bert-base-cased-chinese | bhavikardeshna | 2021-12-21T11:41:47Z | 6 | 2 | transformers | [
"transformers",
"pytorch",
"bert",
"question-answering",
"arxiv:2112.09866",
"endpoints_compatible",
"region:us"
] | question-answering | 2022-03-02T23:29:05Z | # BibTeX entry and citation info
```
@misc{pandya2021cascading,
title={Cascading Adaptors to Leverage English Data to Improve Performance of Question Answering for Low-Resource Languages},
author={Hariom A. Pandya and Bhavik Ardeshna and Dr. Brijesh S. Bhatt},
year={2021},
eprint={2112.09866},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |
bhavikardeshna/multilingual-bert-base-cased-arabic | bhavikardeshna | 2021-12-21T11:41:30Z | 27 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"question-answering",
"arxiv:2112.09866",
"endpoints_compatible",
"region:us"
] | question-answering | 2022-03-02T23:29:05Z | # BibTeX entry and citation info
```
@misc{pandya2021cascading,
title={Cascading Adaptors to Leverage English Data to Improve Performance of Question Answering for Low-Resource Languages},
author={Hariom A. Pandya and Bhavik Ardeshna and Dr. Brijesh S. Bhatt},
year={2021},
eprint={2112.09866},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |
bhavikardeshna/xlm-roberta-base-german | bhavikardeshna | 2021-12-21T11:40:35Z | 7 | 0 | transformers | [
"transformers",
"pytorch",
"xlm-roberta",
"question-answering",
"arxiv:2112.09866",
"endpoints_compatible",
"region:us"
] | question-answering | 2022-03-02T23:29:05Z | # BibTeX entry and citation info
```
@misc{pandya2021cascading,
title={Cascading Adaptors to Leverage English Data to Improve Performance of Question Answering for Low-Resource Languages},
author={Hariom A. Pandya and Bhavik Ardeshna and Dr. Brijesh S. Bhatt},
year={2021},
eprint={2112.09866},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |
bhavikardeshna/xlm-roberta-base-spanish | bhavikardeshna | 2021-12-21T11:39:52Z | 6 | 0 | transformers | [
"transformers",
"pytorch",
"xlm-roberta",
"question-answering",
"arxiv:2112.09866",
"endpoints_compatible",
"region:us"
] | question-answering | 2022-03-02T23:29:05Z | # BibTeX entry and citation info
```
@misc{pandya2021cascading,
title={Cascading Adaptors to Leverage English Data to Improve Performance of Question Answering for Low-Resource Languages},
author={Hariom A. Pandya and Bhavik Ardeshna and Dr. Brijesh S. Bhatt},
year={2021},
eprint={2112.09866},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |
patrickvonplaten/xls-r-300m-tr-phoneme | patrickvonplaten | 2021-12-21T11:13:30Z | 7 | 3 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_3_0",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2022-03-02T23:29:05Z | ---
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_3_0
- generated_from_trainer
model-index:
- name: xls-r-300m-tr-phoneme
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-300m-tr-phoneme
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the mozilla-foundation/common_voice_3_0 - TR dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4378
- Wer: 0.09936
## 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: 0.000075
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 32
- total_eval_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: 150
- mixed_precision_training: Native AMP
### Training results
See Training Metrics Tab.
### Framework versions
- Transformers 4.15.0.dev0
- Pytorch 1.9.0+cu111
- Datasets 1.16.2.dev0
- Tokenizers 0.10.3
|
NbAiLabArchive/test_w5_long_dataset | NbAiLabArchive | 2021-12-21T08:30:00Z | 28 | 0 | transformers | [
"transformers",
"pytorch",
"jax",
"tensorboard",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2022-03-02T23:29:04Z | Just for performing some experiments. Do not use. |
kwang1993/wav2vec2-base-timit-demo | kwang1993 | 2021-12-21T04:54:44Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2022-03-02T23:29:05Z | https://huggingface.co/blog/fine-tune-wav2vec2-english
Use the processor from https://huggingface.co/facebook/wav2vec2-base |
vuiseng9/pegasus-arxiv | vuiseng9 | 2021-12-21T02:23:21Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-03-02T23:29:05Z | This model is developed with transformers v4.13 with minor patch in this [fork](https://github.com/vuiseng9/transformers/tree/pegasus-v4p13).
# Setup
```bash
git clone https://github.com/vuiseng9/transformers
cd transformers
git checkout pegasus-v4p13 && git reset --hard 41eeb07
# installation, set summarization dependency
# . . .
```
# Train
```bash
#!/usr/bin/env bash
export CUDA_VISIBLE_DEVICES=0,1,2,3
NEPOCH=10
RUNID=pegasus-arxiv-${NEPOCH}eph-run1
OUTDIR=/data1/vchua/pegasus-hf4p13/pegasus-ft/${RUNID}
mkdir -p $OUTDIR
python run_summarization.py \
--model_name_or_path google/pegasus-large \
--dataset_name ccdv/arxiv-summarization \
--do_train \
--adafactor \
--learning_rate 8e-4 \
--label_smoothing_factor 0.1 \
--num_train_epochs $NEPOCH \
--per_device_train_batch_size 2 \
--do_eval \
--per_device_eval_batch_size 2 \
--num_beams 8 \
--max_source_length 1024 \
--max_target_length 256 \
--evaluation_strategy steps \
--eval_steps 10000 \
--save_strategy steps \
--save_steps 5000 \
--logging_steps 1 \
--overwrite_output_dir \
--run_name $RUNID \
--output_dir $OUTDIR > $OUTDIR/run.log 2>&1 &
```
# Eval
```bash
#!/usr/bin/env bash
export CUDA_VISIBLE_DEVICES=3
DT=$(date +%F_%H-%M)
RUNID=pegasus-arxiv-${DT}
OUTDIR=/data1/vchua/pegasus-hf4p13/pegasus-eval/${RUNID}
mkdir -p $OUTDIR
python run_summarization.py \
--model_name_or_path vuiseng9/pegasus-arxiv \
--dataset_name ccdv/arxiv-summarization \
--max_source_length 1024 \
--max_target_length 256 \
--do_predict \
--per_device_eval_batch_size 8 \
--predict_with_generate \
--num_beams 8 \
--overwrite_output_dir \
--run_name $RUNID \
--output_dir $OUTDIR > $OUTDIR/run.log 2>&1 &
```
Although fine-tuning is carried out for 5 epochs, this model is the checkpoint @150000 steps, 5.91 epoch, 34hrs) with lowest eval loss during training. Test/predict with this checkpoint should give results below. Note that we observe model at 80000 steps is closed to published result from HF.
```
***** predict metrics *****
predict_gen_len = 210.0925
predict_loss = 1.7192
predict_rouge1 = 46.1383
predict_rouge2 = 19.1393
predict_rougeL = 27.7573
predict_rougeLsum = 41.583
predict_runtime = 2:40:25.86
predict_samples = 6440
predict_samples_per_second = 0.669
predict_steps_per_second = 0.084
``` |
vuiseng9/pegasus-billsum | vuiseng9 | 2021-12-21T01:41:33Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-03-02T23:29:05Z | This model is developed with transformers v4.13 with minor patch in this [fork](https://github.com/vuiseng9/transformers/tree/pegasus-v4p13).
# Setup
```bash
git clone https://github.com/vuiseng9/transformers
cd transformers
git checkout pegasus-v4p13 && git reset --hard 41eeb07
# installation, set summarization dependency
# . . .
```
# Train
```bash
#!/usr/bin/env bash
export CUDA_VISIBLE_DEVICES=0,1,2,3
NEPOCH=10
RUNID=pegasus-billsum-${NEPOCH}eph-run1
OUTDIR=/data1/vchua/pegasus-hf4p13/pegasus/${RUNID}
mkdir -p $OUTDIR
nohup python run_summarization.py \
--model_name_or_path google/pegasus-large \
--dataset_name billsum \
--do_train \
--adafactor \
--learning_rate 2e-4 \
--label_smoothing_factor 0.1 \
--num_train_epochs $NEPOCH \
--per_device_train_batch_size 2 \
--do_eval \
--per_device_eval_batch_size 2 \
--num_beams 8 \
--max_source_length 1024 \
--max_target_length 256 \
--evaluation_strategy steps \
--eval_steps 1000 \
--save_strategy steps \
--save_steps 2000 \
--logging_steps 1 \
--overwrite_output_dir \
--run_name $RUNID \
--output_dir $OUTDIR > $OUTDIR/run.log 2>&1 &
```
# Eval
```bash
#!/usr/bin/env bash
export CUDA_VISIBLE_DEVICES=3
DT=$(date +%F_%H-%M)
RUNID=pegasus-billsum-${DT}
OUTDIR=/data1/vchua/pegasus-hf4p13/pegasus-test/${RUNID}
mkdir -p $OUTDIR
nohup python run_summarization.py \
--model_name_or_path vuiseng9/pegasus-billsum \
--dataset_name billsum \
--max_source_length 1024 \
--max_target_length 256 \
--do_predict \
--per_device_eval_batch_size 8 \
--predict_with_generate \
--num_beams 8 \
--overwrite_output_dir \
--run_name $RUNID \
--output_dir $OUTDIR > $OUTDIR/run.log 2>&1 &
```
Although fine-tuning is carried out for 10 epochs, this model is the checkpoint (@12000 steps, 6.6epoch, 210mins) with lowest eval loss during training. Test/predict with this checkpoint should give results below.
```
***** predict metrics *****
predict_gen_len = 179.7363
predict_loss = 1.2452
predict_rouge1 = 56.8657
predict_rouge2 = 38.6531
predict_rougeL = 44.8399
predict_rougeLsum = 51.6266
predict_runtime = 1:19:28.20
predict_samples = 3269
predict_samples_per_second = 0.686
predict_steps_per_second = 0.086
``` |
patrickvonplaten/wavlm-libri-clean-100h-base | patrickvonplaten | 2021-12-20T12:59:09Z | 7,849 | 1 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"wavlm",
"automatic-speech-recognition",
"librispeech_asr",
"generated_from_trainer",
"wavlm_libri_finetune",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2022-03-02T23:29:05Z | ---
tags:
- automatic-speech-recognition
- librispeech_asr
- generated_from_trainer
- wavlm_libri_finetune
model-index:
- name: wavlm-libri-clean-100h-base
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. -->
# wavlm-libri-clean-100h-base
This model is a fine-tuned version of [microsoft/wavlm-base](https://huggingface.co/microsoft/wavlm-base) on the LIBRISPEECH_ASR - CLEAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0829
- Wer: 0.0675
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 32
- total_eval_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: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 2.8805 | 0.34 | 300 | 2.8686 | 1.0 |
| 0.2459 | 0.67 | 600 | 0.1858 | 0.1554 |
| 0.1114 | 1.01 | 900 | 0.1379 | 0.1191 |
| 0.0867 | 1.35 | 1200 | 0.1130 | 0.0961 |
| 0.0698 | 1.68 | 1500 | 0.1032 | 0.0877 |
| 0.0663 | 2.02 | 1800 | 0.0959 | 0.0785 |
| 0.0451 | 2.35 | 2100 | 0.0887 | 0.0748 |
| 0.0392 | 2.69 | 2400 | 0.0859 | 0.0698 |
### Framework versions
- Transformers 4.15.0.dev0
- Pytorch 1.9.0+cu111
- Datasets 1.16.2.dev0
- Tokenizers 0.10.3
|
patrickvonplaten/wav2vec2-common_voice-tr-demo-dist | patrickvonplaten | 2021-12-20T12:54:17Z | 13 | 2 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"speech-recognition",
"common_voice",
"generated_from_trainer",
"tr",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2022-03-02T23:29:05Z | ---
language:
- tr
license: apache-2.0
tags:
- speech-recognition
- common_voice
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-common_voice-tr-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-common_voice-tr-demo-dist
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the COMMON_VOICE - TR dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3856
- Wer: 0.3581
- Cer: 0.0805
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 4
- num_gpus: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 1
- 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: 15.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.7391 | 0.92 | 100 | 3.5760 | 1.0 |
| 2.927 | 1.83 | 200 | 3.0796 | 0.9999 |
| 0.9009 | 2.75 | 300 | 0.9278 | 0.8226 |
| 0.6529 | 3.67 | 400 | 0.5926 | 0.6367 |
| 0.3623 | 4.59 | 500 | 0.5372 | 0.5692 |
| 0.2888 | 5.5 | 600 | 0.4407 | 0.4838 |
| 0.285 | 6.42 | 700 | 0.4341 | 0.4694 |
| 0.0842 | 7.34 | 800 | 0.4153 | 0.4302 |
| 0.1415 | 8.26 | 900 | 0.4317 | 0.4136 |
| 0.1552 | 9.17 | 1000 | 0.4145 | 0.4013 |
| 0.1184 | 10.09 | 1100 | 0.4115 | 0.3844 |
| 0.0556 | 11.01 | 1200 | 0.4182 | 0.3862 |
| 0.0851 | 11.93 | 1300 | 0.3985 | 0.3688 |
| 0.0961 | 12.84 | 1400 | 0.4030 | 0.3665 |
| 0.0596 | 13.76 | 1500 | 0.3880 | 0.3631 |
| 0.0359 | 14.68 | 1600 | 0.3878 | 0.3589 |
### Framework versions
- Transformers 4.11.0.dev0
- Pytorch 1.9.0+cu111
- Datasets 1.12.1
- Tokenizers 0.10.3
|
patrickvonplaten/hubert-librispeech-clean-100h-demo-dist | patrickvonplaten | 2021-12-20T12:53:35Z | 10 | 1 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"hubert",
"automatic-speech-recognition",
"speech-recognition",
"librispeech_asr",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2022-03-02T23:29:05Z | ---
license: apache-2.0
tags:
- speech-recognition
- librispeech_asr
- generated_from_trainer
model-index:
- name: hubert-librispeech-clean-100h-demo-dist
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. -->
# hubert-librispeech-clean-100h-demo-dist
This model is a fine-tuned version of [facebook/hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k) on the LIBRISPEECH_ASR - CLEAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0984
- Wer: 0.0883
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 32
- total_eval_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: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 2.9031 | 0.11 | 100 | 2.9220 | 1.0 |
| 2.6437 | 0.22 | 200 | 2.6268 | 1.0 |
| 0.3934 | 0.34 | 300 | 0.4860 | 0.4182 |
| 0.3531 | 0.45 | 400 | 0.3088 | 0.2894 |
| 0.2255 | 0.56 | 500 | 0.2568 | 0.2426 |
| 0.3379 | 0.67 | 600 | 0.2073 | 0.2011 |
| 0.2419 | 0.78 | 700 | 0.1849 | 0.1838 |
| 0.2128 | 0.9 | 800 | 0.1662 | 0.1690 |
| 0.1341 | 1.01 | 900 | 0.1600 | 0.1541 |
| 0.0946 | 1.12 | 1000 | 0.1431 | 0.1404 |
| 0.1643 | 1.23 | 1100 | 0.1373 | 0.1304 |
| 0.0663 | 1.35 | 1200 | 0.1293 | 0.1307 |
| 0.162 | 1.46 | 1300 | 0.1247 | 0.1266 |
| 0.1433 | 1.57 | 1400 | 0.1246 | 0.1262 |
| 0.1581 | 1.68 | 1500 | 0.1219 | 0.1154 |
| 0.1036 | 1.79 | 1600 | 0.1127 | 0.1081 |
| 0.1352 | 1.91 | 1700 | 0.1087 | 0.1040 |
| 0.0471 | 2.02 | 1800 | 0.1085 | 0.1005 |
| 0.0945 | 2.13 | 1900 | 0.1066 | 0.0973 |
| 0.0843 | 2.24 | 2000 | 0.1102 | 0.0964 |
| 0.0774 | 2.35 | 2100 | 0.1079 | 0.0940 |
| 0.0952 | 2.47 | 2200 | 0.1056 | 0.0927 |
| 0.0635 | 2.58 | 2300 | 0.1026 | 0.0920 |
| 0.0665 | 2.69 | 2400 | 0.1012 | 0.0905 |
| 0.034 | 2.8 | 2500 | 0.1009 | 0.0900 |
| 0.0251 | 2.91 | 2600 | 0.0993 | 0.0883 |
### Framework versions
- Transformers 4.11.0.dev0
- Pytorch 1.9.0+cu111
- Datasets 1.12.1
- Tokenizers 0.10.3
|
Alerosae/SocratesGPT-2 | Alerosae | 2021-12-20T12:36:38Z | 16 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"feature-extraction",
"text-generation",
"en",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:04Z | ---
language: "en"
tags:
- text-generation
pipeline_tag: text-generation
widget:
- text: "The Gods"
- text: "What is"
---
This is a fine-tuned version of GPT-2, trained with the entire corpus of Plato's works. By generating text samples you should be able to generate ancient Greek philosophy on the fly!
|
austin/adr-ner | austin | 2021-12-20T06:48:11Z | 8 | 0 | transformers | [
"transformers",
"pytorch",
"deberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2022-03-02T23:29:05Z | ---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: adr-ner
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# adr-ner
This model is a fine-tuned version of [austin/Austin-MeDeBERTa](https://huggingface.co/austin/Austin-MeDeBERTa) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0434
- Precision: 0.7305
- Recall: 0.6934
- F1: 0.7115
- Accuracy: 0.9941
## 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: 12
- eval_batch_size: 12
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 107 | 0.0630 | 0.0 | 0.0 | 0.0 | 0.9876 |
| No log | 2.0 | 214 | 0.0308 | 0.4282 | 0.3467 | 0.3832 | 0.9900 |
| No log | 3.0 | 321 | 0.0254 | 0.5544 | 0.5603 | 0.5573 | 0.9920 |
| No log | 4.0 | 428 | 0.0280 | 0.6430 | 0.5751 | 0.6071 | 0.9929 |
| 0.0465 | 5.0 | 535 | 0.0266 | 0.5348 | 0.7146 | 0.6118 | 0.9915 |
| 0.0465 | 6.0 | 642 | 0.0423 | 0.7632 | 0.5793 | 0.6587 | 0.9939 |
| 0.0465 | 7.0 | 749 | 0.0336 | 0.6957 | 0.6765 | 0.6860 | 0.9939 |
| 0.0465 | 8.0 | 856 | 0.0370 | 0.6876 | 0.6702 | 0.6788 | 0.9936 |
| 0.0465 | 9.0 | 963 | 0.0349 | 0.6555 | 0.7040 | 0.6789 | 0.9932 |
| 0.0044 | 10.0 | 1070 | 0.0403 | 0.6910 | 0.6808 | 0.6858 | 0.9938 |
| 0.0044 | 11.0 | 1177 | 0.0415 | 0.7140 | 0.6808 | 0.6970 | 0.9939 |
| 0.0044 | 12.0 | 1284 | 0.0440 | 0.7349 | 0.6681 | 0.6999 | 0.9941 |
| 0.0044 | 13.0 | 1391 | 0.0423 | 0.7097 | 0.6977 | 0.7036 | 0.9941 |
| 0.0044 | 14.0 | 1498 | 0.0435 | 0.7174 | 0.6977 | 0.7074 | 0.9941 |
| 0.0006 | 15.0 | 1605 | 0.0434 | 0.7305 | 0.6934 | 0.7115 | 0.9941 |
### Framework versions
- Transformers 4.14.1
- Pytorch 1.10.0+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|
Amalq/roberta-base-finetuned-schizophreniaReddit2 | Amalq | 2021-12-20T05:41:28Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2022-03-02T23:29:04Z | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: roberta-base-finetuned-schizophreniaReddit2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-finetuned-schizophreniaReddit2
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7785
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 490 | 1.8093 |
| 1.9343 | 2.0 | 980 | 1.7996 |
| 1.8856 | 3.0 | 1470 | 1.7966 |
| 1.8552 | 4.0 | 1960 | 1.7844 |
| 1.8267 | 5.0 | 2450 | 1.7839 |
### Framework versions
- Transformers 4.14.1
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
rockmiin/ko-boolq-model | rockmiin | 2021-12-20T02:42:43Z | 5 | 2 | transformers | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-02T23:29:05Z | labeled by "YES" : 1, "NO" : 0, "No Answer" : 2
fine tuned by klue/roberta-large |
anelnurkayeva/autonlp-covid-432211280 | anelnurkayeva | 2021-12-20T01:23:47Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"autonlp",
"en",
"dataset:anelnurkayeva/autonlp-data-covid",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-02T23:29:05Z | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- anelnurkayeva/autonlp-data-covid
co2_eq_emissions: 8.898145050355591
---
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 432211280
- CO2 Emissions (in grams): 8.898145050355591
## Validation Metrics
- Loss: 0.12489336729049683
- Accuracy: 0.9520089285714286
- Precision: 0.9436443331246086
- Recall: 0.9747736093143596
- AUC: 0.9910066767410616
- F1: 0.958956411072224
## 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 AutoNLP"}' https://api-inference.huggingface.co/models/anelnurkayeva/autonlp-covid-432211280
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("anelnurkayeva/autonlp-covid-432211280", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("anelnurkayeva/autonlp-covid-432211280", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
``` |
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