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seduerr/pai_emotion | 07cfba18cc3dce3fa8d6a08d0ea7632e19355fd1 | 2021-06-23T14:13:09.000Z | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | seduerr | null | seduerr/pai_emotion | 0 | null | transformers | Entry not found | [
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seduerr/pai_exem | 077394fe81b791fd2f00f4f0b61bcf64863a0842 | 2021-01-27T07:01:57.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | seduerr | null | seduerr/pai_exem | 0 | null | transformers | Entry not found | [
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seduerr/pai_f2m | 5d8c860c44387d76d107597cabca3266a06139c4 | 2021-06-23T14:13:46.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | seduerr | null | seduerr/pai_f2m | 0 | null | transformers | Entry not found | [
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seduerr/pai_joke | ed7c3b06eb1b2516da76b93fa8fe40f37cbf9f00 | 2021-05-23T12:50:42.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | seduerr | null | seduerr/pai_joke | 0 | null | transformers | Entry not found | [
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seduerr/pai_pos2neg | 7a39bb1cbded97f73b1caa4d69aaedfb5b5edaa9 | 2021-06-23T14:16:37.000Z | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | seduerr | null | seduerr/pai_pos2neg | 0 | null | transformers | Entry not found | [
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seduerr/sentiment | 4ca5ae72ae3c86159308b057ecae8fc04e4aaf3e | 2021-01-12T10:50:07.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | seduerr | null | seduerr/sentiment | 0 | null | transformers | hello
hello
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] |
sehandev/koelectra-long-qa | a8cf43cc9d290c8aa2b34de39f991a67d618c68e | 2021-07-18T06:01:25.000Z | [
"pytorch",
"electra",
"question-answering",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | question-answering | false | sehandev | null | sehandev/koelectra-long-qa | 0 | null | transformers | ---
tags:
- generated_from_trainer
model_index:
- name: koelectra-long-qa
results:
- task:
name: Question Answering
type: question-answering
---
<!-- 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-long-qa
This model is a fine-tuned version of [monologg/koelectra-base-v3-discriminator](https://huggingface.co/monologg/koelectra-base-v3-discriminator) on an unkown 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: 64
- eval_batch_size: 256
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.8.2
- Pytorch 1.8.1
- Datasets 1.9.0
- Tokenizers 0.10.3
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seidel/plsum-base-ptt5 | 6d38e8aab290fc2dfd09e146e957accc70bc8a85 | 2022-04-28T16:59:49.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | seidel | null | seidel/plsum-base-ptt5 | 0 | 1 | transformers | # Abstractive stage of PLSUM
Abstractive stage of the Multi-document Extractive Summarization (MDAS) model for portuguese, PLSUM. To goal here is to create Wikipedia-like summaries from multiple sentences extracted in the previous stage of PLSUM (the extractive stage) from websites (input and output in portuguese).
Project [github](https://github.com/aseidelo/wiki_generator/tree/cdd38918c2070200595b7cc64013d6d9ae4eddd0), and [paper](https://sol.sbc.org.br/index.php/eniac/article/view/18300).
## Usage
```
# query: summary title
query = 'torta de limão'
# sentences: list of relevant sentences extracted from multiple documents (i.e via TF-IDF or Textrank or anyother extractive summarization model)
sentences = [
'apostar na união do doce com o azedinho da torta de limão é quase certeza de acertar na sobremesa. E você pode escolher a forma mais tradicional com uma massa crocante de farinha de trigo, ou dar um toque de sofisticação servindo porções em taças individuais .',
'uma fruta no ponto e suculenta faz toda a diferença no preparo de qualquer receita . por isso , aqui vão algumas dicas para escolher o limão ideal para fazer a torta de limão perfeita . observe bem a casca. Uma casca lisa mostra que o limão está suculento . ela também precisa ser bem verde e brilhante ; preste atenção à maciez . aperte a fruta suavemente , se ele ceder ao toque é porque está macio e no ponto para ser consumido ; atenção para a firmeza . mesmo sendo macio , o limão precisa ser firme .',
'tudo indica que a torta de limão nasceu nos estados unidos , na cidade de Key West , no estado da Flórida , a fins do século xix. por isso , o nome original da receita em inglês – key lime pie – seria originário do nome da cidade e do limão usado naquela região , bem semelhante ao limão taiti consumido no brasil , mas com uma casca amarelada .',
'as tortas têm a massa como base que podem se estender pelas laterais da sobremesa e até por cima , parecida com uma crosta , mais crocante ou cremosa de acordo com os ingredientes utilizados . podem ser feitas com biscoitos doces com manteiga derretida , ou com uma mistura de farinha , sal , açúcar , manteiga derretida , gema e água . a massa da torta também pode ser feita com um bolo , e a partir daí se estrutura a torta . ',
'as tortas , geralmente precisam ficar no forno a 200 ° c , por cerca de 20 a 40 minutos . dependendo de cada tipo de forno , o tempo pode variar',
'para fazer uma massa de torta quase sempre é usada uma gordura como base , geralmente a manteiga . tem a gordura , a farinha , o trigo e às vezes , ovos na sua composição . durante o processo não pode incorporar calor e nem desenvolver o glúten em excesso , pois queremos como resultado uma massa que se dissolve na boca .'
]
input_text = 'summarize: {}'.format(query) + sentences.join('</s>')
# input_text = "summarize: torta de limão </s> apostar na união do doce com o azedinho da torta de limão é quase certeza de acertar na sobremesa. E você pode escolher a forma mais tradicional com uma massa crocante de farinha de trigo, ou dar um toque de sofisticação servindo porções em taças individuais. </s> uma fruta no ponto e suculenta faz toda a diferença no preparo de qualquer receita . por isso , aqui vão algumas dicas para escolher o limão ideal para fazer a torta de limão perfeita . observe bem a casca. Uma casca lisa mostra que o limão está suculento . ela também precisa ser bem verde e brilhante ; preste atenção à maciez . aperte a fruta suavemente , se ele ceder ao toque é porque está macio e no ponto para ser consumido ; atenção para a firmeza . mesmo sendo macio , o limão precisa ser firme . </s> tudo indica que a torta de limão nasceu nos estados unidos , na cidade de Key West , no estado da Flórida , a fins do século xix. por isso , o nome original da receita em inglês – key lime pie – seria originário do nome da cidade e do limão usado naquela região , bem semelhante ao limão taiti consumido no brasil , mas com uma casca amarelada . </s> as tortas têm a massa como base que podem se estender pelas laterais da sobremesa e até por cima , parecida com uma crosta , mais crocante ou cremosa de acordo com os ingredientes utilizados . podem ser feitas com biscoitos doces com manteiga derretida , ou com uma mistura de farinha , sal , açúcar , manteiga derretida , gema e água . a massa da torta também pode ser feita com um bolo , e a partir daí se estrutura a torta . </s> as tortas , geralmente precisam ficar no forno a 200 ° c , por cerca de 20 a 40 minutos . dependendo de cada tipo de forno , o tempo pode variar . </s> para fazer uma massa de torta quase sempre é usada uma gordura como base , geralmente a manteiga . tem a gordura , a farinha , o trigo e às vezes , ovos na sua composição . durante o processo não pode incorporar calor e nem desenvolver o glúten em excesso , pois queremos como resultado uma massa que se dissolve na boca ."
tokenizer = T5TokenizerFast.from_pretrained("seidel/plsum-base-ptt5")
model = T5ForConditionalGeneration.from_pretrained("seidel/plsum-base-ptt5", use_cache=False)
x = tokenizer([input_text], padding="max_length", max_length=512, return_tensors="pt", truncation=True)
y = model.generate(**x)
print(tokenizer.batch_decode(y, skip_special_tokens=True))
# output: a torta de limão é um doce feito com a fruta limão , que é uma mistura de farinha de trigo , sal , açúcar , manteiga derretida , gema e água . a massa da torta pode ser feita com biscoitos doces , biscoitinhos ou bolos . é uma receita tradicional dos estados unidos , com a utilização de uma massa crocante , ou ainda com um bolo .
```
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sello-ralethe/roberta-base-frozen-generics-mlm | 0059ca2fc12c85337dbc0826af05af2c224e4876 | 2021-05-20T20:08:48.000Z | [
"pytorch",
"jax",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | sello-ralethe | null | sello-ralethe/roberta-base-frozen-generics-mlm | 0 | null | transformers | Entry not found | [
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sergiyvl/first_try_RuBERT_200_16_16_10ep | 3dcb4a4172870ead5d2fbcb6264b482c0f7007ea | 2021-05-20T05:34:45.000Z | [
"pytorch",
"jax",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | sergiyvl | null | sergiyvl/first_try_RuBERT_200_16_16_10ep | 0 | null | transformers | Entry not found | [
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sergiyvl/first_try_RuBERT_200_16_16_25ep | e6796aafb6527634e2806e9c71a91e35a47a7df6 | 2021-05-20T05:36:09.000Z | [
"pytorch",
"jax",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | sergiyvl | null | sergiyvl/first_try_RuBERT_200_16_16_25ep | 0 | null | transformers | Entry not found | [
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setiadia/DialogGPT-small-HPBot | 39c89bfa8e1c84d493f9c15bdbe2bfe3ba6c478a | 2021-10-28T09:48:50.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | setiadia | null | setiadia/DialogGPT-small-HPBot | 0 | null | transformers | ---
tags:
- conversational
---
# Harry Potter DialogGPT Model | [
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] |
seyonec/BPE_SELFIES_PubChem_shard00_166_5k | 89fed69a1c6f3d833a8a2ef464521d4fc2a52c65 | 2021-05-20T20:46:50.000Z | [
"pytorch",
"jax",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | seyonec | null | seyonec/BPE_SELFIES_PubChem_shard00_166_5k | 0 | null | transformers | Entry not found | [
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seyonec/BPE_SELFIES_PubChem_shard00_70k | 56e53d53b04926df17fbc6cbe165fee6cd77f175 | 2021-05-20T20:49:05.000Z | [
"pytorch",
"jax",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | seyonec | null | seyonec/BPE_SELFIES_PubChem_shard00_70k | 0 | null | transformers | Entry not found | [
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seyonec/ChemBERTA_PubChem1M_shard00_125k | fb6ab956c93c7fd53e81218a8024cc68f80ce786 | 2021-05-20T20:52:31.000Z | [
"pytorch",
"jax",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | seyonec | null | seyonec/ChemBERTA_PubChem1M_shard00_125k | 0 | null | transformers | Entry not found | [
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seyonec/ChemBERTa_masked_30_PubChem_shard00_1M_150k_steps | e2dc5a368e7b0e0036e77c8d6b4b7b43f39ad125 | 2021-05-20T20:56:58.000Z | [
"pytorch",
"jax",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | seyonec | null | seyonec/ChemBERTa_masked_30_PubChem_shard00_1M_150k_steps | 0 | null | transformers | Entry not found | [
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sgich/bert_case_uncased_KenyaHateSpeech | b446836722e0f0ef59662c34e8288cf31406be8f | 2021-04-25T19:12:40.000Z | [
"pytorch",
"text-classification"
] | text-classification | false | sgich | null | sgich/bert_case_uncased_KenyaHateSpeech | 0 | null | null | # HateSpeechDetection
---
pipeline_tag: text-classification
---
The model is used for classifying a text as Hatespeech or Normal. The model is trained using data from Twitter, specifically Kenyan related tweets. To maximize on the limited dataset, text augmentation was done.
The dataset is available here: https://github.com/sgich/HateSpeechDetection
Using a pre-trained "bert-base-uncased" transformer model, adding a dropout layer, a linear output layer and adding 10 common emojis that may be related to either Hate or Normal Speech. Then the model was tuned on a dataset of Kenyan/Kenyan-related scraped tweets with the purpose of performing text classification of "Normal Speech" or "Hate Speech" based on the text. This model was the result of realizing that majority of similar models did not cater for the African context where the target groups are not based on race and/or religious affiliation but mostly tribal differences which has proved fatal in the past.
The model can be improved greatly by using a large and representative dataset and optimization of the model to a better degree.
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shauryr/arqmath-roberta-base-1.5M | 61b124433a65c2cf471d3b167665eb69f7f4e9ec | 2021-05-20T21:13:26.000Z | [
"pytorch",
"jax",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | shauryr | null | shauryr/arqmath-roberta-base-1.5M | 0 | null | transformers | Entry not found | [
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shauryr/arqmath-roberta-base-2M | 7e7c87e34113df7e6cab8b44607083ccb5bf1d2a | 2021-05-20T21:15:07.000Z | [
"pytorch",
"jax",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | shauryr | null | shauryr/arqmath-roberta-base-2M | 0 | null | transformers | Entry not found | [
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shauryr/arqmath-roberta-base | 5e9ce9a5abb24b0bcdbfac5e6ee591474c75958b | 2021-05-20T21:16:05.000Z | [
"pytorch",
"jax",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | shauryr | null | shauryr/arqmath-roberta-base | 0 | null | transformers | Entry not found | [
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shields/repo_name | 8d7fd97d00ffc0f399663a177ff15c889fd5f8ba | 2022-01-28T02:09:21.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"transformers"
] | automatic-speech-recognition | false | shields | null | shields/repo_name | 0 | null | transformers | Entry not found | [
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] |
shihab/HarryPotter | 7f4386fd5236aff9f09c4bb0004853c676fd048d | 2021-10-08T10:44:08.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | shihab | null | shihab/HarryPotter | 0 | null | transformers | ---
tags:
- conversational
---
# Harry Potter DialoGPT Model | [
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shiqing/opus-mt-en-zh-finetuned-en-to-zh | 6895591a237d7ed033db20d00d59b93b4a89cf91 | 2021-10-29T08:38:40.000Z | [
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | shiqing | null | shiqing/opus-mt-en-zh-finetuned-en-to-zh | 0 | null | transformers | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: opus-mt-en-zh-finetuned-en-to-zh
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. -->
# opus-mt-en-zh-finetuned-en-to-zh
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-zh](https://huggingface.co/Helsinki-NLP/opus-mt-en-zh) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|
| No log | 1.0 | 10 | 4.0166 | 1.3628 | 416.6867 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cpu
- Datasets 1.14.0
- Tokenizers 0.10.3
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shmuelamar/REQA-RoBERTa | 6e1bd8b2bf55f00d40bc359cf0fb03aa7c586050 | 2021-05-20T21:19:31.000Z | [
"pytorch",
"jax",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | shmuelamar | null | shmuelamar/REQA-RoBERTa | 0 | null | transformers | # REQA-RoBERTa
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shoarora/electra-small-owt | b47d5a55674d89b37b0193abada8bdef4db34da4 | 2021-05-20T05:54:08.000Z | [
"pytorch",
"jax",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | false | shoarora | null | shoarora/electra-small-owt | 0 | null | transformers | # ELECTRA-small-OWT
This is an unnoficial implementation of an
[ELECTRA](https://openreview.net/forum?id=r1xMH1BtvB) small model, trained on the
[OpenWebText corpus](https://skylion007.github.io/OpenWebTextCorpus/).
Differences from official ELECTRA models:
- we use a `BertForMaskedLM` as the generator and `BertForTokenClassification` as the discriminator
- they use an embedding projection layer, but Bert doesn't have one
## Pretraining ttask
(figure from [Clark et al. 2020](https://openreview.net/pdf?id=r1xMH1BtvB))
ELECTRA uses discriminative LM / replaced-token-detection for pretraining.
This involves a generator (a Masked LM model) creating examples for a discriminator
to classify as original or replaced for each token.
## Usage
```python
from transformers import BertForSequenceClassification, BertTokenizer
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
electra = BertForSequenceClassification.from_pretrained('shoarora/electra-small-owt')
```
## Code
The pytorch module that implements this task is available [here](https://github.com/shoarora/lmtuners/blob/master/lmtuners/lightning_modules/discriminative_lm.py).
Further implementation information [here](https://github.com/shoarora/lmtuners/tree/master/experiments/disc_lm_small),
and [here](https://github.com/shoarora/lmtuners/blob/master/experiments/disc_lm_small/train_electra_small.py) is the script that created this model.
This specific model was trained with the following params:
- `batch_size: 512`
- `training_steps: 5e5`
- `warmup_steps: 4e4`
- `learning_rate: 2e-3`
## Downstream tasks
#### GLUE Dev results
| Model | # Params | CoLA | SST | MRPC | STS | QQP | MNLI | QNLI | RTE |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| ELECTRA-Small++ | 14M | 57.0 | 91. | 88.0 | 87.5 | 89.0 | 81.3 | 88.4 | 66.7|
| ELECTRA-Small-OWT | 14M | 56.8 | 88.3| 87.4 | 86.8 | 88.3 | 78.9 | 87.9 | 68.5|
| ELECTRA-Small-OWT (ours) | 17M | 56.3 | 88.4| 75.0 | 86.1 | 89.1 | 77.9 | 83.0 | 67.1|
| ALECTRA-Small-OWT (ours) | 4M | 50.6 | 89.1| 86.3 | 87.2 | 89.1 | 78.2 | 85.9 | 69.6|
- Table initialized from [ELECTRA github repo](https://github.com/google-research/electra)
#### GLUE Test results
| Model | # Params | CoLA | SST | MRPC | STS | QQP | MNLI | QNLI | RTE |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| BERT-Base | 110M | 52.1 | 93.5| 84.8 | 85.9 | 89.2 | 84.6 | 90.5 | 66.4|
| GPT | 117M | 45.4 | 91.3| 75.7 | 80.0 | 88.5 | 82.1 | 88.1 | 56.0|
| ELECTRA-Small++ | 14M | 57.0 | 91.2| 88.0 | 87.5 | 89.0 | 81.3 | 88.4 | 66.7|
| ELECTRA-Small-OWT (ours) | 17M | 57.4 | 89.3| 76.2 | 81.9 | 87.5 | 78.1 | 82.4 | 68.1|
| ALECTRA-Small-OWT (ours) | 4M | 43.9 | 87.9| 82.1 | 82.0 | 87.6 | 77.9 | 85.8 | 67.5|
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shonenkov/rudalle-Malevich | 75e4f71ff506fe28b2c44efca3d61bfb58a9295f | 2021-11-01T11:09:30.000Z | [
"pytorch"
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shoubhik/wav2vec2-lm-ch | a09f4387e5bface2f247b3e70810e4f5f68e73f2 | 2022-01-26T14:08:53.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"transformers"
] | automatic-speech-recognition | false | shoubhik | null | shoubhik/wav2vec2-lm-ch | 0 | null | transformers | Entry not found | [
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shoubhik/wav2vec2-xls-r-300m-hindi_V2 | 450f86b047c7816e2f584fde089529fdd20882b9 | 2022-02-05T18:48:37.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"transformers"
] | automatic-speech-recognition | false | shoubhik | null | shoubhik/wav2vec2-xls-r-300m-hindi_V2 | 0 | null | transformers | Entry not found | [
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sibckukgvaxsepbkyb/IndoBARTv2-SQuAD-AQG | 51eb8c4a8483ea43d4932d452535e2b4c80b9b84 | 2022-02-17T19:37:54.000Z | [
"pytorch",
"mbart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | sibckukgvaxsepbkyb | null | sibckukgvaxsepbkyb/IndoBARTv2-SQuAD-AQG | 0 | null | transformers | Entry not found | [
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sibckukgvaxsepbkyb/IndoBARTv2-TyDiQA-AQG | 524ed16c472eb31ed544a473472ac3db954844b0 | 2022-02-17T08:22:40.000Z | [
"pytorch",
"mbart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | sibckukgvaxsepbkyb | null | sibckukgvaxsepbkyb/IndoBARTv2-TyDiQA-AQG | 0 | null | transformers | Entry not found | [
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sibckukgvaxsepbkyb/IndoGPT-SQuAD-5 | 8e14b90eb1fce842295c7549f9c6087d9965e571 | 2022-02-19T14:57:18.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | sibckukgvaxsepbkyb | null | sibckukgvaxsepbkyb/IndoGPT-SQuAD-5 | 0 | null | transformers | Entry not found | [
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sibckukgvaxsepbkyb/mT5IndoQG | 9223d67e1032065d156c5afcba67f25e4541889d | 2022-01-26T20:31:06.000Z | [
"pytorch",
"mt5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | sibckukgvaxsepbkyb | null | sibckukgvaxsepbkyb/mT5IndoQG | 0 | null | transformers | Entry not found | [
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sibckukgvaxsepbkyb/mT5IndoQGSQuAD | dbef3d9c95a7ed8c4c586674676be9ca6a665441 | 2022-01-28T23:32:58.000Z | [
"pytorch",
"mt5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | sibckukgvaxsepbkyb | null | sibckukgvaxsepbkyb/mT5IndoQGSQuAD | 0 | null | transformers | Entry not found | [
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sigmoid/mt5-en-ja | 4985ea8a2f0bf7e6430b63177ed7f4bbd04cb996 | 2021-05-11T08:15:37.000Z | [
"pytorch",
"mt5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | sigmoid | null | sigmoid/mt5-en-ja | 0 | null | transformers | Entry not found | [
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simonlevine/biomed_roberta_base-4096-speedfix | f631bb27824f9744a096576ba7336f27a6205a04 | 2021-05-20T21:27:26.000Z | [
"pytorch",
"jax",
"roberta",
"feature-extraction",
"transformers"
] | feature-extraction | false | simonlevine | null | simonlevine/biomed_roberta_base-4096-speedfix | 0 | null | transformers | Entry not found | [
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simonmun/COHA1810to1850 | 5d62189b958cd228254377488c72d32d9d3cd42b | 2021-05-20T21:30:27.000Z | [
"pytorch",
"jax",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | simonmun | null | simonmun/COHA1810to1850 | 0 | null | transformers | Entry not found | [
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simonmun/COHA1820s | 6607c4c8322deab69f0c58aedd12627d364a2eb5 | 2021-05-20T21:31:16.000Z | [
"pytorch",
"jax",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | simonmun | null | simonmun/COHA1820s | 0 | null | transformers | Entry not found | [
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simonmun/COHA1920s | 445f8b63802fabedde6223568d73212e138082eb | 2021-05-20T21:41:36.000Z | [
"pytorch",
"jax",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | simonmun | null | simonmun/COHA1920s | 0 | null | transformers | Entry not found | [
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simonsr/wav2vec2-large-xlsr-dutch | 9c650d4c99350af089b86f9afbb12e5b656c5b97 | 2021-03-26T13:53:35.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"nl",
"dataset:common_voice",
"transformers",
"audio",
"speech",
"xlsr-fine-tuning-week",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | simonsr | null | simonsr/wav2vec2-large-xlsr-dutch | 0 | null | transformers | ---
language: nl
datasets:
- common_voice
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: simonsr wav2vec2-large-xlsr-dutch
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice nl
type: common_voice
args: nl
metrics:
- name: Test WER
type: wer
value: 38.74
---
# Wav2Vec2-Large-XLSR-53-Dutch
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Dutch using the [Common Voice](https://huggingface.co/datasets/common_voice)
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
test_dataset = load_dataset("common_voice", "nl", split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("simonsr/wav2vec2-large-xlsr-dutch")
model = Wav2Vec2ForCTC.from_pretrained("simonsr/wav2vec2-large-xlsr-dutch")
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the audio files as arrays
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
predicted_ids = torch.argmax(logits, dim=-1)
print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][:2])
```
## Evaluation
The model can be evaluated as follows on the Dutch test data of Common Voice.
```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import unidecode
import re
test_dataset = load_dataset("common_voice", "nl", split="test")
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("{model_id}") #TODO: replace {model_id} with your model id. The model id consists of {your_username}/{your_modelname}, *e.g.* `elgeish/wav2vec2-large-xlsr-53-arabic`
model = Wav2Vec2ForCTC.from_pretrained("{model_id}") #TODO: replace {model_id} with your model id. The model id consists of {your_username}/{your_modelname}, *e.g.* `elgeish/wav2vec2-large-xlsr-53-arabic`
model.to("cuda")
chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\”\�\(\)\=\´\–\&\…\—\’]'
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
batch["sentence"] = unidecode.unidecode(batch["sentence"])
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def evaluate(batch):
inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
pred_ids = torch.argmax(logits, dim=-1)
batch["pred_strings"] = processor.batch_decode(pred_ids)
return batch
result = test_dataset.map(evaluate, batched=True, batch_size=8)
print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
```
**Test Result**: 38.74 %
## Training
The Common Voice `train`, `validation`, and ... datasets were used for training.
The script used for training can be found [here](...) # TODO: fill in a link to your training script here. If you trained your model in a colab, simply fill in the link here. If you trained the model locally, it would be great if you could upload the training script on github and paste the link here. | [
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simrana5/RickBotExample | 25890573347714be5fb7d977891d0a471375184c | 2021-08-09T10:57:40.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | simrana5 | null | simrana5/RickBotExample | 0 | null | transformers | ---
tags:
- conversational
---
# RickBot built for [Chai](https://chai.ml/)
Make your own [here](https://colab.research.google.com/drive/1LtVm-VHvDnfNy7SsbZAqhh49ikBwh1un?usp=sharing)
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sin-survivor/re-errors | e8c7d828a81e220870df4a5350bfbb10a97355c1 | 2022-02-13T19:06:53.000Z | [
"pytorch",
"mbart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | sin-survivor | null | sin-survivor/re-errors | 0 | null | transformers | Entry not found | [
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sivavee-train/iac-v1 | 725fd7ab451194cc0205f95a95d4a50f79c2a620 | 2021-09-19T07:56:33.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | sivavee-train | null | sivavee-train/iac-v1 | 0 | null | transformers | Entry not found | [
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sivavee-train/iac-v2 | 4dcd4f51c18b2b6603b3f31a619e096f97b12fcc | 2021-09-20T07:04:28.000Z | [
"pytorch",
"gpt_neo",
"text-generation",
"transformers"
] | text-generation | false | sivavee-train | null | sivavee-train/iac-v2 | 0 | null | transformers | Entry not found | [
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skhurana/test_model | a8c256e9609a6104713b2c3460da51d9cb28452d | 2022-02-10T16:28:36.000Z | [
"pytorch",
"List of ISO 639-1 code for your language",
"lang1",
"lang2",
"dataset:dataset1",
"dataset:dataset2",
"PyTorch",
"license:apache-2.0"
] | null | false | skhurana | null | skhurana/test_model | 0 | null | null | # Hugging-face testing
---
language:
- "List of ISO 639-1 code for your language"
- lang1
- lang2
thumbnail: "url to a thumbnail used in social sharing"
tags:
- PyTorch
license: apache-2.0
datasets:
- dataset1
- dataset2
metrics:
- metric1
---
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skillzzzzzy/telberto | b378a055fbbb44f624d1ac64129c52cb31000251 | 2021-11-14T13:15:15.000Z | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | skillzzzzzy | null | skillzzzzzy/telberto | 0 | null | transformers | Entry not found | [
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skynex/DialoGPT-small-finalbatman | d7c79e6e6413aa987efa34ba23c2258fcabb7c61 | 2021-08-30T19:23:36.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | skynex | null | skynex/DialoGPT-small-finalbatman | 0 | null | transformers | ---
tags:
- conversational
---
#batman DialoGPT Model | [
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slider/mirror-bert | 8c1124f598fa53b2cd2e62c8d9fb611db349eb46 | 2021-12-10T01:48:40.000Z | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | false | slider | null | slider/mirror-bert | 0 | null | transformers | Entry not found | [
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sm6342/Health101 | dca7c5567ed0ab6ded29632d552d4928905109ac | 2021-05-20T21:55:29.000Z | [
"pytorch",
"jax",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | sm6342 | null | sm6342/Health101 | 0 | null | transformers | Entry not found | [
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] |
snoop2head/kogpt-conditional-2 | 5a06ebaa426ef9a9425e4911b680217d51b28cfb | 2021-12-25T04:42:13.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | snoop2head | null | snoop2head/kogpt-conditional-2 | 0 | null | transformers | # 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']
```
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soeque1/kosimcse | 411062d82408faf6ae07a2380b08cd5ffe087b7f | 2021-10-14T15:09:06.000Z | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | false | soeque1 | null | soeque1/kosimcse | 0 | null | transformers | Entry not found | [
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songhee/i-manual-mbert | 11b0c7d95b6128691cd5ca32d11a77ff262c3cd2 | 2021-11-02T15:19:11.000Z | [
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | songhee | null | songhee/i-manual-mbert | 0 | null | transformers | Entry not found | [
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sourabharsh/wav2vec2 | bada09ca174d14be965a23c34bcec951264c679f | 2021-07-21T04:47:20.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"transformers"
] | automatic-speech-recognition | false | sourabharsh | null | sourabharsh/wav2vec2 | 0 | null | transformers | Entry not found | [
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sourabharsh/wav2vec2_10july | fb636f08ce142146bf85bac6b319d903e6bbf0f2 | 2021-07-10T08:25:13.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"de",
"dataset:common_voice",
"transformers",
"audio",
"speech",
"xlsr-fine-tuning-week",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | sourabharsh | null | sourabharsh/wav2vec2_10july | 0 | null | transformers | ---
language: de
datasets:
- common_voice
metrics:
- wer
- cer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: XLSR Wav2Vec2 German by Jonatas Grosman
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice de
type: common_voice
args: de
metrics:
- name: Test WER
type: wer
value: 10.55
- name: Test CER
type: cer
value: 2.81
--- | [
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] |
sravn/e2e-qg-bert | d3d1dc7858249ab71184f0e1b2c0438674278930 | 2021-07-02T11:25:58.000Z | [
"pytorch",
"encoder-decoder",
"text2text-generation",
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sripadhstudy/100_SDB_TAxxL_truncate_768 | 44337d70e82218e219d04cf37fb47f188ef7df7f | 2021-06-05T15:36:46.000Z | [
"pytorch",
"distilbert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | sripadhstudy | null | sripadhstudy/100_SDB_TAxxL_truncate_768 | 0 | null | transformers | Entry not found | [
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sripadhstudy/100_SDB_TBB | fc9953109771b4f2dba9347a9752646bdae551fb | 2021-06-03T13:05:44.000Z | [
"pytorch",
"distilbert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | sripadhstudy | null | sripadhstudy/100_SDB_TBB | 0 | null | transformers | Entry not found | [
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sripadhstudy/500_SAB_TAxxL_truncate_12_layers | a80f4d10f7341165d39ec5a2ddd697957c1736a6 | 2021-06-14T15:40:49.000Z | [
"pytorch",
"albert",
"transformers"
] | null | false | sripadhstudy | null | sripadhstudy/500_SAB_TAxxL_truncate_12_layers | 0 | null | transformers | Entry not found | [
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sripadhstudy/500_SDB_TAxxL_average_768 | 8268518c8fffaee32bbe84345e6acfaa4ff5d3d0 | 2021-06-05T11:05:26.000Z | [
"pytorch",
"distilbert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | sripadhstudy | null | sripadhstudy/500_SDB_TAxxL_average_768 | 0 | null | transformers | Entry not found | [
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sripadhstudy/500_SDB_TBB | 0e321595cc3918f93c2187d0aa015a23d436372a | 2021-06-03T21:37:32.000Z | [
"pytorch",
"distilbert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | sripadhstudy | null | sripadhstudy/500_SDB_TBB | 0 | null | transformers | Entry not found | [
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sripadhstudy/50_SDB_TBB | 24f893e2d12b7420d9dabef81d9c4cd60f0a5906 | 2021-06-03T12:22:46.000Z | [
"pytorch",
"distilbert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | sripadhstudy | null | sripadhstudy/50_SDB_TBB | 0 | null | transformers | Entry not found | [
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srirachasenpai/DialoGPT-medium-harrypotter | 581c86190253c91c193bb012ea6d5387b574728c | 2021-10-09T06:23:58.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | srirachasenpai | null | srirachasenpai/DialoGPT-medium-harrypotter | 0 | null | transformers | ---
tags:
- conversational
---
# Harry Potter DialoGPT Model | [
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] |
srv/DialoGPT-medium-Breaking_Bad | 0f4d1ee658a7dbc46be9064e4af9a9641d1177cc | 2022-02-01T13:01:59.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | srv | null | srv/DialoGPT-medium-Breaking_Bad | 0 | null | transformers | ---
tags:
- conversational
---
# Breaking Bad DialoGPT Model | [
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ssam/DialoGPT-small-RickmfSanchez | 2a8790a867a7385b16ebf04d9a27443e457e2423 | 2021-08-22T18:11:38.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | ssam | null | ssam/DialoGPT-small-RickmfSanchez | 0 | null | transformers | ---
tags :
- conversational
---
#Rick Sanchez DialoGPT Model | [
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sshasnain/wav2vec2-xls-r-300m-bangla-command-word-combination-synthetic | 9db9b3a38b1cff5b11e52a4e8f8ac9b3fcc2d5c2 | 2022-02-11T13:25:09.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | sshasnain | null | sshasnain/wav2vec2-xls-r-300m-bangla-command-word-combination-synthetic | 0 | null | transformers | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-xls-r-300m-bangla-command-word-combination-synthetic
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-300m-bangla-command-word-combination-synthetic
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0068
- Wer: 0.4111
## 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: 100
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 5.2982 | 17.86 | 500 | 2.4580 | 1.1089 |
| 0.9644 | 35.71 | 1000 | 0.1250 | 0.5156 |
| 0.1767 | 53.57 | 1500 | 0.0310 | 0.4267 |
| 0.0912 | 71.43 | 2000 | 0.0149 | 0.4178 |
| 0.0505 | 89.29 | 2500 | 0.0068 | 0.4111 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
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sshleifer/student_cnn_12_1 | 8e16591a3f636bceb0fbfc94d76c42095c5f7932 | 2021-06-14T08:29:25.000Z | [
"pytorch",
"jax",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | sshleifer | null | sshleifer/student_cnn_12_1 | 0 | null | transformers | Entry not found | [
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sshleifer/student_cnn_12_2 | b64a7c3ca4338dae92baffc9b56b9cfead8af63b | 2021-06-14T08:31:55.000Z | [
"pytorch",
"jax",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | sshleifer | null | sshleifer/student_cnn_12_2 | 0 | null | transformers | Entry not found | [
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sshleifer/student_cnn_12_3 | ffe2a88d7f32122288b0ad4ca7d900f8f8ae7a9f | 2021-06-14T08:34:05.000Z | [
"pytorch",
"jax",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | sshleifer | null | sshleifer/student_cnn_12_3 | 0 | null | transformers | Entry not found | [
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sshleifer/student_enro_avg_12_3 | 19beec88ca76e9c0d6d617f71d4c612d2c201b67 | 2020-07-18T20:16:27.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | sshleifer | null | sshleifer/student_enro_avg_12_3 | 0 | null | transformers | Entry not found | [
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sshleifer/student_enro_sum_12_6 | 151f360c57773cedd2f92d12726883c6e7093829 | 2020-07-18T20:25:05.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | sshleifer | null | sshleifer/student_enro_sum_12_6 | 0 | null | transformers | Entry not found | [
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sshleifer/student_marian_en_ro_6_2 | 7691d4cf700a5a3e1c326b0db2c7321bf4ac341d | 2020-08-26T02:18:56.000Z | [
"pytorch",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | sshleifer | null | sshleifer/student_marian_en_ro_6_2 | 0 | 1 | transformers | Entry not found | [
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sshleifer/student_marian_en_ro_6_3 | 204b91850ec0f96eab4a35d57f6e95000b4fad6f | 2020-08-26T02:17:44.000Z | [
"pytorch",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | sshleifer | null | sshleifer/student_marian_en_ro_6_3 | 0 | null | transformers | Entry not found | [
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sshleifer/student_mbart_en_ro_12_3 | 947b4160a7bf606c30921638ca9728fedb8bd071 | 2020-07-15T15:14:51.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | sshleifer | null | sshleifer/student_mbart_en_ro_12_3 | 0 | null | transformers | Entry not found | [
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sshleifer/student_pegasus-xsum-12-4 | 9d2773e4f0d2745cd97fc604073bd9335b620c08 | 2020-09-11T04:03:12.000Z | [
"pytorch",
"pegasus",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | sshleifer | null | sshleifer/student_pegasus-xsum-12-4 | 0 | null | transformers | Entry not found | [
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sshleifer/student_xsum_12_2 | 63b4fef4aff97f1c7b70c43043f5db238f6faf31 | 2021-06-14T09:42:48.000Z | [
"pytorch",
"jax",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | sshleifer | null | sshleifer/student_xsum_12_2 | 0 | null | transformers | Entry not found | [
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stanford-crfm/darkmatter-gpt2-small-x343 | 70736a88c7d9f4dda06af14d375706c0ec3d61e0 | 2022-06-20T09:40:32.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | stanford-crfm | null | stanford-crfm/darkmatter-gpt2-small-x343 | 0 | null | transformers | Entry not found | [
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stanleychu2/t5_user_simulator | 15ba77de65cbb2ad15dcc34a2c413f88b920f469 | 2021-09-13T09:37:03.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | stanleychu2 | null | stanleychu2/t5_user_simulator | 0 | null | transformers | Entry not found | [
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stasvmk/honeymad_gpt_ru_v0_1 | da3534d71a67277d9539f546d27d79925bfc2c18 | 2022-01-10T13:05:50.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | stasvmk | null | stasvmk/honeymad_gpt_ru_v0_1 | 0 | null | transformers | Entry not found | [
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stefan-it/electra-base-gc4-64k-0-cased-discriminator | 3e9408f135e7b09f5554cb4739194f784e289659 | 2021-04-30T22:16:19.000Z | [
"pytorch",
"tf",
"electra",
"pretraining",
"de",
"dataset:german-nlp-group/german_common_crawl",
"transformers",
"license:mit"
] | null | false | stefan-it | null | stefan-it/electra-base-gc4-64k-0-cased-discriminator | 0 | null | transformers | ---
language: de
license: mit
datasets:
- german-nlp-group/german_common_crawl
---
# GC4LM: A Colossal (Biased) language model for German
This repository presents a colossal (and biased) language model for German trained on the recently released
["German colossal, clean Common Crawl corpus"](https://german-nlp-group.github.io/projects/gc4-corpus.html) (GC4),
with a total dataset size of ~844GB.
---
**Disclaimer**: the presented and trained language models in this repository are for **research only** purposes.
The GC4 corpus - that was used for training - contains crawled texts from the internet. Thus, the language models can
be considered as highly biased, resulting in a model that encodes stereotypical associations along gender, race,
ethnicity and disability status. Before using and working with the released checkpoints, it is highly recommended
to read:
[On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?](https://faculty.washington.edu/ebender/papers/Stochastic_Parrots.pdf)
from Emily M. Bender, Timnit Gebru, Angelina McMillan-Major and Shmargaret Shmitchell.
The aim of the released checkpoints is to boost research on large pre-trained language models for German, especially
for identifying biases and how to prevent them, as most research is currently done only for English.
---
Please use the new GitHub Discussions feature in order to discuss or present further research questions.
Feel free to use `#gc4lm` on Twitter 🐦. | [
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stefan-it/electra-base-gc4-64k-200000-cased-discriminator | 1eb0fca25b970d81c558e80c05afdf3754520f89 | 2021-04-30T22:36:06.000Z | [
"pytorch",
"tf",
"electra",
"pretraining",
"de",
"dataset:german-nlp-group/german_common_crawl",
"transformers",
"license:mit"
] | null | false | stefan-it | null | stefan-it/electra-base-gc4-64k-200000-cased-discriminator | 0 | null | transformers | ---
language: de
license: mit
datasets:
- german-nlp-group/german_common_crawl
---
# GC4LM: A Colossal (Biased) language model for German
This repository presents a colossal (and biased) language model for German trained on the recently released
["German colossal, clean Common Crawl corpus"](https://german-nlp-group.github.io/projects/gc4-corpus.html) (GC4),
with a total dataset size of ~844GB.
---
**Disclaimer**: the presented and trained language models in this repository are for **research only** purposes.
The GC4 corpus - that was used for training - contains crawled texts from the internet. Thus, the language models can
be considered as highly biased, resulting in a model that encodes stereotypical associations along gender, race,
ethnicity and disability status. Before using and working with the released checkpoints, it is highly recommended
to read:
[On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?](https://faculty.washington.edu/ebender/papers/Stochastic_Parrots.pdf)
from Emily M. Bender, Timnit Gebru, Angelina McMillan-Major and Shmargaret Shmitchell.
The aim of the released checkpoints is to boost research on large pre-trained language models for German, especially
for identifying biases and how to prevent them, as most research is currently done only for English.
---
Please use the new GitHub Discussions feature in order to discuss or present further research questions.
Feel free to use `#gc4lm` on Twitter 🐦.
| [
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stefan-it/electra-base-gc4-64k-400000-cased-discriminator | bb84cbbc4e912341c856a5749b4293d3a91cff6d | 2021-04-30T22:41:07.000Z | [
"pytorch",
"tf",
"electra",
"pretraining",
"de",
"dataset:german-nlp-group/german_common_crawl",
"transformers",
"license:mit"
] | null | false | stefan-it | null | stefan-it/electra-base-gc4-64k-400000-cased-discriminator | 0 | null | transformers | ---
language: de
license: mit
datasets:
- german-nlp-group/german_common_crawl
---
# GC4LM: A Colossal (Biased) language model for German
This repository presents a colossal (and biased) language model for German trained on the recently released
["German colossal, clean Common Crawl corpus"](https://german-nlp-group.github.io/projects/gc4-corpus.html) (GC4),
with a total dataset size of ~844GB.
---
**Disclaimer**: the presented and trained language models in this repository are for **research only** purposes.
The GC4 corpus - that was used for training - contains crawled texts from the internet. Thus, the language models can
be considered as highly biased, resulting in a model that encodes stereotypical associations along gender, race,
ethnicity and disability status. Before using and working with the released checkpoints, it is highly recommended
to read:
[On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?](https://faculty.washington.edu/ebender/papers/Stochastic_Parrots.pdf)
from Emily M. Bender, Timnit Gebru, Angelina McMillan-Major and Shmargaret Shmitchell.
The aim of the released checkpoints is to boost research on large pre-trained language models for German, especially
for identifying biases and how to prevent them, as most research is currently done only for English.
---
Please use the new GitHub Discussions feature in order to discuss or present further research questions.
Feel free to use `#gc4lm` on Twitter 🐦.
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] |
stefan-it/electra-base-gc4-64k-500000-cased-discriminator | 9904ddc55c6668423b3cbfdb84f126f444861757 | 2021-05-01T07:47:50.000Z | [
"pytorch",
"tf",
"electra",
"pretraining",
"de",
"dataset:german-nlp-group/german_common_crawl",
"transformers",
"license:mit"
] | null | false | stefan-it | null | stefan-it/electra-base-gc4-64k-500000-cased-discriminator | 0 | null | transformers | ---
language: de
license: mit
datasets:
- german-nlp-group/german_common_crawl
---
# GC4LM: A Colossal (Biased) language model for German
This repository presents a colossal (and biased) language model for German trained on the recently released
["German colossal, clean Common Crawl corpus"](https://german-nlp-group.github.io/projects/gc4-corpus.html) (GC4),
with a total dataset size of ~844GB.
---
**Disclaimer**: the presented and trained language models in this repository are for **research only** purposes.
The GC4 corpus - that was used for training - contains crawled texts from the internet. Thus, the language models can
be considered as highly biased, resulting in a model that encodes stereotypical associations along gender, race,
ethnicity and disability status. Before using and working with the released checkpoints, it is highly recommended
to read:
[On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?](https://faculty.washington.edu/ebender/papers/Stochastic_Parrots.pdf)
from Emily M. Bender, Timnit Gebru, Angelina McMillan-Major and Shmargaret Shmitchell.
The aim of the released checkpoints is to boost research on large pre-trained language models for German, especially
for identifying biases and how to prevent them, as most research is currently done only for English.
---
Please use the new GitHub Discussions feature in order to discuss or present further research questions.
Feel free to use `#gc4lm` on Twitter 🐦.
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] |
stefan-it/electra-base-gc4-64k-500000-cased-generator | 2d84481f78c9fe7b7c02120df649d0566b776220 | 2021-05-01T11:20:11.000Z | [
"pytorch",
"tf",
"electra",
"fill-mask",
"de",
"dataset:german-nlp-group/german_common_crawl",
"transformers",
"license:mit",
"autotrain_compatible"
] | fill-mask | false | stefan-it | null | stefan-it/electra-base-gc4-64k-500000-cased-generator | 0 | null | transformers | ---
language: de
license: mit
datasets:
- german-nlp-group/german_common_crawl
widget:
- text: "Heute ist ein [MASK] Tag"
---
# GC4LM: A Colossal (Biased) language model for German
This repository presents a colossal (and biased) language model for German trained on the recently released
["German colossal, clean Common Crawl corpus"](https://german-nlp-group.github.io/projects/gc4-corpus.html) (GC4),
with a total dataset size of ~844GB.
---
**Disclaimer**: the presented and trained language models in this repository are for **research only** purposes.
The GC4 corpus - that was used for training - contains crawled texts from the internet. Thus, the language models can
be considered as highly biased, resulting in a model that encodes stereotypical associations along gender, race,
ethnicity and disability status. Before using and working with the released checkpoints, it is highly recommended
to read:
[On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?](https://faculty.washington.edu/ebender/papers/Stochastic_Parrots.pdf)
from Emily M. Bender, Timnit Gebru, Angelina McMillan-Major and Shmargaret Shmitchell.
The aim of the released checkpoints is to boost research on large pre-trained language models for German, especially
for identifying biases and how to prevent them, as most research is currently done only for English.
---
Please use the new GitHub Discussions feature in order to discuss or present further research questions.
Feel free to use `#gc4lm` on Twitter 🐦.
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stefan-it/electra-base-gc4-64k-600000-cased-discriminator | e85fe42ea6308054ddbb35ef07e5d598019e7cbf | 2021-05-01T07:52:54.000Z | [
"pytorch",
"tf",
"electra",
"pretraining",
"de",
"dataset:german-nlp-group/german_common_crawl",
"transformers",
"license:mit"
] | null | false | stefan-it | null | stefan-it/electra-base-gc4-64k-600000-cased-discriminator | 0 | null | transformers | ---
language: de
license: mit
datasets:
- german-nlp-group/german_common_crawl
---
# GC4LM: A Colossal (Biased) language model for German
This repository presents a colossal (and biased) language model for German trained on the recently released
["German colossal, clean Common Crawl corpus"](https://german-nlp-group.github.io/projects/gc4-corpus.html) (GC4),
with a total dataset size of ~844GB.
---
**Disclaimer**: the presented and trained language models in this repository are for **research only** purposes.
The GC4 corpus - that was used for training - contains crawled texts from the internet. Thus, the language models can
be considered as highly biased, resulting in a model that encodes stereotypical associations along gender, race,
ethnicity and disability status. Before using and working with the released checkpoints, it is highly recommended
to read:
[On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?](https://faculty.washington.edu/ebender/papers/Stochastic_Parrots.pdf)
from Emily M. Bender, Timnit Gebru, Angelina McMillan-Major and Shmargaret Shmitchell.
The aim of the released checkpoints is to boost research on large pre-trained language models for German, especially
for identifying biases and how to prevent them, as most research is currently done only for English.
---
Please use the new GitHub Discussions feature in order to discuss or present further research questions.
Feel free to use `#gc4lm` on Twitter 🐦.
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stefan-it/electra-base-gc4-64k-700000-cased-discriminator | 726e453c8dc24793f326d53430f017001637d312 | 2021-05-01T09:41:36.000Z | [
"pytorch",
"tf",
"electra",
"pretraining",
"de",
"dataset:german-nlp-group/german_common_crawl",
"transformers",
"license:mit"
] | null | false | stefan-it | null | stefan-it/electra-base-gc4-64k-700000-cased-discriminator | 0 | null | transformers | ---
language: de
license: mit
datasets:
- german-nlp-group/german_common_crawl
---
# GC4LM: A Colossal (Biased) language model for German
This repository presents a colossal (and biased) language model for German trained on the recently released
["German colossal, clean Common Crawl corpus"](https://german-nlp-group.github.io/projects/gc4-corpus.html) (GC4),
with a total dataset size of ~844GB.
---
**Disclaimer**: the presented and trained language models in this repository are for **research only** purposes.
The GC4 corpus - that was used for training - contains crawled texts from the internet. Thus, the language models can
be considered as highly biased, resulting in a model that encodes stereotypical associations along gender, race,
ethnicity and disability status. Before using and working with the released checkpoints, it is highly recommended
to read:
[On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?](https://faculty.washington.edu/ebender/papers/Stochastic_Parrots.pdf)
from Emily M. Bender, Timnit Gebru, Angelina McMillan-Major and Shmargaret Shmitchell.
The aim of the released checkpoints is to boost research on large pre-trained language models for German, especially
for identifying biases and how to prevent them, as most research is currently done only for English.
---
Please use the new GitHub Discussions feature in order to discuss or present further research questions.
Feel free to use `#gc4lm` on Twitter 🐦.
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] |
stefan-it/electra-base-gc4-64k-700000-cased-generator | 3b67c236e99ecb328d6290be80ff0dc3ecf92fc2 | 2021-05-01T11:21:51.000Z | [
"pytorch",
"tf",
"electra",
"fill-mask",
"de",
"dataset:german-nlp-group/german_common_crawl",
"transformers",
"license:mit",
"autotrain_compatible"
] | fill-mask | false | stefan-it | null | stefan-it/electra-base-gc4-64k-700000-cased-generator | 0 | null | transformers | ---
language: de
license: mit
datasets:
- german-nlp-group/german_common_crawl
widget:
- text: "Heute ist ein [MASK] Tag"
---
# GC4LM: A Colossal (Biased) language model for German
This repository presents a colossal (and biased) language model for German trained on the recently released
["German colossal, clean Common Crawl corpus"](https://german-nlp-group.github.io/projects/gc4-corpus.html) (GC4),
with a total dataset size of ~844GB.
---
**Disclaimer**: the presented and trained language models in this repository are for **research only** purposes.
The GC4 corpus - that was used for training - contains crawled texts from the internet. Thus, the language models can
be considered as highly biased, resulting in a model that encodes stereotypical associations along gender, race,
ethnicity and disability status. Before using and working with the released checkpoints, it is highly recommended
to read:
[On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?](https://faculty.washington.edu/ebender/papers/Stochastic_Parrots.pdf)
from Emily M. Bender, Timnit Gebru, Angelina McMillan-Major and Shmargaret Shmitchell.
The aim of the released checkpoints is to boost research on large pre-trained language models for German, especially
for identifying biases and how to prevent them, as most research is currently done only for English.
---
Please use the new GitHub Discussions feature in order to discuss or present further research questions.
Feel free to use `#gc4lm` on Twitter 🐦.
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stefan-it/electra-base-gc4-64k-800000-cased-discriminator | 7f7d712194b55df6949ce3a57ef0bd82de7e6420 | 2021-05-01T09:46:59.000Z | [
"pytorch",
"tf",
"electra",
"pretraining",
"de",
"dataset:german-nlp-group/german_common_crawl",
"transformers",
"license:mit"
] | null | false | stefan-it | null | stefan-it/electra-base-gc4-64k-800000-cased-discriminator | 0 | null | transformers | ---
language: de
license: mit
datasets:
- german-nlp-group/german_common_crawl
---
# GC4LM: A Colossal (Biased) language model for German
This repository presents a colossal (and biased) language model for German trained on the recently released
["German colossal, clean Common Crawl corpus"](https://german-nlp-group.github.io/projects/gc4-corpus.html) (GC4),
with a total dataset size of ~844GB.
---
**Disclaimer**: the presented and trained language models in this repository are for **research only** purposes.
The GC4 corpus - that was used for training - contains crawled texts from the internet. Thus, the language models can
be considered as highly biased, resulting in a model that encodes stereotypical associations along gender, race,
ethnicity and disability status. Before using and working with the released checkpoints, it is highly recommended
to read:
[On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?](https://faculty.washington.edu/ebender/papers/Stochastic_Parrots.pdf)
from Emily M. Bender, Timnit Gebru, Angelina McMillan-Major and Shmargaret Shmitchell.
The aim of the released checkpoints is to boost research on large pre-trained language models for German, especially
for identifying biases and how to prevent them, as most research is currently done only for English.
---
Please use the new GitHub Discussions feature in order to discuss or present further research questions.
Feel free to use `#gc4lm` on Twitter 🐦.
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stevenshoemaker/horrors | a285c088852c3570c1df461f4655daa7891a983c | 2021-05-23T12:57:05.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | stevenshoemaker | null | stevenshoemaker/horrors | 0 | null | transformers | Entry not found | [
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stevenshoemaker/pitchfork | 11269816cc938d5cfd738ea555c80e8623f8c22b | 2021-05-23T12:58:02.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | stevenshoemaker | null | stevenshoemaker/pitchfork | 0 | null | transformers | Entry not found | [
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stfuowned/rick-small | b642eba85f276e374736fbb23d5a48189b1f511a | 2021-06-08T06:09:39.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | stfuowned | null | stfuowned/rick-small | 0 | null | transformers | Entry not found | [
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stfuowned/rick | 21af8542f99ddb25cdebe41e71e085ef9d05da8c | 2021-06-08T05:50:30.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | stfuowned | null | stfuowned/rick | 0 | null | transformers | ---
tags:
- conversational
---
# My Awesome Model | [
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] |
sthom/DialoGPT-small-tin | 154c88d95d10b029650ba14823dc86cb0271c76b | 2021-12-05T07:21:54.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | sthom | null | sthom/DialoGPT-small-tin | 0 | null | transformers | ---
tags:
- conversational
---
# tin bot | [
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stolenpyjak/testing_model | 61322099e0e01cba0ec635ecf046af937704b127 | 2021-05-20T21:56:30.000Z | [
"pytorch",
"jax",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | stolenpyjak | null | stolenpyjak/testing_model | 0 | null | transformers | Entry not found | [
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stonkgs/stonkgs-300k | b04454eb1909f998c5226a7c48e746457e363a3d | 2021-07-26T12:17:38.000Z | [
"pytorch",
"bert",
"transformers"
] | null | false | stonkgs | null | stonkgs/stonkgs-300k | 0 | null | transformers | Entry not found | [
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subbareddyiiit/BERT-NLP | 84a467e784ea2ec1028171f163fc9327b0a77651 | 2021-05-20T07:15:46.000Z | [
"pytorch",
"jax",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | subbareddyiiit | null | subbareddyiiit/BERT-NLP | 0 | null | transformers | hello
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subbareddyiiit/GPT2NLP | 14b57df6157367e172270f24dfea67dbb13b21db | 2021-05-23T13:00:32.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | subbareddyiiit | null | subbareddyiiit/GPT2NLP | 0 | null | transformers | hello
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subbareddyiiit/TeAlbert | 80c9da543be945f7b3c9331b2542276c72e39fae | 2020-06-19T22:39:46.000Z | [
"pytorch",
"transformers"
] | null | false | subbareddyiiit | null | subbareddyiiit/TeAlbert | 0 | null | transformers | Entry not found | [
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subbareddyiiit/gpt2_csl_gold8k | 3c0092acdf22cf3fb7b6c6c35c79ac41ae78a183 | 2021-05-23T13:01:39.000Z | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | subbareddyiiit | null | subbareddyiiit/gpt2_csl_gold8k | 0 | null | transformers | hello
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] |
subbareddyiiit/inria_roberta | 2a671dc9c316b02bb12e999c563274e7c1cc1800 | 2021-05-20T22:00:14.000Z | [
"pytorch",
"jax",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | subbareddyiiit | null | subbareddyiiit/inria_roberta | 0 | null | transformers | hello
| [
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suhruth/test_model_2 | 1ca7ca78bb8b8dcd09b4508235c6662aa892d33a | 2021-12-15T04:10:23.000Z | [
"pytorch"
] | null | false | suhruth | null | suhruth/test_model_2 | 0 | null | null | Entry not found | [
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sultan/ArabicTransformer-intermediate-encoder | dea46eaa819be6a17f6330b8562633ab59b5a2d4 | 2021-10-08T06:30:32.000Z | [
"pytorch",
"funnel",
"feature-extraction",
"transformers"
] | feature-extraction | false | sultan | null | sultan/ArabicTransformer-intermediate-encoder | 0 | null | transformers | Entry not found | [
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