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<!-- 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-large-fake-news-detection-spanish
This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-large-bne](https://huggingface.co/PlanTL-GOB-ES/roberta-large-bne) on an [Spanish Fake News Dataset](https://sites.google.com/view/iberlef2020/#h.p_w0c31bn0r-SW).
It achieves the following results on the evaluation set:
- Loss: 1.7474
- F1: **0.7717**
- Accuracy: 0.7797
> So, based on the [leaderboard](https://sites.google.com/view/fakedes/results?authuser=0) our model **outperforms** the best model (scores F1 = 0.7666).
## Model description
RoBERTa-large-bne is a transformer-based masked language model for the Spanish language. It is based on the RoBERTa large model and has been pre-trained using the largest Spanish corpus known to date, with a total of 570GB of clean and deduplicated text processed for this work, compiled from the web crawlings performed by the National Library of Spain (Biblioteca Nacional de España) from 2009 to 2019.
## Intended uses & limitations
The objective of this task is to decide if a news item is fake or real by analyzing its textual representation.
## Training and evaluation data
**FakeDeS**: [Fake News Detection in Spanish Shared Task](https://sites.google.com/view/fakedes/home)
Fake news provides information that aims to manipulate people for different purposes: terrorism, political elections, advertisement, satire, among others. In social networks, misinformation extends in seconds among thousands of people, so it is necessary to develop tools that help control the amount of false information on the web. Similar tasks are detection of popularity in social networks and detection of subjectivity of messages in this media. A fake news detection system aims to help users detect and filter out potentially deceptive news. The prediction of intentionally misleading news is based on the analysis of truthful and fraudulent previously reviewed news, i.e., annotated corpora.
The Spanish Fake News Corpus is a collection of news compiled from several web sources: established newspapers websites,media companies websites, special websites dedicated to validating fake news, websites designated by different journalists as sites that regularly publish fake news. The news were collected from January to July of 2018 and all of them were written in Mexican Spanish.
The corpus has 971 news collected from January to July, 2018, from different sources:
- Established newspapers websites,
- Media companies websites,
- Special websites dedicated to validating fake news,
- Websites designated by different journalists as sites that regularly publish fake news.
The corpus was tagged considering only two classes (true or fake), following a manual labeling process:
- A news is true if there is evidence that it has been published in reliable sites.
- A news is fake if there is news from reliable sites or specialized website in detection of deceptive content that contradicts it or no other evidence was found about the news besides the source.
- We collected the true-fake news pair of an event so there is a correlation of news in the corpus.
In order to avoid topic bias, the corpus covers news from 9 different topics: Science, Sport, Economy, Education, Entertainment, Politics, Health, Security, and Society. As it can be seen in the table below, the number of fake and true news is quite balanced. Approximately 70% will be used as training corpus (676 news), and the 30% as testing corpus (295 news).
The training corpus contains the following information:
- Category: Fake/ True
- Topic: Science/ Sport/ Economy/ Education/ Entertainment/ Politics, Health/ Security/ Society
- Headline: The title of the news.
- Text: The complete text of the news.
- Link: The URL where the news was published.
More information needed
## Training procedure
TBA
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|
| No log | 1.0 | 243 | 0.6282 | 0.7513 | 0.75 |
| No log | 2.0 | 486 | 0.9600 | 0.7346 | 0.7587 |
| 0.5099 | 3.0 | 729 | 1.2128 | 0.7656 | 0.7570 |
| 0.5099 | 4.0 | 972 | 1.4001 | 0.7606 | 0.7622 |
| 0.1949 | 5.0 | 1215 | 1.9748 | 0.6475 | 0.7220 |
| 0.1949 | 6.0 | 1458 | 1.7386 | 0.7706 | 0.7710 |
| 0.0263 | 7.0 | 1701 | 1.7474 | 0.7717 | 0.7797 |
| 0.0263 | 8.0 | 1944 | 1.8114 | 0.7695 | 0.7780 |
| 0.0046 | 9.0 | 2187 | 1.8444 | 0.7709 | 0.7797 |
| 0.0046 | 10.0 | 2430 | 1.8552 | 0.7709 | 0.7797 |
### Fast usage with HF `pipelines`
```python
from transformers import pipeline
ckpt = "Narrativaai/fake-news-detection-spanish"
classifier = pipeline("text-classification", model=ckpt)
headline = "Your headline"
text = "Your article text here..."
classifier(headline + " [SEP] " + text)
```
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3
Created by: [Narrativa](https://www.narrativa.com/)
About Narrativa: Natural Language Generation (NLG) | Gabriele, our machine learning-based platform, builds and deploys natural language solutions. #NLG #AI | {"language": "es", "tags": ["generated_from_trainer", "fake", "news", "competition"], "datasets": ["fakedes"], "metrics": ["f1", "accuracy"], "widget": [{"text": "La palabra \"haiga\", aceptada por la RAE [SEP] La palabra \"haiga\", aceptada por la RAE La Real Academia de la Lengua (RAE), ha aceptado el uso de \"HAIGA\", para su utilizaci\u00f3n en las tres personas del singular del presente del subjuntivo del verbo hacer, aunque asegura que la forma m\u00e1s recomendable en la lengua culta para este tiempo, sigue siendo \"haya\". As\u00ed lo han confirmado fuentes de la RAE, que explican que este cambio ha sido propuesto y aprobado por el pleno de la Academia de la Lengua, tras la extendida utilizaci\u00f3n por todo el territorio nacional, sobre todo, empleado por personas carentes de estudios o con estudios b\u00e1sicos de graduado escolar. Ya no ser\u00e1 objeto de burla ese compa\u00f1ero que a diario repite aquello de \"Mientras que haiga faena, no podemos quejarnos\" o esa abuela que repite aquello de \"El que haiga sacao los juguetes, que los recoja\". Entre otras palabras novedosas que ha aceptado la RAE, contamos tambi\u00e9n con \"Descambiar\", significa deshacer un cambio, por ejemplo \"devolver la compra\". Visto lo visto, nadie apostar\u00eda que la palabra \"follamigos\" sea la siguiente de la lista."}], "model-index": [{"name": "roberta-large-fake-news-detection-spanish", "results": []}]} | text-classification | Narrativaai/fake-news-detection-spanish | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"fake",
"news",
"competition",
"es",
"dataset:fakedes",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #fake #news #competition #es #dataset-fakedes #autotrain_compatible #endpoints_compatible #has_space #region-us
| RoBERTa-large-fake-news-detection-spanish
=========================================
This model is a fine-tuned version of PlanTL-GOB-ES/roberta-large-bne on an Spanish Fake News Dataset.
It achieves the following results on the evaluation set:
* Loss: 1.7474
* F1: 0.7717
* Accuracy: 0.7797
>
> So, based on the leaderboard our model outperforms the best model (scores F1 = 0.7666).
>
>
>
Model description
-----------------
RoBERTa-large-bne is a transformer-based masked language model for the Spanish language. It is based on the RoBERTa large model and has been pre-trained using the largest Spanish corpus known to date, with a total of 570GB of clean and deduplicated text processed for this work, compiled from the web crawlings performed by the National Library of Spain (Biblioteca Nacional de España) from 2009 to 2019.
Intended uses & limitations
---------------------------
The objective of this task is to decide if a news item is fake or real by analyzing its textual representation.
Training and evaluation data
----------------------------
FakeDeS: Fake News Detection in Spanish Shared Task
Fake news provides information that aims to manipulate people for different purposes: terrorism, political elections, advertisement, satire, among others. In social networks, misinformation extends in seconds among thousands of people, so it is necessary to develop tools that help control the amount of false information on the web. Similar tasks are detection of popularity in social networks and detection of subjectivity of messages in this media. A fake news detection system aims to help users detect and filter out potentially deceptive news. The prediction of intentionally misleading news is based on the analysis of truthful and fraudulent previously reviewed news, i.e., annotated corpora.
The Spanish Fake News Corpus is a collection of news compiled from several web sources: established newspapers websites,media companies websites, special websites dedicated to validating fake news, websites designated by different journalists as sites that regularly publish fake news. The news were collected from January to July of 2018 and all of them were written in Mexican Spanish.
The corpus has 971 news collected from January to July, 2018, from different sources:
* Established newspapers websites,
* Media companies websites,
* Special websites dedicated to validating fake news,
* Websites designated by different journalists as sites that regularly publish fake news.
The corpus was tagged considering only two classes (true or fake), following a manual labeling process:
* A news is true if there is evidence that it has been published in reliable sites.
* A news is fake if there is news from reliable sites or specialized website in detection of deceptive content that contradicts it or no other evidence was found about the news besides the source.
* We collected the true-fake news pair of an event so there is a correlation of news in the corpus.
In order to avoid topic bias, the corpus covers news from 9 different topics: Science, Sport, Economy, Education, Entertainment, Politics, Health, Security, and Society. As it can be seen in the table below, the number of fake and true news is quite balanced. Approximately 70% will be used as training corpus (676 news), and the 30% as testing corpus (295 news).
The training corpus contains the following information:
* Category: Fake/ True
* Topic: Science/ Sport/ Economy/ Education/ Entertainment/ Politics, Health/ Security/ Society
* Headline: The title of the news.
* Text: The complete text of the news.
* Link: The URL where the news was published.
More information needed
Training procedure
------------------
TBA
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10
### Training results
### Fast usage with HF 'pipelines'
### Framework versions
* Transformers 4.11.3
* Pytorch 1.9.0+cu111
* Datasets 1.14.0
* Tokenizers 0.10.3
Created by: Narrativa
About Narrativa: Natural Language Generation (NLG) | Gabriele, our machine learning-based platform, builds and deploys natural language solutions. #NLG #AI
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Training results",
"### Fast usage with HF 'pipelines'",
"### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.9.0+cu111\n* Datasets 1.14.0\n* Tokenizers 0.10.3\n\n\nCreated by: Narrativa\n\n\nAbout Narrativa: Natural Language Generation (NLG) | Gabriele, our machine learning-based platform, builds and deploys natural language solutions. #NLG #AI"
] | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #fake #news #competition #es #dataset-fakedes #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Training results",
"### Fast usage with HF 'pipelines'",
"### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.9.0+cu111\n* Datasets 1.14.0\n* Tokenizers 0.10.3\n\n\nCreated by: Narrativa\n\n\nAbout Narrativa: Natural Language Generation (NLG) | Gabriele, our machine learning-based platform, builds and deploys natural language solutions. #NLG #AI"
] | [
69,
98,
4,
12,
80
] | [
"passage: TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #fake #news #competition #es #dataset-fakedes #autotrain_compatible #endpoints_compatible #has_space #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10### Training results### Fast usage with HF 'pipelines'### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.9.0+cu111\n* Datasets 1.14.0\n* Tokenizers 0.10.3\n\n\nCreated by: Narrativa\n\n\nAbout Narrativa: Natural Language Generation (NLG) | Gabriele, our machine learning-based platform, builds and deploys natural language solutions. #NLG #AI"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# test-mlm
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6481
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.12.1
- Tokenizers 0.10.3
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "test-mlm", "results": []}]} | null | Narshion/bert-base-multilingual-cased-mwach | [
"transformers",
"pytorch",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #endpoints_compatible #region-us
|
# test-mlm
This model is a fine-tuned version of bert-base-multilingual-cased on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6481
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.12.1
- Tokenizers 0.10.3
| [
"# test-mlm\n\nThis model is a fine-tuned version of bert-base-multilingual-cased on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.6481",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3.0",
"### Training results",
"### Framework versions\n\n- Transformers 4.11.3\n- Pytorch 1.9.0+cu111\n- Datasets 1.12.1\n- Tokenizers 0.10.3"
] | [
"TAGS\n#transformers #pytorch #endpoints_compatible #region-us \n",
"# test-mlm\n\nThis model is a fine-tuned version of bert-base-multilingual-cased on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.6481",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3.0",
"### Training results",
"### Framework versions\n\n- Transformers 4.11.3\n- Pytorch 1.9.0+cu111\n- Datasets 1.12.1\n- Tokenizers 0.10.3"
] | [
21,
52,
6,
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34
] | [
"passage: TAGS\n#transformers #pytorch #endpoints_compatible #region-us \n# test-mlm\n\nThis model is a fine-tuned version of bert-base-multilingual-cased on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.6481## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3.0### Training results### Framework versions\n\n- Transformers 4.11.3\n- Pytorch 1.9.0+cu111\n- Datasets 1.12.1\n- Tokenizers 0.10.3"
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null | null | transformers |
<!-- 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-multilingual-cased-urgency
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/) on the mWACH NEO dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2797
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 4.1408 | 1.0 | 5659 | 3.6705 |
| 2.8777 | 2.0 | 11318 | 2.5536 |
| 2.561 | 3.0 | 16977 | 2.2740 |
### Framework versions
- Transformers 4.10.0
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3
| {"tags": ["generated_from_trainer"], "datasets": []} | fill-mask | Narshion/bert-base-multilingual-cased-urgency | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| bert-base-multilingual-cased-urgency
====================================
This model is a fine-tuned version of bert-base-multilingual-cased on the mWACH NEO dataset.
It achieves the following results on the evaluation set:
* Loss: 2.2797
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3.0
### Training results
### Framework versions
* Transformers 4.10.0
* Pytorch 1.9.0+cu102
* Datasets 1.11.0
* Tokenizers 0.10.3
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.10.0\n* Pytorch 1.9.0+cu102\n* Datasets 1.11.0\n* Tokenizers 0.10.3"
] | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.10.0\n* Pytorch 1.9.0+cu102\n* Datasets 1.11.0\n* Tokenizers 0.10.3"
] | [
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34
] | [
"passage: TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0### Training results### Framework versions\n\n\n* Transformers 4.10.0\n* Pytorch 1.9.0+cu102\n* Datasets 1.11.0\n* Tokenizers 0.10.3"
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null | null | transformers |
## DeBERTa: Decoding-enhanced BERT with Disentangled Attention
[DeBERTa](https://arxiv.org/abs/2006.03654) improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. It outperforms BERT and RoBERTa on majority of NLU tasks with 80GB training data.
Please check the [official repository](https://github.com/microsoft/DeBERTa) for more details and updates.
This is the DeBERTa large model fine-tuned with MNLI task.
#### Fine-tuning on NLU tasks
We present the dev results on SQuAD 1.1/2.0 and several GLUE benchmark tasks.
| Model | SQuAD 1.1 | SQuAD 2.0 | MNLI-m/mm | SST-2 | QNLI | CoLA | RTE | MRPC | QQP |STS-B |
|---------------------------|-----------|-----------|-------------|-------|------|------|--------|-------|-------|------|
| | F1/EM | F1/EM | Acc | Acc | Acc | MCC | Acc |Acc/F1 |Acc/F1 |P/S |
| BERT-Large | 90.9/84.1 | 81.8/79.0 | 86.6/- | 93.2 | 92.3 | 60.6 | 70.4 | 88.0/- | 91.3/- |90.0/- |
| RoBERTa-Large | 94.6/88.9 | 89.4/86.5 | 90.2/- | 96.4 | 93.9 | 68.0 | 86.6 | 90.9/- | 92.2/- |92.4/- |
| XLNet-Large | 95.1/89.7 | 90.6/87.9 | 90.8/- | 97.0 | 94.9 | 69.0 | 85.9 | 90.8/- | 92.3/- |92.5/- |
| [DeBERTa-Large](https://huggingface.co/microsoft/deberta-large)<sup>1</sup> | 95.5/90.1 | 90.7/88.0 | 91.3/91.1| 96.5|95.3| 69.5| 91.0| 92.6/94.6| 92.3/- |92.8/92.5 |
| [DeBERTa-XLarge](https://huggingface.co/microsoft/deberta-xlarge)<sup>1</sup> | -/- | -/- | 91.5/91.2| 97.0 | - | - | 93.1 | 92.1/94.3 | - |92.9/92.7|
| [DeBERTa-V2-XLarge](https://huggingface.co/microsoft/deberta-v2-xlarge)<sup>1</sup>|95.8/90.8| 91.4/88.9|91.7/91.6| **97.5**| 95.8|71.1|**93.9**|92.0/94.2|92.3/89.8|92.9/92.9|
|**[DeBERTa-V2-XXLarge](https://huggingface.co/microsoft/deberta-v2-xxlarge)<sup>1,2</sup>**|**96.1/91.4**|**92.2/89.7**|**91.7/91.9**|97.2|**96.0**|**72.0**| 93.5| **93.1/94.9**|**92.7/90.3** |**93.2/93.1** |
--------
#### Notes.
- <sup>1</sup> Following RoBERTa, for RTE, MRPC, STS-B, we fine-tune the tasks based on [DeBERTa-Large-MNLI](https://huggingface.co/microsoft/deberta-large-mnli), [DeBERTa-XLarge-MNLI](https://huggingface.co/microsoft/deberta-xlarge-mnli), [DeBERTa-V2-XLarge-MNLI](https://huggingface.co/microsoft/deberta-v2-xlarge-mnli), [DeBERTa-V2-XXLarge-MNLI](https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli). The results of SST-2/QQP/QNLI/SQuADv2 will also be slightly improved when start from MNLI fine-tuned models, however, we only report the numbers fine-tuned from pretrained base models for those 4 tasks.
- <sup>2</sup> To try the **XXLarge** model with **[HF transformers](https://huggingface.co/transformers/main_classes/trainer.html)**, you need to specify **--sharded_ddp**
```bash
cd transformers/examples/text-classification/
export TASK_NAME=mrpc
python -m torch.distributed.launch --nproc_per_node=8 run_glue.py --model_name_or_path microsoft/deberta-v2-xxlarge \\\n--task_name $TASK_NAME --do_train --do_eval --max_seq_length 128 --per_device_train_batch_size 4 \\\n--learning_rate 3e-6 --num_train_epochs 3 --output_dir /tmp/$TASK_NAME/ --overwrite_output_dir --sharded_ddp --fp16
```
### Citation
If you find DeBERTa useful for your work, please cite the following paper:
``` latex
@inproceedings{
he2021deberta,
title={DEBERTA: DECODING-ENHANCED BERT WITH DISENTANGLED ATTENTION},
author={Pengcheng He and Xiaodong Liu and Jianfeng Gao and Weizhu Chen},
booktitle={International Conference on Learning Representations},
year={2021},
url={https://openreview.net/forum?id=XPZIaotutsD}
}
```
| {"language": "en", "license": "mit", "tags": ["deberta-v1", "deberta-mnli"], "tasks": "mnli", "thumbnail": "https://huggingface.co/front/thumbnails/microsoft.png", "pipeline_tag": "zero-shot-classification"} | zero-shot-classification | Narsil/deberta-large-mnli-zero-cls | [
"transformers",
"pytorch",
"deberta",
"text-classification",
"deberta-v1",
"deberta-mnli",
"zero-shot-classification",
"en",
"arxiv:2006.03654",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2006.03654"
] | [
"en"
] | TAGS
#transformers #pytorch #deberta #text-classification #deberta-v1 #deberta-mnli #zero-shot-classification #en #arxiv-2006.03654 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
| DeBERTa: Decoding-enhanced BERT with Disentangled Attention
-----------------------------------------------------------
DeBERTa improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. It outperforms BERT and RoBERTa on majority of NLU tasks with 80GB training data.
Please check the official repository for more details and updates.
This is the DeBERTa large model fine-tuned with MNLI task.
#### Fine-tuning on NLU tasks
We present the dev results on SQuAD 1.1/2.0 and several GLUE benchmark tasks.
---
#### Notes.
* 1 Following RoBERTa, for RTE, MRPC, STS-B, we fine-tune the tasks based on DeBERTa-Large-MNLI, DeBERTa-XLarge-MNLI, DeBERTa-V2-XLarge-MNLI, DeBERTa-V2-XXLarge-MNLI. The results of SST-2/QQP/QNLI/SQuADv2 will also be slightly improved when start from MNLI fine-tuned models, however, we only report the numbers fine-tuned from pretrained base models for those 4 tasks.
* 2 To try the XXLarge model with HF transformers, you need to specify --sharded\_ddp
If you find DeBERTa useful for your work, please cite the following paper:
| [
"#### Fine-tuning on NLU tasks\n\n\nWe present the dev results on SQuAD 1.1/2.0 and several GLUE benchmark tasks.\n\n\n\n\n\n---",
"#### Notes.\n\n\n* 1 Following RoBERTa, for RTE, MRPC, STS-B, we fine-tune the tasks based on DeBERTa-Large-MNLI, DeBERTa-XLarge-MNLI, DeBERTa-V2-XLarge-MNLI, DeBERTa-V2-XXLarge-MNLI. The results of SST-2/QQP/QNLI/SQuADv2 will also be slightly improved when start from MNLI fine-tuned models, however, we only report the numbers fine-tuned from pretrained base models for those 4 tasks.\n* 2 To try the XXLarge model with HF transformers, you need to specify --sharded\\_ddp\n\n\nIf you find DeBERTa useful for your work, please cite the following paper:"
] | [
"TAGS\n#transformers #pytorch #deberta #text-classification #deberta-v1 #deberta-mnli #zero-shot-classification #en #arxiv-2006.03654 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"#### Fine-tuning on NLU tasks\n\n\nWe present the dev results on SQuAD 1.1/2.0 and several GLUE benchmark tasks.\n\n\n\n\n\n---",
"#### Notes.\n\n\n* 1 Following RoBERTa, for RTE, MRPC, STS-B, we fine-tune the tasks based on DeBERTa-Large-MNLI, DeBERTa-XLarge-MNLI, DeBERTa-V2-XLarge-MNLI, DeBERTa-V2-XXLarge-MNLI. The results of SST-2/QQP/QNLI/SQuADv2 will also be slightly improved when start from MNLI fine-tuned models, however, we only report the numbers fine-tuned from pretrained base models for those 4 tasks.\n* 2 To try the XXLarge model with HF transformers, you need to specify --sharded\\_ddp\n\n\nIf you find DeBERTa useful for your work, please cite the following paper:"
] | [
80,
32,
186
] | [
"passage: TAGS\n#transformers #pytorch #deberta #text-classification #deberta-v1 #deberta-mnli #zero-shot-classification #en #arxiv-2006.03654 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n#### Fine-tuning on NLU tasks\n\n\nWe present the dev results on SQuAD 1.1/2.0 and several GLUE benchmark tasks.\n\n\n\n\n\n---#### Notes.\n\n\n* 1 Following RoBERTa, for RTE, MRPC, STS-B, we fine-tune the tasks based on DeBERTa-Large-MNLI, DeBERTa-XLarge-MNLI, DeBERTa-V2-XLarge-MNLI, DeBERTa-V2-XXLarge-MNLI. The results of SST-2/QQP/QNLI/SQuADv2 will also be slightly improved when start from MNLI fine-tuned models, however, we only report the numbers fine-tuned from pretrained base models for those 4 tasks.\n* 2 To try the XXLarge model with HF transformers, you need to specify --sharded\\_ddp\n\n\nIf you find DeBERTa useful for your work, please cite the following paper:"
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null | null | transformers |
# 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"\
\
The 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">
\t<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
| {"language": "en", "license": "mit", "tags": ["exbert"], "pipeline_tag": "text-generation"} | text-generation | Narsil/gpt2 | [
"transformers",
"pytorch",
"tf",
"jax",
"tflite",
"rust",
"safetensors",
"gpt2",
"text-generation",
"exbert",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #tflite #rust #safetensors #gpt2 #text-generation #exbert #en #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| GPT-2
=====
Test the whole generation capabilities here: URL
Pretrained model on English language using a causal language modeling (CLM) objective. It was introduced in
this paper
and first released at this page.
Disclaimer: The team releasing GPT-2 also wrote a
model card 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 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:
Here is how to use this model to get the features of a given text in PyTorch:
and in TensorFlow:
### 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:
>
> 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:
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.
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):
### BibTeX entry and citation info
<a href="URL
\t<img width="300px" src="URL
| [
"### How to use\n\n\nYou can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we\nset a seed for reproducibility:\n\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\n\nand in TensorFlow:",
"### Limitations and bias\n\n\nThe training data used for this model has not been released as a dataset one can browse. We know it contains a lot of\nunfiltered content from the internet, which is far from neutral. As the openAI team themselves point out in their\nmodel card:\n\n\n\n> \n> Because large-scale language models like GPT-2 do not distinguish fact from fiction, we don’t support use-cases\n> that require the generated text to be true.\n> \n> \n> Additionally, language models like GPT-2 reflect the biases inherent to the systems they were trained on, so we do\n> not recommend that they be deployed into systems that interact with humans > unless the deployers first carry out a\n> study of biases relevant to the intended use-case. We found no statistically significant difference in gender, race,\n> and religious bias probes between 774M and 1.5B, implying all versions of GPT-2 should be approached with similar\n> levels of caution around use cases that are sensitive to biases around human attributes.\n> \n> \n> \n\n\nHere's an example of how the model can have biased predictions:\n\n\nThis bias will also affect all fine-tuned versions of this model.\n\n\nTraining data\n-------------\n\n\nThe OpenAI team wanted to train this model on a corpus as large as possible. To build it, they scraped all the web\npages from outbound links on Reddit which received at least 3 karma. Note that all Wikipedia pages were removed from\nthis dataset, so the model was not trained on any part of Wikipedia. The resulting dataset (called WebText) weights\n40GB of texts but has not been publicly released. You can find a list of the top 1,000 domains present in WebText\nhere.\n\n\nTraining procedure\n------------------",
"### Preprocessing\n\n\nThe texts are tokenized using a byte-level version of Byte Pair Encoding (BPE) (for unicode characters) and a\nvocabulary size of 50,257. The inputs are sequences of 1024 consecutive tokens.\n\n\nThe larger model was trained on 256 cloud TPU v3 cores. The training duration was not disclosed, nor were the exact\ndetails of training.\n\n\nEvaluation results\n------------------\n\n\nThe model achieves the following results without any fine-tuning (zero-shot):",
"### BibTeX entry and citation info\n\n\n<a href=\"URL\n\\t<img width=\"300px\" src=\"URL"
] | [
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"### How to use\n\n\nYou can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we\nset a seed for reproducibility:\n\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\n\nand in TensorFlow:",
"### Limitations and bias\n\n\nThe training data used for this model has not been released as a dataset one can browse. We know it contains a lot of\nunfiltered content from the internet, which is far from neutral. As the openAI team themselves point out in their\nmodel card:\n\n\n\n> \n> Because large-scale language models like GPT-2 do not distinguish fact from fiction, we don’t support use-cases\n> that require the generated text to be true.\n> \n> \n> Additionally, language models like GPT-2 reflect the biases inherent to the systems they were trained on, so we do\n> not recommend that they be deployed into systems that interact with humans > unless the deployers first carry out a\n> study of biases relevant to the intended use-case. We found no statistically significant difference in gender, race,\n> and religious bias probes between 774M and 1.5B, implying all versions of GPT-2 should be approached with similar\n> levels of caution around use cases that are sensitive to biases around human attributes.\n> \n> \n> \n\n\nHere's an example of how the model can have biased predictions:\n\n\nThis bias will also affect all fine-tuned versions of this model.\n\n\nTraining data\n-------------\n\n\nThe OpenAI team wanted to train this model on a corpus as large as possible. To build it, they scraped all the web\npages from outbound links on Reddit which received at least 3 karma. Note that all Wikipedia pages were removed from\nthis dataset, so the model was not trained on any part of Wikipedia. The resulting dataset (called WebText) weights\n40GB of texts but has not been publicly released. You can find a list of the top 1,000 domains present in WebText\nhere.\n\n\nTraining procedure\n------------------",
"### Preprocessing\n\n\nThe texts are tokenized using a byte-level version of Byte Pair Encoding (BPE) (for unicode characters) and a\nvocabulary size of 50,257. The inputs are sequences of 1024 consecutive tokens.\n\n\nThe larger model was trained on 256 cloud TPU v3 cores. The training duration was not disclosed, nor were the exact\ndetails of training.\n\n\nEvaluation results\n------------------\n\n\nThe model achieves the following results without any fine-tuning (zero-shot):",
"### BibTeX entry and citation info\n\n\n<a href=\"URL\n\\t<img width=\"300px\" src=\"URL"
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null | null | generic | ## Keras semantic segmentation models on the 🤗Hub! 🐶 🐕 🐩
Image classification task tells us about a class assigned to an image, and object detection task creates a boundary box on an object in an image. But what if we want to know about the shape of the image? Segmentation models helps us segment images and reveal their shapes. It has many variants. You can host your Keras segmentation models on the Hub.
Semantic segmentation models classify pixels, meaning, they assign a class (can be cat or dog) to each pixel. The output of a model looks like following.
We need to get the best prediction for every pixel.
This is still not readable. We have to convert this into different binary masks for each class and convert to a readable format by converting each mask into base64. We will return a list of dicts, and for each dictionary, we have the label itself, the base64 code and a score (semantic segmentation models don't return a score, so we have to return 1.0 for this case). You can find the full implementation in ```pipeline.py```.
Now that you know the expected output by the model, you can host your Keras segmentation models (and other semantic segmentation models) in the similar fashion. Try it yourself and host your segmentation models!
 | {"license": "apache-2.0", "library_name": "generic", "tags": ["image-segmentation", "generic"], "pipeline_tag": "image-segmentation", "dataset": ["oxfort-iit pets"]} | image-segmentation | Narsil/pet-segmentation | [
"generic",
"tf",
"image-segmentation",
"license:apache-2.0",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#generic #tf #image-segmentation #license-apache-2.0 #has_space #region-us
| ## Keras semantic segmentation models on the Hub!
Image classification task tells us about a class assigned to an image, and object detection task creates a boundary box on an object in an image. But what if we want to know about the shape of the image? Segmentation models helps us segment images and reveal their shapes. It has many variants. You can host your Keras segmentation models on the Hub.
Semantic segmentation models classify pixels, meaning, they assign a class (can be cat or dog) to each pixel. The output of a model looks like following.
!Raw Output
We need to get the best prediction for every pixel.
!Mask
This is still not readable. We have to convert this into different binary masks for each class and convert to a readable format by converting each mask into base64. We will return a list of dicts, and for each dictionary, we have the label itself, the base64 code and a score (semantic segmentation models don't return a score, so we have to return 1.0 for this case). You can find the full implementation in .
!Binary Mask
Now that you know the expected output by the model, you can host your Keras segmentation models (and other semantic segmentation models) in the similar fashion. Try it yourself and host your segmentation models!
!Segmented Cat | [
"## Keras semantic segmentation models on the Hub! \n\nImage classification task tells us about a class assigned to an image, and object detection task creates a boundary box on an object in an image. But what if we want to know about the shape of the image? Segmentation models helps us segment images and reveal their shapes. It has many variants. You can host your Keras segmentation models on the Hub.\nSemantic segmentation models classify pixels, meaning, they assign a class (can be cat or dog) to each pixel. The output of a model looks like following.\n!Raw Output\nWe need to get the best prediction for every pixel.\n!Mask\nThis is still not readable. We have to convert this into different binary masks for each class and convert to a readable format by converting each mask into base64. We will return a list of dicts, and for each dictionary, we have the label itself, the base64 code and a score (semantic segmentation models don't return a score, so we have to return 1.0 for this case). You can find the full implementation in .\n!Binary Mask\nNow that you know the expected output by the model, you can host your Keras segmentation models (and other semantic segmentation models) in the similar fashion. Try it yourself and host your segmentation models!\n!Segmented Cat"
] | [
"TAGS\n#generic #tf #image-segmentation #license-apache-2.0 #has_space #region-us \n",
"## Keras semantic segmentation models on the Hub! \n\nImage classification task tells us about a class assigned to an image, and object detection task creates a boundary box on an object in an image. But what if we want to know about the shape of the image? Segmentation models helps us segment images and reveal their shapes. It has many variants. You can host your Keras segmentation models on the Hub.\nSemantic segmentation models classify pixels, meaning, they assign a class (can be cat or dog) to each pixel. The output of a model looks like following.\n!Raw Output\nWe need to get the best prediction for every pixel.\n!Mask\nThis is still not readable. We have to convert this into different binary masks for each class and convert to a readable format by converting each mask into base64. We will return a list of dicts, and for each dictionary, we have the label itself, the base64 code and a score (semantic segmentation models don't return a score, so we have to return 1.0 for this case). You can find the full implementation in .\n!Binary Mask\nNow that you know the expected output by the model, you can host your Keras segmentation models (and other semantic segmentation models) in the similar fashion. Try it yourself and host your segmentation models!\n!Segmented Cat"
] | [
30,
304
] | [
"passage: TAGS\n#generic #tf #image-segmentation #license-apache-2.0 #has_space #region-us \n## Keras semantic segmentation models on the Hub! \n\nImage classification task tells us about a class assigned to an image, and object detection task creates a boundary box on an object in an image. But what if we want to know about the shape of the image? Segmentation models helps us segment images and reveal their shapes. It has many variants. You can host your Keras segmentation models on the Hub.\nSemantic segmentation models classify pixels, meaning, they assign a class (can be cat or dog) to each pixel. The output of a model looks like following.\n!Raw Output\nWe need to get the best prediction for every pixel.\n!Mask\nThis is still not readable. We have to convert this into different binary masks for each class and convert to a readable format by converting each mask into base64. We will return a list of dicts, and for each dictionary, we have the label itself, the base64 code and a score (semantic segmentation models don't return a score, so we have to return 1.0 for this case). You can find the full implementation in .\n!Binary Mask\nNow that you know the expected output by the model, you can host your Keras segmentation models (and other semantic segmentation models) in the similar fashion. Try it yourself and host your segmentation models!\n!Segmented Cat"
] | [
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null | null | transformers | Small change. again. again ? again.
| {} | token-classification | Narsil/small | [
"transformers",
"tf",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #tf #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us
| Small change. again. again ? again.
| [] | [
"TAGS\n#transformers #tf #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
36
] | [
"passage: TAGS\n#transformers #tf #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us \n"
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null | null | transformers | Small change. again. again ? again.
| {} | token-classification | Narsil/small2 | [
"transformers",
"pytorch",
"tf",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us
| Small change. again. again ? again.
| [] | [
"TAGS\n#transformers #pytorch #tf #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
40
] | [
"passage: TAGS\n#transformers #pytorch #tf #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
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] |
null | null | transformers | ```python
import tempfile
from tokenizers import Tokenizer, models, processors
from transformers.tokenization_utils_fast import PreTrainedTokenizerFast
vocab = [(chr(i), i) for i in range(256)]
tokenizer = Tokenizer(models.Unigram(vocab))
tokenizer.add_special_tokens(["<bos>", "<eos>"])
tokenizer.post_processor = processors.TemplateProcessing(
single="<bos> $0 <eos>", special_tokens=[("<bos>", 256), ("<eos>", 257)]
)
with tempfile.NamedTemporaryFile() as f:
tokenizer.save(f.name)
real_tokenizer = PreTrainedTokenizerFast(tokenizer_file=f.name, eos_token="<eos>", bos_token="<bos>")
real_tokenizer._tokenizer.save("dummy.json")
```
Small change.
| {} | null | Narsil/small_conversational_test | [
"transformers",
"albert",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #albert #endpoints_compatible #region-us
|
Small change.
| [] | [
"TAGS\n#transformers #albert #endpoints_compatible #region-us \n"
] | [
20
] | [
"passage: TAGS\n#transformers #albert #endpoints_compatible #region-us \n"
] | [
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] |
null | null | transformers | ```python
import tempfile
from tokenizers import Tokenizer, models
from transformers import PreTrainedTokenizerFast
model_max_length = 4
vocab = [(chr(i), i) for i in range(256)]
tokenizer = Tokenizer(models.Unigram(vocab))
with tempfile.NamedTemporaryFile() as f:
tokenizer.save(f.name)
real_tokenizer = PreTrainedTokenizerFast(tokenizer_file=f.name, model_max_length=model_max_length)
real_tokenizer._tokenizer.save("dummy/tokenizer.json")
```
config uses Albert which works with a minimal `config.json` | {} | null | Narsil/small_summarization_test | [
"transformers",
"albert",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #albert #endpoints_compatible #region-us
|
config uses Albert which works with a minimal 'URL' | [] | [
"TAGS\n#transformers #albert #endpoints_compatible #region-us \n"
] | [
20
] | [
"passage: TAGS\n#transformers #albert #endpoints_compatible #region-us \n"
] | [
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null | null | null | Говорили: "Погоди", уходил с дождём
Эта ночь нужна, переваривал сон
Вы порвали паруса, ожидая восторг
Это мой Тачтаун, это мой Гонконг
Надо созерцать, и не более того
Либо до конца переполох
Хитроматы пустот, наливай по сто
Забывай мой голос и меня самого
Забывай мой рай, я пропитый бадман
Добровольно приговаривал, а вам по делом
Заливал до дна, дабы дать по щам
Не хочу себя жалеть, и не буду прощать
Этот мир не смог меня сохранить
Потеряли головы, теряя нить
Во время дабы любить без обид и жить
Не забыть нам бед, и незачем творить
Ночи в одного, ночи в одного
Холили, лелеяли убитого меня собой
Ночи в одного, ночи в одного
Верили в меня, как никогда, никто и ни в кого
Ночи в одного, ночи в одного
Холили, лелеяли убитого меня собой
Ночи в одного, ночи в одного
Верили в меня, как никогда, никто и ни в кого | {} | null | Nasvai1702/Night | [
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#region-us
| Говорили: "Погоди", уходил с дождём
Эта ночь нужна, переваривал сон
Вы порвали паруса, ожидая восторг
Это мой Тачтаун, это мой Гонконг
Надо созерцать, и не более того
Либо до конца переполох
Хитроматы пустот, наливай по сто
Забывай мой голос и меня самого
Забывай мой рай, я пропитый бадман
Добровольно приговаривал, а вам по делом
Заливал до дна, дабы дать по щам
Не хочу себя жалеть, и не буду прощать
Этот мир не смог меня сохранить
Потеряли головы, теряя нить
Во время дабы любить без обид и жить
Не забыть нам бед, и незачем творить
Ночи в одного, ночи в одного
Холили, лелеяли убитого меня собой
Ночи в одного, ночи в одного
Верили в меня, как никогда, никто и ни в кого
Ночи в одного, ночи в одного
Холили, лелеяли убитого меня собой
Ночи в одного, ночи в одного
Верили в меня, как никогда, никто и ни в кого | [] | [
"TAGS\n#region-us \n"
] | [
6
] | [
"passage: TAGS\n#region-us \n"
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null | null | transformers | Test for use in Google Colab :'( | {} | text-classification | NathanZhu/GabHateCorpusTrained | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| Test for use in Google Colab :'( | [] | [
"TAGS\n#transformers #pytorch #jax #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
39
] | [
"passage: TAGS\n#transformers #pytorch #jax #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
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null | null | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | text-generation | Naturealbe/DialoGPT-small-harrypotter-2 | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Harry Potter DialoGPT Model | [
"# Harry Potter DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Harry Potter DialoGPT Model"
] | [
51,
8
] | [
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Harry Potter DialoGPT Model"
] | [
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null | null | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | text-generation | Naturealbe/DialoGPT-small-harrypotter | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Harry Potter DialoGPT Model | [
"# Harry Potter DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Harry Potter DialoGPT Model"
] | [
51,
8
] | [
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Harry Potter DialoGPT Model"
] | [
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null | null | transformers |
# Welcome to KanBERTo (ಕನ್ಬರ್ಟೋ)
## Model Description
> This is a small language model for [Kannada](https://en.wikipedia.org/wiki/Kannada) language with 1M data samples taken from
[OSCAR page](https://traces1.inria.fr/oscar/files/compressed-orig/kn.txt.gz)
## Training params
- **Dataset** - 1M data samples are used to train this model from OSCAR page(https://traces1.inria.fr/oscar/) eventhough data set is of 1.7 GB due to resource constraint to train
I have picked only 1M data from the total 1.7GB data set. If you are interested in collaboration and have computational resources to train on you are most welcome to do so.
- **Preprocessing** - ByteLevelBPETokenizer is used to tokenize the sentences at character level and vocabulary size is set to 52k as per standard values given by 🤗
- **Hyperparameters** - __ByteLevelBPETokenizer__ : vocabulary size = 52_000 and min_frequency = 2
__Trainer__ : num_train_epochs=12 - trained for 12 epochs
per_gpu_train_batch_size=64 - batch size for the datasamples is 64
save_steps=10_000 - save model for every 10k steps
save_total_limit=2 - save limit is set for 2
**Intended uses & limitations**
this is for anyone who wants to make use of kannada language models for various tasks like language generation, translation and many more use cases.
**Whatever else is helpful!**
If you are intersted in collaboration feel free to reach me [Naveen](mailto:[email protected])
| {"language": "kn"} | fill-mask | Naveen-k/KanBERTo | [
"transformers",
"pytorch",
"jax",
"roberta",
"fill-mask",
"kn",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"kn"
] | TAGS
#transformers #pytorch #jax #roberta #fill-mask #kn #autotrain_compatible #endpoints_compatible #region-us
|
# Welcome to KanBERTo (ಕನ್ಬರ್ಟೋ)
## Model Description
> This is a small language model for Kannada language with 1M data samples taken from
OSCAR page
## Training params
- Dataset - 1M data samples are used to train this model from OSCAR page(URL eventhough data set is of 1.7 GB due to resource constraint to train
I have picked only 1M data from the total 1.7GB data set. If you are interested in collaboration and have computational resources to train on you are most welcome to do so.
- Preprocessing - ByteLevelBPETokenizer is used to tokenize the sentences at character level and vocabulary size is set to 52k as per standard values given by
- Hyperparameters - __ByteLevelBPETokenizer__ : vocabulary size = 52_000 and min_frequency = 2
__Trainer__ : num_train_epochs=12 - trained for 12 epochs
per_gpu_train_batch_size=64 - batch size for the datasamples is 64
save_steps=10_000 - save model for every 10k steps
save_total_limit=2 - save limit is set for 2
Intended uses & limitations
this is for anyone who wants to make use of kannada language models for various tasks like language generation, translation and many more use cases.
Whatever else is helpful!
If you are intersted in collaboration feel free to reach me Naveen
| [
"# Welcome to KanBERTo (ಕನ್ಬರ್ಟೋ)",
"## Model Description\n \n> This is a small language model for Kannada language with 1M data samples taken from\n OSCAR page",
"## Training params \n\n- Dataset - 1M data samples are used to train this model from OSCAR page(URL eventhough data set is of 1.7 GB due to resource constraint to train \nI have picked only 1M data from the total 1.7GB data set. If you are interested in collaboration and have computational resources to train on you are most welcome to do so.\n\n- Preprocessing - ByteLevelBPETokenizer is used to tokenize the sentences at character level and vocabulary size is set to 52k as per standard values given by \n- Hyperparameters - __ByteLevelBPETokenizer__ : vocabulary size = 52_000 and min_frequency = 2\n __Trainer__ : num_train_epochs=12 - trained for 12 epochs\n per_gpu_train_batch_size=64 - batch size for the datasamples is 64\n save_steps=10_000 - save model for every 10k steps\n save_total_limit=2 - save limit is set for 2\n\nIntended uses & limitations\n this is for anyone who wants to make use of kannada language models for various tasks like language generation, translation and many more use cases.\n\nWhatever else is helpful!\n If you are intersted in collaboration feel free to reach me Naveen"
] | [
"TAGS\n#transformers #pytorch #jax #roberta #fill-mask #kn #autotrain_compatible #endpoints_compatible #region-us \n",
"# Welcome to KanBERTo (ಕನ್ಬರ್ಟೋ)",
"## Model Description\n \n> This is a small language model for Kannada language with 1M data samples taken from\n OSCAR page",
"## Training params \n\n- Dataset - 1M data samples are used to train this model from OSCAR page(URL eventhough data set is of 1.7 GB due to resource constraint to train \nI have picked only 1M data from the total 1.7GB data set. If you are interested in collaboration and have computational resources to train on you are most welcome to do so.\n\n- Preprocessing - ByteLevelBPETokenizer is used to tokenize the sentences at character level and vocabulary size is set to 52k as per standard values given by \n- Hyperparameters - __ByteLevelBPETokenizer__ : vocabulary size = 52_000 and min_frequency = 2\n __Trainer__ : num_train_epochs=12 - trained for 12 epochs\n per_gpu_train_batch_size=64 - batch size for the datasamples is 64\n save_steps=10_000 - save model for every 10k steps\n save_total_limit=2 - save limit is set for 2\n\nIntended uses & limitations\n this is for anyone who wants to make use of kannada language models for various tasks like language generation, translation and many more use cases.\n\nWhatever else is helpful!\n If you are intersted in collaboration feel free to reach me Naveen"
] | [
42,
12,
24,
288
] | [
"passage: TAGS\n#transformers #pytorch #jax #roberta #fill-mask #kn #autotrain_compatible #endpoints_compatible #region-us \n# Welcome to KanBERTo (ಕನ್ಬರ್ಟೋ)## Model Description\n \n> This is a small language model for Kannada language with 1M data samples taken from\n OSCAR page## Training params \n\n- Dataset - 1M data samples are used to train this model from OSCAR page(URL eventhough data set is of 1.7 GB due to resource constraint to train \nI have picked only 1M data from the total 1.7GB data set. If you are interested in collaboration and have computational resources to train on you are most welcome to do so.\n\n- Preprocessing - ByteLevelBPETokenizer is used to tokenize the sentences at character level and vocabulary size is set to 52k as per standard values given by \n- Hyperparameters - __ByteLevelBPETokenizer__ : vocabulary size = 52_000 and min_frequency = 2\n __Trainer__ : num_train_epochs=12 - trained for 12 epochs\n per_gpu_train_batch_size=64 - batch size for the datasamples is 64\n save_steps=10_000 - save model for every 10k steps\n save_total_limit=2 - save limit is set for 2\n\nIntended uses & limitations\n this is for anyone who wants to make use of kannada language models for various tasks like language generation, translation and many more use cases.\n\nWhatever else is helpful!\n If you are intersted in collaboration feel free to reach me Naveen"
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null | null | transformers |
#Marty McFly model | {"tags": ["conversational"]} | text-generation | Navigator/DialoGPT-medium-martymcfly | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#Marty McFly model | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
51
] | [
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
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null | null | transformers | # Chandler Bing DialoGPT Model | {"tags": ["conversational"]} | text-generation | Navya2608/DialoGPT-medium-chandler | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # Chandler Bing DialoGPT Model | [
"# Chandler Bing DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Chandler Bing DialoGPT Model"
] | [
51,
9
] | [
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Chandler Bing DialoGPT Model"
] | [
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null | null | transformers | # Rachel Green DialoGPT Model | {"tags": ["conversational"]} | text-generation | Navya2608/DialoGPT-medium-rachel | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # Rachel Green DialoGPT Model | [
"# Rachel Green DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Rachel Green DialoGPT Model"
] | [
51,
8
] | [
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Rachel Green DialoGPT Model"
] | [
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null | null | transformers |
# Tony Stark dialoGPT model | {"tags": ["conversational"]} | text-generation | Navya2608/DialoGPT-small-tonystarkscript | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Tony Stark dialoGPT model | [
"# Tony Stark dialoGPT model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Tony Stark dialoGPT model"
] | [
51,
9
] | [
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Tony Stark dialoGPT model"
] | [
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null | null | null |
# Norwegian Wav2Vec2 Model - 1B - Bokmål
This achieves the following results on the test set with a 5-gram KenLM:
- WER: 0.0668
- CER: 0.0256
Without using a language model, we are getting these results:
- WER: ???
- CER: ???
## Model description
This is one of several Wav2Vec-models created during the 🤗 hosted [Robust Speech Event](https://discuss.huggingface.co/t/open-to-the-community-robust-speech-recognition-challenge/13614?s=09). In parallell with the event, the team also converted the [Norwegian Parliamentary Speech Corpus (NPSC)](https://huggingface.co/datasets/NbAiLab/NPSC) to the 🤗 Dataset format and used that as the main source for training.
We do release all code developed during the event so that the Norwegian NLP community can build upon this to develop even better Norwegian ASR models. The finetuning of these models are not very compute demanding. You should after following the instructions here, be able to train your own automatic speech recognition system in less than a day with an average GPU.
## Team
The following people contributed to building this model: Rolv-Arild Braaten, Per Egil Kummervold, Andre Kåsen, Javier de la Rosa, Per Erik Solberg, and Freddy Wetjen.
## Training procedure
To reproduce these results, we strongly recommend that you follow the [instructions from HuggingFace](https://github.com/huggingface/transformers/tree/master/examples/research_projects/robust-speech-event#talks) to train a simple Swedish model.
When you have verified that you are able to do this, create a fresh new repo. You can then start by copying the files ```run.sh``` and ```run_speech_recognition_ctc.py``` from our repo. Running this will create all the other necessary files, and should let you reproduce our results. With some tweaks to the hyperparameters, you might even be able to build an even better ASR. Good luck!
### Language Model
As you see from the results above, adding even a simple 5-gram language will significantly improve the results. 🤗 has provided another [very nice blog](https://huggingface.co/blog/wav2vec2-with-ngram) about how to add a 5-gram language model to improve the ASR model. You can build this from your own corpus, for instance by extracting some suitable text from the [Norwegian Colossal Corpus](https://huggingface.co/datasets/NbAiLab/NCC). You can also skip some of the steps in the guide, and copy the [5-gram model from this repo](https://huggingface.co/NbAiLab/XLSR-300M-bokmaal/tree/main/language_model).
### Parameters
The following parameters were used during training:
```
--dataset_name="NbAiLab/NPSC"
--model_name_or_path="facebook/wav2vec2-xls-r-1b"
--dataset_config_name="16K_mp3_bokmaal"
--output_dir="./"
--overwrite_output_dir
--num_train_epochs="40"
--per_device_train_batch_size="12"
--per_device_eval_batch_size="12"
--gradient_accumulation_steps="2"
--learning_rate="2e-5"
--warmup_steps="2000"
--length_column_name="input_length"
--evaluation_strategy="steps"
--text_column_name="text"
--save_steps="500"
--eval_steps="500"
--logging_steps="100"
--layerdrop="0.041"
--attention_dropout="0.094"
--activation_dropout="0.055"
--hidden_dropout="0.047"
--save_total_limit="3"
--freeze_feature_encoder
--feat_proj_dropout="0.04"
--mask_time_prob="0.082"
--mask_time_length="10"
--mask_feature_prob="0.25"
--mask_feature_length="64"
--gradient_checkpointing
--min_duration_in_seconds="0.5"
--max_duration_in_seconds="30.0"
--ctc_zero_infinity=True
--use_auth_token
--seed="42"
--fp16
--group_by_length
--do_train --do_eval
--push_to_hub
--preprocessing_num_workers="16"
```
Following this settings, the training might take 3-4 days on an average GPU. You should however get a decent model and faster results by tweaking these parameters
| Parameter| Comment |
|:-------------|:-----|
| per_device_train_batch_size | Adjust this to the maximum of available memory. 16 or 24 might be good settings depending on your system |
|gradient_accumulation_steps |Can be adjusted even further up to increase batch size and speed up training without running into memory issues |
| learning_rate|Can be increased, maybe as high as 1e-4. Speeds up training but might add instability |
| epochs| Can be decreased significantly. This is a huge dataset and you might get a decent result already after a couple of epochs|
| {"language": ["nb-NO"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "NbAiLab/NPSC", "xxx-robust-speech-event", false, "nb-NO"], "datasets": ["NbAiLab/NPSC"], "model-index": [{"name": "wav2vec2-xls-r-1b-npsc-bokmaal-low-27k", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "NPSC", "type": "NbAiLab/NPSC", "args": "16K_mp3_bokmaal"}, "metrics": [{"type": "wer", "value": 0.06686424124625939, "name": "Test (Bokm\u00e5l) WER"}, {"type": "cer", "value": 0.025697763468940576, "name": "Test (Bokm\u00e5l) CER"}]}]}]} | automatic-speech-recognition | NbAiLab/Wav2Vec-Template | [
"automatic-speech-recognition",
"NbAiLab/NPSC",
"xxx-robust-speech-event",
"no",
"nb-NO",
"dataset:NbAiLab/NPSC",
"license:apache-2.0",
"model-index",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"nb-NO"
] | TAGS
#automatic-speech-recognition #NbAiLab/NPSC #xxx-robust-speech-event #no #nb-NO #dataset-NbAiLab/NPSC #license-apache-2.0 #model-index #region-us
| Norwegian Wav2Vec2 Model - 1B - Bokmål
======================================
This achieves the following results on the test set with a 5-gram KenLM:
* WER: 0.0668
* CER: 0.0256
Without using a language model, we are getting these results:
* WER: ???
* CER: ???
Model description
-----------------
This is one of several Wav2Vec-models created during the hosted Robust Speech Event. In parallell with the event, the team also converted the Norwegian Parliamentary Speech Corpus (NPSC) to the Dataset format and used that as the main source for training.
We do release all code developed during the event so that the Norwegian NLP community can build upon this to develop even better Norwegian ASR models. The finetuning of these models are not very compute demanding. You should after following the instructions here, be able to train your own automatic speech recognition system in less than a day with an average GPU.
Team
----
The following people contributed to building this model: Rolv-Arild Braaten, Per Egil Kummervold, Andre Kåsen, Javier de la Rosa, Per Erik Solberg, and Freddy Wetjen.
Training procedure
------------------
To reproduce these results, we strongly recommend that you follow the instructions from HuggingFace to train a simple Swedish model.
When you have verified that you are able to do this, create a fresh new repo. You can then start by copying the files and from our repo. Running this will create all the other necessary files, and should let you reproduce our results. With some tweaks to the hyperparameters, you might even be able to build an even better ASR. Good luck!
### Language Model
As you see from the results above, adding even a simple 5-gram language will significantly improve the results. has provided another very nice blog about how to add a 5-gram language model to improve the ASR model. You can build this from your own corpus, for instance by extracting some suitable text from the Norwegian Colossal Corpus. You can also skip some of the steps in the guide, and copy the 5-gram model from this repo.
### Parameters
The following parameters were used during training:
Following this settings, the training might take 3-4 days on an average GPU. You should however get a decent model and faster results by tweaking these parameters
| [
"### Language Model\n\n\nAs you see from the results above, adding even a simple 5-gram language will significantly improve the results. has provided another very nice blog about how to add a 5-gram language model to improve the ASR model. You can build this from your own corpus, for instance by extracting some suitable text from the Norwegian Colossal Corpus. You can also skip some of the steps in the guide, and copy the 5-gram model from this repo.",
"### Parameters\n\n\nThe following parameters were used during training:\n\n\nFollowing this settings, the training might take 3-4 days on an average GPU. You should however get a decent model and faster results by tweaking these parameters"
] | [
"TAGS\n#automatic-speech-recognition #NbAiLab/NPSC #xxx-robust-speech-event #no #nb-NO #dataset-NbAiLab/NPSC #license-apache-2.0 #model-index #region-us \n",
"### Language Model\n\n\nAs you see from the results above, adding even a simple 5-gram language will significantly improve the results. has provided another very nice blog about how to add a 5-gram language model to improve the ASR model. You can build this from your own corpus, for instance by extracting some suitable text from the Norwegian Colossal Corpus. You can also skip some of the steps in the guide, and copy the 5-gram model from this repo.",
"### Parameters\n\n\nThe following parameters were used during training:\n\n\nFollowing this settings, the training might take 3-4 days on an average GPU. You should however get a decent model and faster results by tweaking these parameters"
] | [
64,
94,
51
] | [
"passage: TAGS\n#automatic-speech-recognition #NbAiLab/NPSC #xxx-robust-speech-event #no #nb-NO #dataset-NbAiLab/NPSC #license-apache-2.0 #model-index #region-us \n### Language Model\n\n\nAs you see from the results above, adding even a simple 5-gram language will significantly improve the results. has provided another very nice blog about how to add a 5-gram language model to improve the ASR model. You can build this from your own corpus, for instance by extracting some suitable text from the Norwegian Colossal Corpus. You can also skip some of the steps in the guide, and copy the 5-gram model from this repo.### Parameters\n\n\nThe following parameters were used during training:\n\n\nFollowing this settings, the training might take 3-4 days on an average GPU. You should however get a decent model and faster results by tweaking these parameters"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# XLSR-1B-bokmaal-low
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1579
- Wer: 0.0722
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1.7e-05
- train_batch_size: 12
- eval_batch_size: 12
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 24
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 34.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.434 | 0.24 | 500 | 0.1704 | 0.1378 |
| 0.2833 | 0.48 | 1000 | 0.1638 | 0.1324 |
| 0.2478 | 0.72 | 1500 | 0.1606 | 0.1240 |
| 0.2276 | 0.97 | 2000 | 0.1562 | 0.1212 |
| 0.2208 | 1.21 | 2500 | 0.1576 | 0.1172 |
| 0.2148 | 1.45 | 3000 | 0.1502 | 0.1119 |
| 0.1994 | 1.69 | 3500 | 0.1409 | 0.1110 |
| 0.1932 | 1.93 | 4000 | 0.1432 | 0.1112 |
| 0.2122 | 2.17 | 4500 | 0.1443 | 0.1098 |
| 0.2177 | 2.42 | 5000 | 0.1329 | 0.1102 |
| 0.2058 | 2.66 | 5500 | 0.1403 | 0.1070 |
| 0.2216 | 2.9 | 6000 | 0.1342 | 0.1067 |
| 0.1984 | 3.14 | 6500 | 0.1370 | 0.1030 |
| 0.2056 | 3.38 | 7000 | 0.1371 | 0.1041 |
| 0.1735 | 3.62 | 7500 | 0.1296 | 0.1003 |
| 0.203 | 3.87 | 8000 | 0.1301 | 0.1005 |
| 0.1835 | 4.11 | 8500 | 0.1310 | 0.1004 |
| 0.178 | 4.35 | 9000 | 0.1300 | 0.0959 |
| 0.1585 | 4.59 | 9500 | 0.1277 | 0.0966 |
| 0.1848 | 4.83 | 10000 | 0.1260 | 0.0974 |
| 0.169 | 5.07 | 10500 | 0.1281 | 0.0969 |
| 0.1666 | 5.32 | 11000 | 0.1291 | 0.1003 |
| 0.1552 | 5.56 | 11500 | 0.1271 | 0.0959 |
| 0.2736 | 5.8 | 12000 | 0.1320 | 0.0935 |
| 0.2845 | 6.04 | 12500 | 0.1299 | 0.0921 |
| 0.1536 | 6.28 | 13000 | 0.1282 | 0.0927 |
| 0.1491 | 6.52 | 13500 | 0.1240 | 0.0906 |
| 0.1579 | 6.77 | 14000 | 0.1208 | 0.0921 |
| 0.16 | 7.01 | 14500 | 0.1182 | 0.0903 |
| 0.1367 | 7.25 | 15000 | 0.1214 | 0.0922 |
| 0.1499 | 7.49 | 15500 | 0.1232 | 0.0916 |
| 0.148 | 7.73 | 16000 | 0.1184 | 0.0896 |
| 0.1426 | 7.97 | 16500 | 0.1201 | 0.0889 |
| 0.1471 | 8.22 | 17000 | 0.1256 | 0.0882 |
| 0.1358 | 8.46 | 17500 | 0.1265 | 0.0909 |
| 0.1245 | 8.7 | 18000 | 0.1263 | 0.0886 |
| 0.1407 | 8.94 | 18500 | 0.1226 | 0.0885 |
| 0.1289 | 9.18 | 19000 | 0.1315 | 0.0873 |
| 0.1326 | 9.42 | 19500 | 0.1233 | 0.0868 |
| 0.1305 | 9.67 | 20000 | 0.1237 | 0.0870 |
| 0.1432 | 9.91 | 20500 | 0.1234 | 0.0857 |
| 0.1205 | 10.15 | 21000 | 0.1303 | 0.0858 |
| 0.1248 | 10.39 | 21500 | 0.1252 | 0.0858 |
| 0.1251 | 10.63 | 22000 | 0.1253 | 0.0869 |
| 0.1143 | 10.87 | 22500 | 0.1266 | 0.0860 |
| 0.1155 | 11.12 | 23000 | 0.1219 | 0.0862 |
| 0.1227 | 11.36 | 23500 | 0.1329 | 0.0864 |
| 0.1229 | 11.6 | 24000 | 0.1244 | 0.0855 |
| 0.1112 | 11.84 | 24500 | 0.1356 | 0.0851 |
| 0.2163 | 12.08 | 25000 | 0.1252 | 0.0847 |
| 0.1146 | 12.32 | 25500 | 0.1211 | 0.0837 |
| 0.1058 | 12.57 | 26000 | 0.1247 | 0.0843 |
| 0.1099 | 12.81 | 26500 | 0.1189 | 0.0833 |
| 0.1028 | 13.05 | 27000 | 0.1303 | 0.0815 |
| 0.1092 | 13.29 | 27500 | 0.1305 | 0.0838 |
| 0.1076 | 13.53 | 28000 | 0.1276 | 0.0842 |
| 0.1074 | 13.77 | 28500 | 0.1268 | 0.0844 |
| 0.0971 | 14.02 | 29000 | 0.1322 | 0.0839 |
| 0.1109 | 14.26 | 29500 | 0.1287 | 0.0821 |
| 0.0991 | 14.5 | 30000 | 0.1289 | 0.0831 |
| 0.1095 | 14.74 | 30500 | 0.1273 | 0.0822 |
| 0.1015 | 14.98 | 31000 | 0.1326 | 0.0816 |
| 0.1051 | 15.22 | 31500 | 0.1337 | 0.0814 |
| 0.0894 | 15.47 | 32000 | 0.1331 | 0.0802 |
| 0.1 | 15.71 | 32500 | 0.1304 | 0.0798 |
| 0.0957 | 15.95 | 33000 | 0.1293 | 0.0824 |
| 0.0921 | 16.19 | 33500 | 0.1382 | 0.0808 |
| 0.0986 | 16.43 | 34000 | 0.1301 | 0.0788 |
| 0.098 | 16.67 | 34500 | 0.1305 | 0.0795 |
| 0.0974 | 16.92 | 35000 | 0.1325 | 0.0796 |
| 0.0886 | 17.16 | 35500 | 0.1332 | 0.0796 |
| 0.0892 | 17.4 | 36000 | 0.1327 | 0.0785 |
| 0.0917 | 17.64 | 36500 | 0.1304 | 0.0793 |
| 0.0919 | 17.88 | 37000 | 0.1353 | 0.0791 |
| 0.1007 | 18.12 | 37500 | 0.1340 | 0.0791 |
| 0.0831 | 18.37 | 38000 | 0.1327 | 0.0786 |
| 0.0862 | 18.61 | 38500 | 0.1343 | 0.0792 |
| 0.0837 | 18.85 | 39000 | 0.1334 | 0.0777 |
| 0.0771 | 19.09 | 39500 | 0.1456 | 0.0778 |
| 0.0841 | 19.33 | 40000 | 0.1365 | 0.0784 |
| 0.0874 | 19.57 | 40500 | 0.1379 | 0.0779 |
| 0.0773 | 19.82 | 41000 | 0.1359 | 0.0776 |
| 0.0771 | 20.06 | 41500 | 0.1392 | 0.0776 |
| 0.0861 | 20.3 | 42000 | 0.1395 | 0.0774 |
| 0.0773 | 20.54 | 42500 | 0.1356 | 0.0775 |
| 0.069 | 20.78 | 43000 | 0.1399 | 0.0765 |
| 0.0823 | 21.02 | 43500 | 0.1469 | 0.0774 |
| 0.0747 | 21.27 | 44000 | 0.1415 | 0.0768 |
| 0.0703 | 21.51 | 44500 | 0.1405 | 0.0778 |
| 0.0776 | 21.75 | 45000 | 0.1492 | 0.0778 |
| 0.0833 | 21.99 | 45500 | 0.1448 | 0.0767 |
| 0.0796 | 22.23 | 46000 | 0.1434 | 0.0761 |
| 0.0613 | 22.47 | 46500 | 0.1446 | 0.0768 |
| 0.0753 | 22.72 | 47000 | 0.1439 | 0.0757 |
| 0.076 | 22.96 | 47500 | 0.1402 | 0.0759 |
| 0.0619 | 23.2 | 48000 | 0.1473 | 0.0767 |
| 0.1322 | 23.44 | 48500 | 0.1431 | 0.0766 |
| 0.0691 | 23.68 | 49000 | 0.1452 | 0.0753 |
| 0.061 | 23.92 | 49500 | 0.1452 | 0.0752 |
| 0.0716 | 24.17 | 50000 | 0.1429 | 0.0756 |
| 0.074 | 24.41 | 50500 | 0.1440 | 0.0746 |
| 0.0696 | 24.65 | 51000 | 0.1459 | 0.0756 |
| 0.081 | 24.89 | 51500 | 0.1443 | 0.0751 |
| 0.0754 | 25.13 | 52000 | 0.1483 | 0.0755 |
| 0.0864 | 25.37 | 52500 | 0.1467 | 0.0757 |
| 0.0662 | 25.62 | 53000 | 0.1471 | 0.0748 |
| 0.109 | 25.86 | 53500 | 0.1472 | 0.0759 |
| 0.0682 | 26.1 | 54000 | 0.1539 | 0.0748 |
| 0.0655 | 26.34 | 54500 | 0.1469 | 0.0743 |
| 0.0651 | 26.58 | 55000 | 0.1553 | 0.0748 |
| 0.0666 | 26.82 | 55500 | 0.1520 | 0.0744 |
| 0.0724 | 27.07 | 56000 | 0.1526 | 0.0738 |
| 0.067 | 27.31 | 56500 | 0.1489 | 0.0738 |
| 0.0658 | 27.55 | 57000 | 0.1518 | 0.0738 |
| 0.0581 | 27.79 | 57500 | 0.1518 | 0.0739 |
| 0.0639 | 28.03 | 58000 | 0.1495 | 0.0736 |
| 0.0606 | 28.27 | 58500 | 0.1549 | 0.0739 |
| 0.0641 | 28.52 | 59000 | 0.1513 | 0.0735 |
| 0.0612 | 28.76 | 59500 | 0.1524 | 0.0739 |
| 0.0536 | 29.0 | 60000 | 0.1565 | 0.0741 |
| 0.0574 | 29.24 | 60500 | 0.1541 | 0.0741 |
| 0.057 | 29.48 | 61000 | 0.1555 | 0.0741 |
| 0.0624 | 29.72 | 61500 | 0.1590 | 0.0736 |
| 0.0531 | 29.97 | 62000 | 0.1590 | 0.0734 |
| 0.0661 | 30.21 | 62500 | 0.1599 | 0.0732 |
| 0.0641 | 30.45 | 63000 | 0.1576 | 0.0730 |
| 0.0562 | 30.69 | 63500 | 0.1593 | 0.0734 |
| 0.0527 | 30.93 | 64000 | 0.1604 | 0.0730 |
| 0.0579 | 31.17 | 64500 | 0.1571 | 0.0734 |
| 0.0508 | 31.42 | 65000 | 0.1603 | 0.0733 |
| 0.0524 | 31.66 | 65500 | 0.1588 | 0.0726 |
| 0.0564 | 31.9 | 66000 | 0.1571 | 0.0727 |
| 0.0551 | 32.14 | 66500 | 0.1584 | 0.0728 |
| 0.0564 | 32.38 | 67000 | 0.1565 | 0.0726 |
| 0.0628 | 32.62 | 67500 | 0.1558 | 0.0725 |
| 0.0561 | 32.87 | 68000 | 0.1582 | 0.0727 |
| 0.0553 | 33.11 | 68500 | 0.1591 | 0.0726 |
| 0.0504 | 33.35 | 69000 | 0.1590 | 0.0725 |
| 0.0539 | 33.59 | 69500 | 0.1582 | 0.0723 |
| 0.0576 | 33.83 | 70000 | 0.1579 | 0.0722 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
| {"tags": ["generated_from_trainer"], "model-index": [{"name": "XLSR-1B-bokmaal-low", "results": []}]} | automatic-speech-recognition | NbAiLab/XLSR-1B-bokmaal-low | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #endpoints_compatible #region-us
| XLSR-1B-bokmaal-low
===================
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1579
* Wer: 0.0722
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 1.7e-05
* train\_batch\_size: 12
* eval\_batch\_size: 12
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 24
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 34.0
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.17.0.dev0
* Pytorch 1.10.0+cu113
* Datasets 1.18.3
* Tokenizers 0.10.3
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1.7e-05\n* train\\_batch\\_size: 12\n* eval\\_batch\\_size: 12\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 24\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 34.0\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.17.0.dev0\n* Pytorch 1.10.0+cu113\n* Datasets 1.18.3\n* Tokenizers 0.10.3"
] | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1.7e-05\n* train\\_batch\\_size: 12\n* eval\\_batch\\_size: 12\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 24\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 34.0\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.17.0.dev0\n* Pytorch 1.10.0+cu113\n* Datasets 1.18.3\n* Tokenizers 0.10.3"
] | [
53,
142,
4,
38
] | [
"passage: TAGS\n#transformers #pytorch #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1.7e-05\n* train\\_batch\\_size: 12\n* eval\\_batch\\_size: 12\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 24\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 34.0\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.17.0.dev0\n* Pytorch 1.10.0+cu113\n* Datasets 1.18.3\n* Tokenizers 0.10.3"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# XLSR-300M-bokmaal
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the NBAILAB/NPSC - 16K_MP3_BOKMAAL dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1635
- Wer: 0.1005
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- 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: 2000
- num_epochs: 15.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 3.0307 | 0.32 | 500 | 3.0026 | 1.0 |
| 2.7865 | 0.64 | 1000 | 2.4849 | 0.9926 |
| 0.7522 | 0.95 | 1500 | 0.4567 | 0.3594 |
| 0.5703 | 1.27 | 2000 | 0.3440 | 0.2586 |
| 0.4762 | 1.59 | 2500 | 0.2925 | 0.2178 |
| 0.4585 | 1.91 | 3000 | 0.2442 | 0.1981 |
| 0.4013 | 2.23 | 3500 | 0.2495 | 0.1818 |
| 0.449 | 2.54 | 4000 | 0.2152 | 0.1808 |
| 0.355 | 2.86 | 4500 | 0.2179 | 0.1670 |
| 0.3142 | 3.18 | 5000 | 0.1953 | 0.1542 |
| 0.3242 | 3.5 | 5500 | 0.2103 | 0.1526 |
| 0.3016 | 3.82 | 6000 | 0.1911 | 0.1477 |
| 0.2713 | 4.13 | 6500 | 0.1836 | 0.1422 |
| 0.2807 | 4.45 | 7000 | 0.1924 | 0.1447 |
| 0.2929 | 4.77 | 7500 | 0.1848 | 0.1402 |
| 0.2595 | 5.09 | 8000 | 0.1783 | 0.1330 |
| 0.2289 | 5.41 | 8500 | 0.1901 | 0.1313 |
| 0.2567 | 5.72 | 9000 | 0.1784 | 0.1298 |
| 0.2401 | 6.04 | 9500 | 0.1956 | 0.1298 |
| 0.2098 | 6.36 | 10000 | 0.1748 | 0.1277 |
| 0.2246 | 6.68 | 10500 | 0.1777 | 0.1254 |
| 0.2197 | 7.0 | 11000 | 0.1703 | 0.1222 |
| 0.2122 | 7.32 | 11500 | 0.1917 | 0.1221 |
| 0.2746 | 7.63 | 12000 | 0.1769 | 0.1215 |
| 0.2148 | 7.95 | 12500 | 0.1736 | 0.1193 |
| 0.1915 | 8.27 | 13000 | 0.1814 | 0.1161 |
| 0.2462 | 8.59 | 13500 | 0.1748 | 0.1166 |
| 0.1872 | 8.91 | 14000 | 0.1769 | 0.1133 |
| 0.1886 | 9.22 | 14500 | 0.1852 | 0.1143 |
| 0.1789 | 9.54 | 15000 | 0.1696 | 0.1126 |
| 0.1692 | 9.86 | 15500 | 0.1817 | 0.1122 |
| 0.1765 | 10.18 | 16000 | 0.1769 | 0.1093 |
| 0.1699 | 10.5 | 16500 | 0.1604 | 0.1084 |
| 0.1591 | 10.81 | 17000 | 0.1777 | 0.1080 |
| 0.1499 | 11.13 | 17500 | 0.1645 | 0.1074 |
| 0.163 | 11.45 | 18000 | 0.1704 | 0.1065 |
| 0.1597 | 11.77 | 18500 | 0.1576 | 0.1064 |
| 0.1484 | 12.09 | 19000 | 0.1637 | 0.1041 |
| 0.1464 | 12.4 | 19500 | 0.1631 | 0.1047 |
| 0.156 | 12.72 | 20000 | 0.1686 | 0.1029 |
| 0.1625 | 13.04 | 20500 | 0.1648 | 0.1023 |
| 0.1395 | 13.36 | 21000 | 0.1688 | 0.1027 |
| 0.1387 | 13.68 | 21500 | 0.1670 | 0.1013 |
| 0.1434 | 13.99 | 22000 | 0.1677 | 0.1017 |
| 0.1442 | 14.31 | 22500 | 0.1688 | 0.1008 |
| 0.1439 | 14.63 | 23000 | 0.1647 | 0.1004 |
| 0.137 | 14.95 | 23500 | 0.1636 | 0.1006 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0
| {"language": ["nb-NO"], "license": "apache-2.0", "tags": ["generated_from_trainer", "automatic-speech-recognition", "NbAiLab/NPSC", "robust-speech-event", false, "nb-NO", "hf-asr-leaderboard"], "datasets": ["NbAiLab/NPSC"], "model-index": [{"name": "XLSR-300M-bokmaal", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "NPSC", "type": "NbAiLab/NPSC", "args": "16K_mp3_bokmaal"}, "metrics": [{"type": "wer", "value": 0.07699635320946434, "name": "Test (Bokm\u00e5l) WER"}, {"type": "cer", "value": 0.0284288464829, "name": "Test (Bokm\u00e5l) CER"}]}]}]} | automatic-speech-recognition | NbAiLab/XLSR-300M-bokmaal | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"dataset:NbAiLab/NPSC",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"nb-NO"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #dataset-NbAiLab/NPSC #license-apache-2.0 #model-index #endpoints_compatible #region-us
| XLSR-300M-bokmaal
=================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the NBAILAB/NPSC - 16K\_MP3\_BOKMAAL dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1635
* Wer: 0.1005
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0001
* train\_batch\_size: 16
* eval\_batch\_size: 16
* 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: 2000
* num\_epochs: 15.0
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.17.0.dev0
* Pytorch 1.10.1+cu102
* Datasets 1.18.2.dev0
* Tokenizers 0.11.0
| [
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"### Training results",
"### Framework versions\n\n\n* Transformers 4.17.0.dev0\n* Pytorch 1.10.1+cu102\n* Datasets 1.18.2.dev0\n* Tokenizers 0.11.0"
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"passage: TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #dataset-NbAiLab/NPSC #license-apache-2.0 #model-index #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 2000\n* num\\_epochs: 15.0\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.17.0.dev0\n* Pytorch 1.10.1+cu102\n* Datasets 1.18.2.dev0\n* Tokenizers 0.11.0"
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null | null | transformers | {"language": ["nn-NO"], "license": "apache-2.0", "tags": ["generated_from_trainer", "automatic-speech-recognition", "NbAiLab/NPSC", "robust-speech-event", false, "nn-NO", "hf-asr-leaderboard"], "datasets": ["NbAiLab/NPSC"], "model-index": [{"name": "XLSR-300M-nynorsk", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "NPSC", "type": "NbAiLab/NPSC", "args": "16K_mp3_nynorsk"}, "metrics": [{"type": "wer", "value": 0.12136286840623241, "name": "Test (Nynorsk) WER"}, {"type": "cer", "value": 0.041988362534453025, "name": "Test (Nynorsk) CER"}]}]}]} | automatic-speech-recognition | NbAiLab/XLSR-300M-nynorsk | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"dataset:NbAiLab/NPSC",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"nn-NO"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #dataset-NbAiLab/NPSC #license-apache-2.0 #model-index #endpoints_compatible #region-us
| [] | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #dataset-NbAiLab/NPSC #license-apache-2.0 #model-index #endpoints_compatible #region-us \n"
] | [
64
] | [
"passage: TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #dataset-NbAiLab/NPSC #license-apache-2.0 #model-index #endpoints_compatible #region-us \n"
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||
null | null | transformers |
**Release 1.0** (March 11, 2021)
# NB-Bert base model finetuned on Norwegian machine translated MNLI
## Description
The most effective way of creating a good classifier is to finetune a pre-trained model for the specific task at hand. However, in many cases this is simply impossible.
[Yin et al.](https://arxiv.org/abs/1909.00161) proposed a very clever way of using pre-trained MNLI models as zero-shot sequence classifiers. The methods works by reformulating the question to an MNLI hypothesis. If we want to figure out if a text is about "sport", we simply state that "This text is about sport" ("Denne teksten handler om sport").
When the model is finetuned on the 400k large MNLI task, it is in many cases able to solve this classification tasks. There are no MNLI-set of this size in Norwegian but we have trained it on a machine translated version of the original MNLI-set.
## Testing the model
For testing the model, we recommend the [NbAiLab Colab Notebook](https://colab.research.google.com/gist/peregilk/769b5150a2f807219ab8f15dd11ea449/nbailab-mnli-norwegian-demo.ipynb)
## Hugging Face zero-shot-classification pipeline
The easiest way to try this out is by using the Hugging Face pipeline. Please, note that you will get better results when using Norwegian hypothesis template instead of the default English one.
```python
from transformers import pipeline
classifier = pipeline("zero-shot-classification", model="NbAiLab/nb-bert-base-mnli")
```
You can then use this pipeline to classify sequences into any of the class names you specify.
```python
sequence_to_classify = 'Folkehelseinstituttets mest optimistiske anslag er at alle voksne er ferdigvaksinert innen midten av september.'
candidate_labels = ['politikk', 'helse', 'sport', 'religion']
hypothesis_template = 'Dette eksempelet er {}.'
classifier(sequence_to_classify, candidate_labels, hypothesis_template=hypothesis_template, multi_class=True)
# {'labels': ['helse', 'politikk', 'sport', 'religion'],
# 'scores': [0.4210019111633301, 0.0674605593085289, 0.000840459018945694, 0.0007541406666859984],
# 'sequence': 'Folkehelseinstituttets mest optimistiske anslag er at alle over 18 år er ferdigvaksinert innen midten av september.'}
```
## More information
For more information on the model, see
https://github.com/NBAiLab/notram
Here you will also find a Colab explaining more in details how to use the zero-shot-classification pipeline. | {"language": false, "license": "cc-by-4.0", "tags": ["nb-bert", "zero-shot-classification", "pytorch", "tensorflow", "norwegian", "bert"], "datasets": ["mnli", "multi_nli", "xnli"], "thumbnail": "https://raw.githubusercontent.com/NBAiLab/notram/master/images/nblogo_2.png", "pipeline_tag": "zero-shot-classification", "widget": [{"example_title": "Nyhetsartikkel om FHI", "text": "Folkehelseinstituttets mest optimistiske anslag er at alle voksne er ferdigvaksinert innen midten av september.", "candidate_labels": "helse, politikk, sport, religion"}]} | zero-shot-classification | NbAiLab/nb-bert-base-mnli | [
"transformers",
"pytorch",
"jax",
"safetensors",
"bert",
"text-classification",
"nb-bert",
"zero-shot-classification",
"tensorflow",
"norwegian",
"no",
"dataset:mnli",
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"dataset:xnli",
"arxiv:1909.00161",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"1909.00161"
] | [
"no"
] | TAGS
#transformers #pytorch #jax #safetensors #bert #text-classification #nb-bert #zero-shot-classification #tensorflow #norwegian #no #dataset-mnli #dataset-multi_nli #dataset-xnli #arxiv-1909.00161 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
Release 1.0 (March 11, 2021)
# NB-Bert base model finetuned on Norwegian machine translated MNLI
## Description
The most effective way of creating a good classifier is to finetune a pre-trained model for the specific task at hand. However, in many cases this is simply impossible.
Yin et al. proposed a very clever way of using pre-trained MNLI models as zero-shot sequence classifiers. The methods works by reformulating the question to an MNLI hypothesis. If we want to figure out if a text is about "sport", we simply state that "This text is about sport" ("Denne teksten handler om sport").
When the model is finetuned on the 400k large MNLI task, it is in many cases able to solve this classification tasks. There are no MNLI-set of this size in Norwegian but we have trained it on a machine translated version of the original MNLI-set.
## Testing the model
For testing the model, we recommend the NbAiLab Colab Notebook
## Hugging Face zero-shot-classification pipeline
The easiest way to try this out is by using the Hugging Face pipeline. Please, note that you will get better results when using Norwegian hypothesis template instead of the default English one.
You can then use this pipeline to classify sequences into any of the class names you specify.
## More information
For more information on the model, see
URL
Here you will also find a Colab explaining more in details how to use the zero-shot-classification pipeline. | [
"# NB-Bert base model finetuned on Norwegian machine translated MNLI",
"## Description\nThe most effective way of creating a good classifier is to finetune a pre-trained model for the specific task at hand. However, in many cases this is simply impossible. \nYin et al. proposed a very clever way of using pre-trained MNLI models as zero-shot sequence classifiers. The methods works by reformulating the question to an MNLI hypothesis. If we want to figure out if a text is about \"sport\", we simply state that \"This text is about sport\" (\"Denne teksten handler om sport\").\n\nWhen the model is finetuned on the 400k large MNLI task, it is in many cases able to solve this classification tasks. There are no MNLI-set of this size in Norwegian but we have trained it on a machine translated version of the original MNLI-set.",
"## Testing the model\nFor testing the model, we recommend the NbAiLab Colab Notebook",
"## Hugging Face zero-shot-classification pipeline\nThe easiest way to try this out is by using the Hugging Face pipeline. Please, note that you will get better results when using Norwegian hypothesis template instead of the default English one. \n\nYou can then use this pipeline to classify sequences into any of the class names you specify.",
"## More information\n\nFor more information on the model, see\n\nURL\n\nHere you will also find a Colab explaining more in details how to use the zero-shot-classification pipeline."
] | [
"TAGS\n#transformers #pytorch #jax #safetensors #bert #text-classification #nb-bert #zero-shot-classification #tensorflow #norwegian #no #dataset-mnli #dataset-multi_nli #dataset-xnli #arxiv-1909.00161 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# NB-Bert base model finetuned on Norwegian machine translated MNLI",
"## Description\nThe most effective way of creating a good classifier is to finetune a pre-trained model for the specific task at hand. However, in many cases this is simply impossible. \nYin et al. proposed a very clever way of using pre-trained MNLI models as zero-shot sequence classifiers. The methods works by reformulating the question to an MNLI hypothesis. If we want to figure out if a text is about \"sport\", we simply state that \"This text is about sport\" (\"Denne teksten handler om sport\").\n\nWhen the model is finetuned on the 400k large MNLI task, it is in many cases able to solve this classification tasks. There are no MNLI-set of this size in Norwegian but we have trained it on a machine translated version of the original MNLI-set.",
"## Testing the model\nFor testing the model, we recommend the NbAiLab Colab Notebook",
"## Hugging Face zero-shot-classification pipeline\nThe easiest way to try this out is by using the Hugging Face pipeline. Please, note that you will get better results when using Norwegian hypothesis template instead of the default English one. \n\nYou can then use this pipeline to classify sequences into any of the class names you specify.",
"## More information\n\nFor more information on the model, see\n\nURL\n\nHere you will also find a Colab explaining more in details how to use the zero-shot-classification pipeline."
] | [
108,
19,
188,
20,
76,
38
] | [
"passage: TAGS\n#transformers #pytorch #jax #safetensors #bert #text-classification #nb-bert #zero-shot-classification #tensorflow #norwegian #no #dataset-mnli #dataset-multi_nli #dataset-xnli #arxiv-1909.00161 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n# NB-Bert base model finetuned on Norwegian machine translated MNLI## Description\nThe most effective way of creating a good classifier is to finetune a pre-trained model for the specific task at hand. However, in many cases this is simply impossible. \nYin et al. proposed a very clever way of using pre-trained MNLI models as zero-shot sequence classifiers. The methods works by reformulating the question to an MNLI hypothesis. If we want to figure out if a text is about \"sport\", we simply state that \"This text is about sport\" (\"Denne teksten handler om sport\").\n\nWhen the model is finetuned on the 400k large MNLI task, it is in many cases able to solve this classification tasks. There are no MNLI-set of this size in Norwegian but we have trained it on a machine translated version of the original MNLI-set.## Testing the model\nFor testing the model, we recommend the NbAiLab Colab Notebook## Hugging Face zero-shot-classification pipeline\nThe easiest way to try this out is by using the Hugging Face pipeline. Please, note that you will get better results when using Norwegian hypothesis template instead of the default English one. \n\nYou can then use this pipeline to classify sequences into any of the class names you specify.## More information\n\nFor more information on the model, see\n\nURL\n\nHere you will also find a Colab explaining more in details how to use the zero-shot-classification pipeline."
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null | null | transformers |
**Release 1.0** (November 17, 2021)
# nb-bert-base-ner
## Description
NB-Bert base model fine-tuned on the Named Entity Recognition task using the [NorNE dataset](https://huggingface.co/datasets/NbAiLab/norne).
## Usage
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline
tokenizer = AutoTokenizer.from_pretrained("NbAiLab/nb-bert-base-ner")
model = AutoModelForTokenClassification.from_pretrained("NbAiLab/nb-bert-base-ner")
nlp = pipeline("ner", model=model, tokenizer=tokenizer)
example = "Jeg heter Kjell og bor i Oslo."
ner_results = nlp(example)
print(ner_results)
``` | {"language": false, "license": "cc-by-4.0", "tags": ["norwegian", "bert", "ner"], "datasets": ["norne"], "thumbnail": "nblogo_3.png", "pipeline_tag": "token-classification", "inference": {"parameters": {"aggregation_strategy": "first"}}, "widget": [{"text": "Trond Giske har bekreftet p\u00e5 sp\u00f8rsm\u00e5l fra Adresseavisen at Hansen leide et rom i hans leilighet i Trondheim."}]} | token-classification | NbAiLab/nb-bert-base-ner | [
"transformers",
"pytorch",
"safetensors",
"bert",
"token-classification",
"norwegian",
"ner",
"no",
"dataset:norne",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"no"
] | TAGS
#transformers #pytorch #safetensors #bert #token-classification #norwegian #ner #no #dataset-norne #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
Release 1.0 (November 17, 2021)
# nb-bert-base-ner
## Description
NB-Bert base model fine-tuned on the Named Entity Recognition task using the NorNE dataset.
## Usage
| [
"# nb-bert-base-ner",
"## Description\nNB-Bert base model fine-tuned on the Named Entity Recognition task using the NorNE dataset.",
"## Usage"
] | [
"TAGS\n#transformers #pytorch #safetensors #bert #token-classification #norwegian #ner #no #dataset-norne #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# nb-bert-base-ner",
"## Description\nNB-Bert base model fine-tuned on the Named Entity Recognition task using the NorNE dataset.",
"## Usage"
] | [
65,
9,
29,
3
] | [
"passage: TAGS\n#transformers #pytorch #safetensors #bert #token-classification #norwegian #ner #no #dataset-norne #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us \n# nb-bert-base-ner## Description\nNB-Bert base model fine-tuned on the Named Entity Recognition task using the NorNE dataset.## Usage"
] | [
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] |
null | null | transformers |
# NB-BERT-base Sámi Relevant
This a model capable of predicting when a chunk of text could potentially be of interest to the Sámi Bibliographers at the National Library of Norway. | {"language": ["se", "no", "en"], "license": "apache-2.0", "library_name": "transformers", "tags": ["sami relevant"], "metrics": ["matthews_correlation"], "pipeline_tag": "text-classification", "widget": [{"text": "Riddu Ri\u0111\u0111u Festiv\u00e1la lea jahk\u00e1sa\u0161 musihkka- ja -kulturfestiv\u00e1la mii l\u00e1giduvvo G\u00e1ivuonas Davvi-Romssas."}, {"text": "The S\u00e1mi languages form a branch of the Uralic language family. According to the traditional view, S\u00e1mi is within the Uralic family most closely related to the Finnic languages (Sammallahti 1998)."}, {"text": "Joseph Robinette Biden Jr. is an American politician who is the 46th and current president of the United States."}]} | text-classification | NbAiLab/nb-bert-base-sami-relevant | [
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"sami relevant",
"se",
"no",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"se",
"no",
"en"
] | TAGS
#transformers #pytorch #safetensors #bert #text-classification #sami relevant #se #no #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# NB-BERT-base Sámi Relevant
This a model capable of predicting when a chunk of text could potentially be of interest to the Sámi Bibliographers at the National Library of Norway. | [] | [
"TAGS\n#transformers #pytorch #safetensors #bert #text-classification #sami relevant #se #no #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
59
] | [
"passage: TAGS\n#transformers #pytorch #safetensors #bert #text-classification #sami relevant #se #no #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
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null | null | transformers | - **Release 1.1** (March 11, 2021)
- **Release 1.0** (January 13, 2021)
# NB-BERT-base
## Description
NB-BERT-base is a general BERT-base model built on the large digital collection at the National Library of Norway.
This model is based on the same structure as [BERT Cased multilingual model](https://github.com/google-research/bert/blob/master/multilingual.md), and is trained on a wide variety of Norwegian text (both bokmål and nynorsk) from the last 200 years.
## Intended use & limitations
The 1.1 version of the model is general, and should be fine-tuned for any particular use. Some fine-tuning sets may be found on GitHub, see
* https://github.com/NBAiLab/notram
## Training data
The model is trained on a wide variety of text. The training set is described on
* https://github.com/NBAiLab/notram
## More information
For more information on the model, see
https://github.com/NBAiLab/notram
| {"language": false, "license": "cc-by-4.0", "tags": ["norwegian", "bert"], "pipeline_tag": "fill-mask", "widget": [{"text": "P\u00e5 biblioteket kan du [MASK] en bok."}, {"text": "Dette er et [MASK] eksempel."}, {"text": "Av og til kan en spr\u00e5kmodell gi et [MASK] resultat."}, {"text": "Som ansat f\u00e5r du [MASK] for at bidrage til borgernes adgang til dansk kulturarv, til forskning og til samfundets demokratiske udvikling."}]} | fill-mask | NbAiLab/nb-bert-base | [
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"norwegian",
"fill-mask",
"no",
"license:cc-by-4.0",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"no"
] | TAGS
#transformers #pytorch #tf #jax #safetensors #bert #norwegian #fill-mask #no #license-cc-by-4.0 #endpoints_compatible #has_space #region-us
| - Release 1.1 (March 11, 2021)
- Release 1.0 (January 13, 2021)
# NB-BERT-base
## Description
NB-BERT-base is a general BERT-base model built on the large digital collection at the National Library of Norway.
This model is based on the same structure as BERT Cased multilingual model, and is trained on a wide variety of Norwegian text (both bokmål and nynorsk) from the last 200 years.
## Intended use & limitations
The 1.1 version of the model is general, and should be fine-tuned for any particular use. Some fine-tuning sets may be found on GitHub, see
* URL
## Training data
The model is trained on a wide variety of text. The training set is described on
* URL
## More information
For more information on the model, see
URL
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"## Description\n\nNB-BERT-base is a general BERT-base model built on the large digital collection at the National Library of Norway.\n\nThis model is based on the same structure as BERT Cased multilingual model, and is trained on a wide variety of Norwegian text (both bokmål and nynorsk) from the last 200 years.",
"## Intended use & limitations\n\nThe 1.1 version of the model is general, and should be fine-tuned for any particular use. Some fine-tuning sets may be found on GitHub, see\n\n* URL",
"## Training data\n\nThe model is trained on a wide variety of text. The training set is described on\n\n* URL",
"## More information\n\nFor more information on the model, see\n\nURL"
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"## Description\n\nNB-BERT-base is a general BERT-base model built on the large digital collection at the National Library of Norway.\n\nThis model is based on the same structure as BERT Cased multilingual model, and is trained on a wide variety of Norwegian text (both bokmål and nynorsk) from the last 200 years.",
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"passage: TAGS\n#transformers #pytorch #tf #jax #safetensors #bert #norwegian #fill-mask #no #license-cc-by-4.0 #endpoints_compatible #has_space #region-us \n# NB-BERT-base## Description\n\nNB-BERT-base is a general BERT-base model built on the large digital collection at the National Library of Norway.\n\nThis model is based on the same structure as BERT Cased multilingual model, and is trained on a wide variety of Norwegian text (both bokmål and nynorsk) from the last 200 years.## Intended use & limitations\n\nThe 1.1 version of the model is general, and should be fine-tuned for any particular use. Some fine-tuning sets may be found on GitHub, see\n\n* URL## Training data\n\nThe model is trained on a wide variety of text. The training set is described on\n\n* URL## More information\n\nFor more information on the model, see\n\nURL"
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null | null | transformers |
- **Release 1.0beta** (April 29, 2021)
# NB-BERT-large (beta)
## Description
NB-BERT-large is a general BERT-large model built on the large digital collection at the National Library of Norway.
This model is trained from scratch on a wide variety of Norwegian text (both bokmål and nynorsk) from the last 200 years using a monolingual Norwegian vocabulary.
## Intended use & limitations
The 1.0 version of the model is general, and should be fine-tuned for any particular use. Some fine-tuning sets may be found on Github, see
* https://github.com/NBAiLab/notram
## Training data
The model is trained on a wide variety of text. The training set is described on
* https://github.com/NBAiLab/notram
## More information
For more information on the model, see
https://github.com/NBAiLab/notram | {"language": false, "license": "cc-by-4.0", "tags": ["norwegian", "bert"], "thumbnail": "nblogo_3.png", "pipeline_tag": "fill-mask", "widget": [{"text": "P\u00e5 biblioteket kan du l\u00e5ne en [MASK]."}]} | fill-mask | NbAiLab/nb-bert-large | [
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] | TAGS
#transformers #pytorch #tf #jax #safetensors #bert #norwegian #fill-mask #no #license-cc-by-4.0 #endpoints_compatible #has_space #region-us
|
- Release 1.0beta (April 29, 2021)
# NB-BERT-large (beta)
## Description
NB-BERT-large is a general BERT-large model built on the large digital collection at the National Library of Norway.
This model is trained from scratch on a wide variety of Norwegian text (both bokmål and nynorsk) from the last 200 years using a monolingual Norwegian vocabulary.
## Intended use & limitations
The 1.0 version of the model is general, and should be fine-tuned for any particular use. Some fine-tuning sets may be found on Github, see
* URL
## Training data
The model is trained on a wide variety of text. The training set is described on
* URL
## More information
For more information on the model, see
URL | [
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null | null | transformers |
- **Release ✨v1✨** (January 18th, 2023) *[Full-precision](https://huggingface.co/NbAiLab/nb-gpt-j-6B/tree/v1), [sharded](https://huggingface.co/NbAiLab/nb-gpt-j-6B/tree/v1-sharded), [half-precision](https://huggingface.co/NbAiLab/nb-gpt-j-6B/tree/v1-float16), and [mesh-transformers-jax](https://huggingface.co/NbAiLab/nb-gpt-j-6B/tree/v1-mesh) weights*
<details><summary>All checkpoints</summary>
- **Release v1beta5** (December 18th, 2022) *[Full-precision](https://huggingface.co/NbAiLab/nb-gpt-j-6B/tree/v1beta5), [sharded](https://huggingface.co/NbAiLab/nb-gpt-j-6B/tree/v1beta5-sharded), and [half-precision](https://huggingface.co/NbAiLab/nb-gpt-j-6B/tree/v1beta5-float16) weights*
- **Release v1beta4** (October 28th, 2022) *[Full-precision](https://huggingface.co/NbAiLab/nb-gpt-j-6B/tree/v1beta4), [sharded](https://huggingface.co/NbAiLab/nb-gpt-j-6B/tree/v1beta4-sharded), and [half-precision](https://huggingface.co/NbAiLab/nb-gpt-j-6B/tree/v1beta4-float16) weights*
- **Release v1beta3** (August 8th, 2022) *[Full-precision](https://huggingface.co/NbAiLab/nb-gpt-j-6B/tree/v1beta3), [sharded](https://huggingface.co/NbAiLab/nb-gpt-j-6B/tree/v1beta3-sharded), and [half-precision](https://huggingface.co/NbAiLab/nb-gpt-j-6B/tree/v1beta3-float16) weights*
- **Release v1beta2** (June 18th, 2022) *[Full-precision](https://huggingface.co/NbAiLab/nb-gpt-j-6B/tree/v1beta2), [sharded](https://huggingface.co/NbAiLab/nb-gpt-j-6B/tree/sharded), and [half-precision](https://huggingface.co/NbAiLab/nb-gpt-j-6B/tree/v1beta2-float16) weights*
- **Release v1beta1** (April 28th, 2022) *[Half-precision](https://huggingface.co/NbAiLab/nb-gpt-j-6B/tree/v1beta1-float16) weights*
</details>
# NB-GPT-J-6B
## Demo: https://ai.nb.no/demo/nb-gpt-j-6B/ (Be patient, it runs on CPU 😅)
## Model Description
NB-GPT-J-6B is a Norwegian finetuned version of GPT-J 6B, a transformer model trained using Ben Wang's [Mesh Transformer JAX](https://github.com/kingoflolz/mesh-transformer-jax/). "GPT-J" refers to the class of model, while "6B" represents the number of trainable parameters (6 billion parameters).
<figure>
| Hyperparameter | Value |
|----------------------|------------|
| \\(n_{parameters}\\) | 6053381344 |
| \\(n_{layers}\\) | 28* |
| \\(d_{model}\\) | 4096 |
| \\(d_{ff}\\) | 16384 |
| \\(n_{heads}\\) | 16 |
| \\(d_{head}\\) | 256 |
| \\(n_{ctx}\\) | 2048 |
| \\(n_{vocab}\\) | 50257/50400† (same tokenizer as GPT-2/3) |
| Positional Encoding | [Rotary Position Embedding (RoPE)](https://arxiv.org/abs/2104.09864) |
| RoPE Dimensions | [64](https://github.com/kingoflolz/mesh-transformer-jax/blob/f2aa66e0925de6593dcbb70e72399b97b4130482/mesh_transformer/layers.py#L223) |
<figcaption><p><strong>*</strong> Each layer consists of one feedforward block and one self attention block.</p>
<p><strong>†</strong> Although the embedding matrix has a size of 50400, only 50257 entries are used by the GPT-2 tokenizer.</p></figcaption></figure>
The model consists of 28 layers with a model dimension of 4096, and a feedforward dimension of 16384. The model
dimension is split into 16 heads, each with a dimension of 256. Rotary Position Embedding (RoPE) is applied to 64
dimensions of each head. The model is trained with a tokenization vocabulary of 50257, using the same set of BPEs as
GPT-2/GPT-3.
## Training data
NB-GPT-J-6B was finetuned on [NCC](https://huggingface.co/datasets/NbAiLab/NCC), the Norwegian Colossal Corpus, plus other Internet sources like Wikipedia, mC4, and OSCAR.
## Training procedure
This model was finetuned for 130 billion tokens over 1,000,000 steps on a TPU v3-8 VM. It was trained as an autoregressive language model, using cross-entropy loss to maximize the likelihood of predicting the next token correctly.
## Intended Use and Limitations
NB-GPT-J-6B learns an inner representation of the Norwegian language that can be used to extract features useful for downstream tasks. The model is best at what it was pretrained for however, which is generating text from a prompt.
### How to use
This model can be easily loaded using the `AutoModelForCausalLM` functionality:
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("NbAiLab/nb-gpt-j-6B")
model = AutoModelForCausalLM.from_pretrained("NbAiLab/nb-gpt-j-6B")
```
### Limitations and Biases
As the original GPT-J model, the core functionality of NB-GPT-J-6B is taking a string of text and predicting the next token. While language models are widely used for tasks other than this, there are a lot of unknowns with this work. When prompting NB-GPT-J-6B it is important to remember that the statistically most likely next token is often not the token that produces the most "accurate" text. Never depend upon NB-GPT-J-6B to produce factually accurate output.
The original GPT-J was trained on the Pile, a dataset known to contain profanity, lewd, and otherwise abrasive language. Depending upon use case GPT-J may produce socially unacceptable text. See [Sections 5 and 6 of the Pile paper](https://arxiv.org/abs/2101.00027) for a more detailed analysis of the biases in the Pile. A fine-grained analysis of the bias contained in the corpus used for fine-tuning is still pending.
As with all language models, it is hard to predict in advance how NB-GPT-J-6B will respond to particular prompts and offensive content may occur without warning. We recommend having a human curate or filter the outputs before releasing them, both to censor undesirable content and to improve the quality of the results.
## Evaluation results
We still have to find proper datasets to evaluate the model, so help is welcome!
## Citation and Related Information
### BibTeX entry
To cite this model or the corpus used:
```bibtex
@inproceedings{kummervold2021operationalizing,
title={Operationalizing a National Digital Library: The Case for a Norwegian Transformer Model},
author={Kummervold, Per E and De la Rosa, Javier and Wetjen, Freddy and Brygfjeld, Svein Arne},
booktitle={Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa)},
pages={20--29},
year={2021},
url={https://aclanthology.org/2021.nodalida-main.3/}
}
```
If you use this model, we would love to hear about it! Reach out on twitter, GitHub, Discord, or shoot us an email.
## Disclaimer
The models published in this repository are intended for a generalist purpose and are available to third parties. These models may have bias and/or any other undesirable distortions. When third parties, deploy or provide systems and/or services to other parties using any of these models (or using systems based on these models) or become users of the models, they should note that it is their responsibility to mitigate the risks arising from their use and, in any event, to comply with applicable regulations, including regulations regarding the use of artificial intelligence. In no event shall the owner of the models (The National Library of Norway) be liable for any results arising from the use made by third parties of these models.
## Acknowledgements
This project would not have been possible without compute generously provided by Google through the
[TPU Research Cloud](https://sites.research.google/trc/), as well as the Cloud TPU team for providing early access to the [Cloud TPU VM](https://cloud.google.com/blog/products/compute/introducing-cloud-tpu-vms) Alpha. Specially, to [Stella Biderman](https://www.stellabiderman.com) for her general openness, and [Ben Wang](https://github.com/kingoflolz/mesh-transformer-jax) for the main codebase. | {"language": ["no", "nb", "nn"], "license": "apache-2.0", "tags": ["pytorch", "causal-lm"], "datasets": ["NbAiLab/NCC", "mc4", "oscar"], "pipeline_tag": "text-generation", "extra_gated_prompt": "You agree to not use the model to conduct experiments that cause harm to human subjects.", "extra_gated_fields": {"Company": "text", "Country": "text", "Intended Use": "text"}} | text-generation | NbAiLab/nb-gpt-j-6B | [
"transformers",
"pytorch",
"safetensors",
"gptj",
"text-generation",
"causal-lm",
"no",
"nb",
"nn",
"dataset:NbAiLab/NCC",
"dataset:mc4",
"dataset:oscar",
"arxiv:2104.09864",
"arxiv:2101.00027",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2104.09864",
"2101.00027"
] | [
"no",
"nb",
"nn"
] | TAGS
#transformers #pytorch #safetensors #gptj #text-generation #causal-lm #no #nb #nn #dataset-NbAiLab/NCC #dataset-mc4 #dataset-oscar #arxiv-2104.09864 #arxiv-2101.00027 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| * Release v1 (January 18th, 2023) *Full-precision, sharded, half-precision, and mesh-transformers-jax weights*
All checkpoints
```
- Release v1beta5 (December 18th, 2022) *Full-precision, sharded, and half-precision weights*
- Release v1beta4 (October 28th, 2022) *Full-precision, sharded, and half-precision weights*
- Release v1beta3 (August 8th, 2022) *Full-precision, sharded, and half-precision weights*
- Release v1beta2 (June 18th, 2022) *Full-precision, sharded, and half-precision weights*
- Release v1beta1 (April 28th, 2022) *Half-precision weights*
```
NB-GPT-J-6B
===========
Demo: URL (Be patient, it runs on CPU )
---------------------------------------
Model Description
-----------------
NB-GPT-J-6B is a Norwegian finetuned version of GPT-J 6B, a transformer model trained using Ben Wang's Mesh Transformer JAX. "GPT-J" refers to the class of model, while "6B" represents the number of trainable parameters (6 billion parameters).
**\*** Each layer consists of one feedforward block and one self attention block.
**†** Although the embedding matrix has a size of 50400, only 50257 entries are used by the GPT-2 tokenizer.
The model consists of 28 layers with a model dimension of 4096, and a feedforward dimension of 16384. The model
dimension is split into 16 heads, each with a dimension of 256. Rotary Position Embedding (RoPE) is applied to 64
dimensions of each head. The model is trained with a tokenization vocabulary of 50257, using the same set of BPEs as
GPT-2/GPT-3.
Training data
-------------
NB-GPT-J-6B was finetuned on NCC, the Norwegian Colossal Corpus, plus other Internet sources like Wikipedia, mC4, and OSCAR.
Training procedure
------------------
This model was finetuned for 130 billion tokens over 1,000,000 steps on a TPU v3-8 VM. It was trained as an autoregressive language model, using cross-entropy loss to maximize the likelihood of predicting the next token correctly.
Intended Use and Limitations
----------------------------
NB-GPT-J-6B learns an inner representation of the Norwegian language that can be used to extract features useful for downstream tasks. The model is best at what it was pretrained for however, which is generating text from a prompt.
### How to use
This model can be easily loaded using the 'AutoModelForCausalLM' functionality:
### Limitations and Biases
As the original GPT-J model, the core functionality of NB-GPT-J-6B is taking a string of text and predicting the next token. While language models are widely used for tasks other than this, there are a lot of unknowns with this work. When prompting NB-GPT-J-6B it is important to remember that the statistically most likely next token is often not the token that produces the most "accurate" text. Never depend upon NB-GPT-J-6B to produce factually accurate output.
The original GPT-J was trained on the Pile, a dataset known to contain profanity, lewd, and otherwise abrasive language. Depending upon use case GPT-J may produce socially unacceptable text. See Sections 5 and 6 of the Pile paper for a more detailed analysis of the biases in the Pile. A fine-grained analysis of the bias contained in the corpus used for fine-tuning is still pending.
As with all language models, it is hard to predict in advance how NB-GPT-J-6B will respond to particular prompts and offensive content may occur without warning. We recommend having a human curate or filter the outputs before releasing them, both to censor undesirable content and to improve the quality of the results.
Evaluation results
------------------
We still have to find proper datasets to evaluate the model, so help is welcome!
and Related Information
### BibTeX entry
To cite this model or the corpus used:
If you use this model, we would love to hear about it! Reach out on twitter, GitHub, Discord, or shoot us an email.
Disclaimer
----------
The models published in this repository are intended for a generalist purpose and are available to third parties. These models may have bias and/or any other undesirable distortions. When third parties, deploy or provide systems and/or services to other parties using any of these models (or using systems based on these models) or become users of the models, they should note that it is their responsibility to mitigate the risks arising from their use and, in any event, to comply with applicable regulations, including regulations regarding the use of artificial intelligence. In no event shall the owner of the models (The National Library of Norway) be liable for any results arising from the use made by third parties of these models.
Acknowledgements
----------------
This project would not have been possible without compute generously provided by Google through the
TPU Research Cloud, as well as the Cloud TPU team for providing early access to the Cloud TPU VM Alpha. Specially, to Stella Biderman for her general openness, and Ben Wang for the main codebase.
| [
"### How to use\n\n\nThis model can be easily loaded using the 'AutoModelForCausalLM' functionality:",
"### Limitations and Biases\n\n\nAs the original GPT-J model, the core functionality of NB-GPT-J-6B is taking a string of text and predicting the next token. While language models are widely used for tasks other than this, there are a lot of unknowns with this work. When prompting NB-GPT-J-6B it is important to remember that the statistically most likely next token is often not the token that produces the most \"accurate\" text. Never depend upon NB-GPT-J-6B to produce factually accurate output.\n\n\nThe original GPT-J was trained on the Pile, a dataset known to contain profanity, lewd, and otherwise abrasive language. Depending upon use case GPT-J may produce socially unacceptable text. See Sections 5 and 6 of the Pile paper for a more detailed analysis of the biases in the Pile. A fine-grained analysis of the bias contained in the corpus used for fine-tuning is still pending.\n\n\nAs with all language models, it is hard to predict in advance how NB-GPT-J-6B will respond to particular prompts and offensive content may occur without warning. We recommend having a human curate or filter the outputs before releasing them, both to censor undesirable content and to improve the quality of the results.\n\n\nEvaluation results\n------------------\n\n\nWe still have to find proper datasets to evaluate the model, so help is welcome!\n\n\nand Related Information",
"### BibTeX entry\n\n\nTo cite this model or the corpus used:\n\n\nIf you use this model, we would love to hear about it! Reach out on twitter, GitHub, Discord, or shoot us an email.\n\n\nDisclaimer\n----------\n\n\nThe models published in this repository are intended for a generalist purpose and are available to third parties. These models may have bias and/or any other undesirable distortions. When third parties, deploy or provide systems and/or services to other parties using any of these models (or using systems based on these models) or become users of the models, they should note that it is their responsibility to mitigate the risks arising from their use and, in any event, to comply with applicable regulations, including regulations regarding the use of artificial intelligence. In no event shall the owner of the models (The National Library of Norway) be liable for any results arising from the use made by third parties of these models.\n\n\nAcknowledgements\n----------------\n\n\nThis project would not have been possible without compute generously provided by Google through the\nTPU Research Cloud, as well as the Cloud TPU team for providing early access to the Cloud TPU VM Alpha. Specially, to Stella Biderman for her general openness, and Ben Wang for the main codebase."
] | [
"TAGS\n#transformers #pytorch #safetensors #gptj #text-generation #causal-lm #no #nb #nn #dataset-NbAiLab/NCC #dataset-mc4 #dataset-oscar #arxiv-2104.09864 #arxiv-2101.00027 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### How to use\n\n\nThis model can be easily loaded using the 'AutoModelForCausalLM' functionality:",
"### Limitations and Biases\n\n\nAs the original GPT-J model, the core functionality of NB-GPT-J-6B is taking a string of text and predicting the next token. While language models are widely used for tasks other than this, there are a lot of unknowns with this work. When prompting NB-GPT-J-6B it is important to remember that the statistically most likely next token is often not the token that produces the most \"accurate\" text. Never depend upon NB-GPT-J-6B to produce factually accurate output.\n\n\nThe original GPT-J was trained on the Pile, a dataset known to contain profanity, lewd, and otherwise abrasive language. Depending upon use case GPT-J may produce socially unacceptable text. See Sections 5 and 6 of the Pile paper for a more detailed analysis of the biases in the Pile. A fine-grained analysis of the bias contained in the corpus used for fine-tuning is still pending.\n\n\nAs with all language models, it is hard to predict in advance how NB-GPT-J-6B will respond to particular prompts and offensive content may occur without warning. We recommend having a human curate or filter the outputs before releasing them, both to censor undesirable content and to improve the quality of the results.\n\n\nEvaluation results\n------------------\n\n\nWe still have to find proper datasets to evaluate the model, so help is welcome!\n\n\nand Related Information",
"### BibTeX entry\n\n\nTo cite this model or the corpus used:\n\n\nIf you use this model, we would love to hear about it! Reach out on twitter, GitHub, Discord, or shoot us an email.\n\n\nDisclaimer\n----------\n\n\nThe models published in this repository are intended for a generalist purpose and are available to third parties. These models may have bias and/or any other undesirable distortions. When third parties, deploy or provide systems and/or services to other parties using any of these models (or using systems based on these models) or become users of the models, they should note that it is their responsibility to mitigate the risks arising from their use and, in any event, to comply with applicable regulations, including regulations regarding the use of artificial intelligence. In no event shall the owner of the models (The National Library of Norway) be liable for any results arising from the use made by third parties of these models.\n\n\nAcknowledgements\n----------------\n\n\nThis project would not have been possible without compute generously provided by Google through the\nTPU Research Cloud, as well as the Cloud TPU team for providing early access to the Cloud TPU VM Alpha. Specially, to Stella Biderman for her general openness, and Ben Wang for the main codebase."
] | [
104,
26,
337,
278
] | [
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] |
null | null | transformers |
# This is just a Test Model. Do NOT use for anything!
Continued pretrained from the nb-roberta-base.
The domain specific pretraining is done on the 102GB (Scandinavian corpus)[https://huggingface.co/datasets/NbAiLab/scandinavian].
## Train for 180k steps for 128 sequences:
```bash
./run_mlm_flax_stream.py \
--output_dir="./" \
--model_type="roberta" \
--config_name="./" \
--tokenizer_name="./" \
--model_name_or_path="./" \
--dataset_name="NbAiLab/scandinavian" \
--max_seq_length="128" \
--weight_decay="0.01" \
--per_device_train_batch_size="128" \
--per_device_eval_batch_size="128" \
--learning_rate="6e-5" \
--warmup_steps="5000" \
--overwrite_output_dir \
--cache_dir /mnt/disks/flaxdisk/cache/ \
--num_train_steps="180000" \
--adam_beta1="0.9" \
--adam_beta2="0.98" \
--logging_steps="10000" \
--save_steps="10000" \
--eval_steps="10000" \
--preprocessing_num_workers 96 \
--auth_token True \
--adafactor \
--push_to_hub
```
## Train for 20k steps for 512 sequences:
```bash
./run_mlm_flax_stream.py \
--output_dir="./" \
--model_type="roberta" \
--config_name="./" \
--tokenizer_name="./" \
--model_name_or_path="./" \
--dataset_name="NbAiLab/scandinavian" \
--max_seq_length="512" \
--weight_decay="0.01" \
--per_device_train_batch_size="48" \
--per_device_eval_batch_size="48" \
--learning_rate="3e-5" \
--warmup_steps="5000" \
--overwrite_output_dir \
--cache_dir /mnt/disks/flaxdisk/cache/ \
--num_train_steps="20000" \
--adam_beta1="0.9" \
--adam_beta2="0.98" \
--logging_steps="20000" \
--save_steps="10000" \
--eval_steps="10000" \
--preprocessing_num_workers 96 \
--auth_token True \
--adafactor \
--push_to_hub
```
Approximate additional training time: 1 week.
| {"language": false, "license": "cc-by-4.0", "tags": ["norwegian", "roberta"], "pipeline_tag": "fill-mask", "widget": [{"text": "P\u00e5 biblioteket kan du <mask> en bok."}, {"text": "Dette er et <mask> eksempel."}, {"text": "Av og til kan en spr\u00e5kmodell gi et <mask> resultat."}, {"text": "Som ansat f\u00e5r du <mask> for at bidrage til borgernes adgang til dansk kulturarv, til forskning og til samfundets demokratiske udvikling."}]} | fill-mask | NbAiLab/nb-roberta-base-scandinavian | [
"transformers",
"pytorch",
"jax",
"tensorboard",
"safetensors",
"roberta",
"fill-mask",
"norwegian",
"no",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"no"
] | TAGS
#transformers #pytorch #jax #tensorboard #safetensors #roberta #fill-mask #norwegian #no #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# This is just a Test Model. Do NOT use for anything!
Continued pretrained from the nb-roberta-base.
The domain specific pretraining is done on the 102GB (Scandinavian corpus)[URL
## Train for 180k steps for 128 sequences:
## Train for 20k steps for 512 sequences:
Approximate additional training time: 1 week.
| [
"# This is just a Test Model. Do NOT use for anything! \n\nContinued pretrained from the nb-roberta-base.\n\nThe domain specific pretraining is done on the 102GB (Scandinavian corpus)[URL",
"## Train for 180k steps for 128 sequences:",
"## Train for 20k steps for 512 sequences:\n\n\n\n\nApproximate additional training time: 1 week."
] | [
"TAGS\n#transformers #pytorch #jax #tensorboard #safetensors #roberta #fill-mask #norwegian #no #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# This is just a Test Model. Do NOT use for anything! \n\nContinued pretrained from the nb-roberta-base.\n\nThe domain specific pretraining is done on the 102GB (Scandinavian corpus)[URL",
"## Train for 180k steps for 128 sequences:",
"## Train for 20k steps for 512 sequences:\n\n\n\n\nApproximate additional training time: 1 week."
] | [
64,
49,
12,
23
] | [
"passage: TAGS\n#transformers #pytorch #jax #tensorboard #safetensors #roberta #fill-mask #norwegian #no #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us \n# This is just a Test Model. Do NOT use for anything! \n\nContinued pretrained from the nb-roberta-base.\n\nThe domain specific pretraining is done on the 102GB (Scandinavian corpus)[URL## Train for 180k steps for 128 sequences:## Train for 20k steps for 512 sequences:\n\n\n\n\nApproximate additional training time: 1 week."
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] |
null | null | transformers | # 🇳🇴 Norwegian T5 Base model Trained on the NCC🇳🇴
This is a Norwegian T5-base model trained on the Norwegian Colossal Corpus (NCC) on a TPU v3-8.
This model is currently training. It will finish in January 2022. Please do not use yet..
```
| {"language": false, "license": "cc-by-4.0", "tags": ["seq2seq"], "datasets": ["Norwegian Nynorsk/Bokm\u00e5l"]} | text2text-generation | NbAiLab/nb-t5-base-v3 | [
"transformers",
"jax",
"tensorboard",
"t5",
"text2text-generation",
"seq2seq",
"no",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"no"
] | TAGS
#transformers #jax #tensorboard #t5 #text2text-generation #seq2seq #no #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # 🇳🇴 Norwegian T5 Base model Trained on the NCC🇳🇴
This is a Norwegian T5-base model trained on the Norwegian Colossal Corpus (NCC) on a TPU v3-8.
This model is currently training. It will finish in January 2022. Please do not use yet..
'''
| [
"# 🇳🇴 Norwegian T5 Base model Trained on the NCC🇳🇴 \n\nThis is a Norwegian T5-base model trained on the Norwegian Colossal Corpus (NCC) on a TPU v3-8. \n\nThis model is currently training. It will finish in January 2022. Please do not use yet..\n '''"
] | [
"TAGS\n#transformers #jax #tensorboard #t5 #text2text-generation #seq2seq #no #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# 🇳🇴 Norwegian T5 Base model Trained on the NCC🇳🇴 \n\nThis is a Norwegian T5-base model trained on the Norwegian Colossal Corpus (NCC) on a TPU v3-8. \n\nThis model is currently training. It will finish in January 2022. Please do not use yet..\n '''"
] | [
68,
67
] | [
"passage: TAGS\n#transformers #jax #tensorboard #t5 #text2text-generation #seq2seq #no #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# 🇳🇴 Norwegian T5 Base model Trained on the NCC🇳🇴 \n\nThis is a Norwegian T5-base model trained on the Norwegian Colossal Corpus (NCC) on a TPU v3-8. \n\nThis model is currently training. It will finish in January 2022. Please do not use yet..\n '''"
] | [
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null | null | transformers |
# Norwegian Wav2Vec2 Model - 1B Bokmål
This model is finetuned on top of feature extractor [XLS-R](https://huggingface.co/facebook/wav2vec2-xls-r-1b) from Facebook/Meta. The finetuned model achieves the following results on the test set with a 5-gram KenLM. The numbers in parentheses are the results without the language model:
- **WER: 0.0633** (0.0738)
- **CER: 0.0248** (0.0263)
## Model description
This is one of several Wav2Vec-models our team created during the 🤗 hosted [Robust Speech Event](https://discuss.huggingface.co/t/open-to-the-community-robust-speech-recognition-challenge/13614?s=09). This is the complete list of our models and their final scores:
| Model | Final WER | |
|:--------------|:------------|:------------:|
| NbAiLab/nb-wav2vec2-1b-bokmaal (this model) | 6.33 | |
| [NbAiLab/nb-wav2vec2-300m-bokmaal](https://huggingface.co/NbAiLab/nb-wav2vec2-300m-bokmaal) | 7.03 | |
| [NbAiLab/nb-wav2vec2-1b-nynorsk](https://huggingface.co/NbAiLab/nb-wav2vec2-1b-nynorsk) | 11.32 | |
| [NbAiLab/nb-wav2vec2-300m-nynorsk](https://huggingface.co/NbAiLab/nb-wav2vec2-300m-nynorsk) | 12.22 | |
## Dataset
In parallel with the event, the team also converted the [Norwegian Parliamentary Speech Corpus (NPSC)](https://www.nb.no/sprakbanken/en/resource-catalogue/oai-nb-no-sbr-58/) to the [NbAiLab/NPSC](https://huggingface.co/datasets/NbAiLab/NPSC) in 🤗 Dataset format and used that as the main source for training.
## Code
We have released all the code developed during the event so that the Norwegian NLP community can build upon it when developing even better Norwegian ASR models. The finetuning of these models is not very computationally demanding. After following the instructions here, you should be able to train your own automatic speech recognition system in less than a day with an average GPU.
## Team
The following people contributed to building this model: Rolv-Arild Braaten, Per Egil Kummervold, Andre Kåsen, Javier de la Rosa, Per Erik Solberg, and Freddy Wetjen.
## Training procedure
To reproduce these results, we strongly recommend that you follow the [instructions from 🤗](https://github.com/huggingface/transformers/tree/master/examples/research_projects/robust-speech-event#talks) to train a simple Swedish model.
When you have verified that you are able to do this, create a fresh new repo. You can then start by copying the files ```run.sh``` and ```run_speech_recognition_ctc.py``` from our repo. Running these will create all the other necessary files, and should let you reproduce our results. With some tweaks to the hyperparameters, you might even be able to build an even better ASR. Good luck!
### Language Model
As the scores indicate, adding even a simple 5-gram language will improve the results. 🤗 has provided another [very nice blog](https://huggingface.co/blog/wav2vec2-with-ngram) explaining how to add a 5-gram language model to improve the ASR model. You can build this from your own corpus, for instance by extracting some suitable text from the [Norwegian Colossal Corpus](https://huggingface.co/datasets/NbAiLab/NCC). You can also skip some of the steps in the guide, and copy the [5-gram model from this repo](https://huggingface.co/NbAiLab/XLSR-300M-bokmaal/tree/main/language_model).
### Parameters
The final model was run using these parameters:
```
--dataset_name="NbAiLab/NPSC"
--model_name_or_path="facebook/wav2vec2-xls-r-1b"
--dataset_config_name="16K_mp3_bokmaal"
--output_dir="./"
--overwrite_output_dir
--num_train_epochs="40"
--per_device_train_batch_size="12"
--per_device_eval_batch_size="12"
--gradient_accumulation_steps="2"
--learning_rate="2e-5"
--warmup_steps="2000"
--length_column_name="input_length"
--evaluation_strategy="steps"
--text_column_name="text"
--save_steps="500"
--eval_steps="500"
--logging_steps="100"
--layerdrop="0.041"
--attention_dropout="0.094"
--activation_dropout="0.055"
--hidden_dropout="0.047"
--save_total_limit="3"
--freeze_feature_encoder
--feat_proj_dropout="0.04"
--mask_time_prob="0.082"
--mask_time_length="10"
--mask_feature_prob="0.25"
--mask_feature_length="64"
--gradient_checkpointing
--min_duration_in_seconds="0.5"
--max_duration_in_seconds="30.0"
--ctc_zero_infinity=True
--use_auth_token
--seed="42"
--fp16
--group_by_length
--do_train --do_eval
--push_to_hub
--preprocessing_num_workers="16"
```
Using these settings, the training might take 3-4 days on an average GPU. You can, however, get a decent model and faster results by tweaking these parameters.
| Parameter| Comment |
|:-------------|:-----|
| per_device_train_batch_size | Adjust this to the maximum of available memory. 16 or 24 might be good settings depending on your system |
|gradient_accumulation_steps |Can be adjusted even further up to increase batch size and speed up training without running into memory issues |
| learning_rate|Can be increased, maybe as high as 1e-4. Speeds up training but might add instability |
| epochs| Can be decreased significantly. This is a huge dataset and you might get a decent result already after a couple of epochs|
## Citation
```bibtex
@inproceedings{de-la-rosa-etal-2023-boosting,
title = "Boosting {N}orwegian Automatic Speech Recognition",
author = "De La Rosa, Javier and
Braaten, Rolv-Arild and
Kummervold, Per and
Wetjen, Freddy",
booktitle = "Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa)",
month = may,
year = "2023",
address = "T{\'o}rshavn, Faroe Islands",
publisher = "University of Tartu Library",
url = "https://aclanthology.org/2023.nodalida-1.55",
pages = "555--564",
abstract = "In this paper, we present several baselines for automatic speech recognition (ASR) models for the two official written languages in Norway: Bokm{\aa}l and Nynorsk. We compare the performance of models of varying sizes and pre-training approaches on multiple Norwegian speech datasets. Additionally, we measure the performance of these models against previous state-of-the-art ASR models, as well as on out-of-domain datasets. We improve the state of the art on the Norwegian Parliamentary Speech Corpus (NPSC) from a word error rate (WER) of 17.10{\%} to 7.60{\%}, with models achieving 5.81{\%} for Bokm{\aa}l and 11.54{\%} for Nynorsk. We also discuss the challenges and potential solutions for further improving ASR models for Norwegian.",
}
```
See https://arxiv.org/abs/2307.01672
| {"language": ["nb", false], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "NbAiLab/NPSC", false, "nb", "nb-NO"], "datasets": ["NbAiLab/NPSC"], "model-index": [{"name": "nb-wav2vec2-1b-bokmaal", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "NPSC", "type": "NbAiLab/NPSC", "args": "16K_mp3_bokmaal"}, "metrics": [{"type": "wer", "value": 0.0633, "name": "Test (Bokm\u00e5l) WER"}, {"type": "cer", "value": 0.0248, "name": "Test (Bokm\u00e5l) CER"}]}]}]} | automatic-speech-recognition | NbAiLab/nb-wav2vec2-1b-bokmaal | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"NbAiLab/NPSC",
"no",
"nb",
"nb-NO",
"dataset:NbAiLab/NPSC",
"arxiv:2307.01672",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2307.01672"
] | [
"nb",
"no"
] | TAGS
#transformers #pytorch #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #NbAiLab/NPSC #no #nb #nb-NO #dataset-NbAiLab/NPSC #arxiv-2307.01672 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us
| Norwegian Wav2Vec2 Model - 1B Bokmål
====================================
This model is finetuned on top of feature extractor XLS-R from Facebook/Meta. The finetuned model achieves the following results on the test set with a 5-gram KenLM. The numbers in parentheses are the results without the language model:
* WER: 0.0633 (0.0738)
* CER: 0.0248 (0.0263)
Model description
-----------------
This is one of several Wav2Vec-models our team created during the hosted Robust Speech Event. This is the complete list of our models and their final scores:
Dataset
-------
In parallel with the event, the team also converted the Norwegian Parliamentary Speech Corpus (NPSC) to the NbAiLab/NPSC in Dataset format and used that as the main source for training.
Code
----
We have released all the code developed during the event so that the Norwegian NLP community can build upon it when developing even better Norwegian ASR models. The finetuning of these models is not very computationally demanding. After following the instructions here, you should be able to train your own automatic speech recognition system in less than a day with an average GPU.
Team
----
The following people contributed to building this model: Rolv-Arild Braaten, Per Egil Kummervold, Andre Kåsen, Javier de la Rosa, Per Erik Solberg, and Freddy Wetjen.
Training procedure
------------------
To reproduce these results, we strongly recommend that you follow the instructions from to train a simple Swedish model.
When you have verified that you are able to do this, create a fresh new repo. You can then start by copying the files and from our repo. Running these will create all the other necessary files, and should let you reproduce our results. With some tweaks to the hyperparameters, you might even be able to build an even better ASR. Good luck!
### Language Model
As the scores indicate, adding even a simple 5-gram language will improve the results. has provided another very nice blog explaining how to add a 5-gram language model to improve the ASR model. You can build this from your own corpus, for instance by extracting some suitable text from the Norwegian Colossal Corpus. You can also skip some of the steps in the guide, and copy the 5-gram model from this repo.
### Parameters
The final model was run using these parameters:
Using these settings, the training might take 3-4 days on an average GPU. You can, however, get a decent model and faster results by tweaking these parameters.
See URL
| [
"### Language Model\n\n\nAs the scores indicate, adding even a simple 5-gram language will improve the results. has provided another very nice blog explaining how to add a 5-gram language model to improve the ASR model. You can build this from your own corpus, for instance by extracting some suitable text from the Norwegian Colossal Corpus. You can also skip some of the steps in the guide, and copy the 5-gram model from this repo.",
"### Parameters\n\n\nThe final model was run using these parameters:\n\n\nUsing these settings, the training might take 3-4 days on an average GPU. You can, however, get a decent model and faster results by tweaking these parameters.\n\n\n\nSee URL"
] | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #NbAiLab/NPSC #no #nb #nb-NO #dataset-NbAiLab/NPSC #arxiv-2307.01672 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us \n",
"### Language Model\n\n\nAs the scores indicate, adding even a simple 5-gram language will improve the results. has provided another very nice blog explaining how to add a 5-gram language model to improve the ASR model. You can build this from your own corpus, for instance by extracting some suitable text from the Norwegian Colossal Corpus. You can also skip some of the steps in the guide, and copy the 5-gram model from this repo.",
"### Parameters\n\n\nThe final model was run using these parameters:\n\n\nUsing these settings, the training might take 3-4 days on an average GPU. You can, however, get a decent model and faster results by tweaking these parameters.\n\n\n\nSee URL"
] | [
99,
92,
57
] | [
"passage: TAGS\n#transformers #pytorch #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #NbAiLab/NPSC #no #nb #nb-NO #dataset-NbAiLab/NPSC #arxiv-2307.01672 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us \n### Language Model\n\n\nAs the scores indicate, adding even a simple 5-gram language will improve the results. has provided another very nice blog explaining how to add a 5-gram language model to improve the ASR model. You can build this from your own corpus, for instance by extracting some suitable text from the Norwegian Colossal Corpus. You can also skip some of the steps in the guide, and copy the 5-gram model from this repo.### Parameters\n\n\nThe final model was run using these parameters:\n\n\nUsing these settings, the training might take 3-4 days on an average GPU. You can, however, get a decent model and faster results by tweaking these parameters.\n\n\n\nSee URL"
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null | null | transformers |
# Norwegian Wav2Vec2 Model - 300M - VoxRex - Bokmål
This model is finetuned on top of feature extractor [VoxRex-model](https://huggingface.co/KBLab/wav2vec2-large-voxrex) from the National Library of Sweden. The finetuned model achieves the following results on the test set with a 5-gram KenLM. The numbers in parentheses are the results without the language model:
- **WER: 0.0703** (0.0979)
- **CER: 0.0269** (0.0311)
## Model description
This is one of several Wav2Vec-models our team created during the 🤗 hosted [Robust Speech Event](https://discuss.huggingface.co/t/open-to-the-community-robust-speech-recognition-challenge/13614?s=09). This is the complete list of our models and their final scores:
| Model | Final WER | |
|:--------------|:------------|:------------:|
| [NbAiLab/nb-wav2vec2-1b-bokmaal](https://huggingface.co/NbAiLab/nb-wav2vec2-1b-bokmaal) | 6.33 | |
| NbAiLab/nb-wav2vec2-300m-bokmaal (this model) | 7.03 | |
| [NbAiLab/nb-wav2vec2-1b-nynorsk](https://huggingface.co/NbAiLab/nb-wav2vec2-1b-nynorsk) | 11.32 | |
| [NbAiLab/nb-wav2vec2-300m-nynorsk](https://huggingface.co/NbAiLab/nb-wav2vec2-300m-nynorsk) | 12.22 | |
## Dataset
In parallel with the event, the team also converted the [Norwegian Parliamentary Speech Corpus (NPSC)](https://www.nb.no/sprakbanken/en/resource-catalogue/oai-nb-no-sbr-58/) to the [NbAiLab/NPSC](https://huggingface.co/datasets/NbAiLab/NPSC) in 🤗 Dataset format and used that as the main source for training.
## Code
We have released all the code developed during the event so that the Norwegian NLP community can build upon it when developing even better Norwegian ASR models. The finetuning of these models is not very computationally demanding. After following the instructions here, you should be able to train your own automatic speech recognition system in less than a day with an average GPU.
## Team
The following people contributed to building this model: Rolv-Arild Braaten, Per Egil Kummervold, Andre Kåsen, Javier de la Rosa, Per Erik Solberg, and Freddy Wetjen.
## Training procedure
To reproduce these results, we strongly recommend that you follow the [instructions from 🤗](https://github.com/huggingface/transformers/tree/master/examples/research_projects/robust-speech-event#talks) to train a simple Swedish model.
When you have verified that you are able to do this, create a fresh new repo. You can then start by copying the files ```run.sh``` and ```run_speech_recognition_ctc.py``` from our repo. Running these will create all the other necessary files, and should let you reproduce our results. With some tweaks to the hyperparameters, you might even be able to build an even better ASR. Good luck!
### Language Model
As the scores indicate, adding even a simple 5-gram language will improve the results. 🤗 has provided another [very nice blog](https://huggingface.co/blog/wav2vec2-with-ngram) explaining how to add a 5-gram language model to improve the ASR model. You can build this from your own corpus, for instance by extracting some suitable text from the [Norwegian Colossal Corpus](https://huggingface.co/datasets/NbAiLab/NCC). You can also skip some of the steps in the guide, and copy the [5-gram model from this repo](https://huggingface.co/NbAiLab/XLSR-300M-bokmaal/tree/main/language_model).
### Parameters
The final model was run using these parameters:
```
--dataset_name="NbAiLab/NPSC"
--model_name_or_path="KBLab/wav2vec2-large-voxrex"
--dataset_config_name="16K_mp3_bokmaal"
--output_dir="./"
--overwrite_output_dir
--num_train_epochs="15"
--per_device_train_batch_size="16"
--per_device_eval_batch_size="16"
--gradient_accumulation_steps="2"
--learning_rate="1e-4"
--warmup_steps="2000"
--length_column_name="input_length"
--evaluation_strategy="steps"
--text_column_name="text"
--save_steps="500"
--eval_steps="500"
--logging_steps="100"
--layerdrop="0.041"
--attention_dropout="0.094"
--activation_dropout="0.055"
--hidden_dropout="0.047"
--save_total_limit="3"
--freeze_feature_encoder
--feat_proj_dropout="0.04"
--mask_time_prob="0.082"
--mask_time_length="10"
--mask_feature_prob="0.25"
--mask_feature_length="64"
--gradient_checkpointing
--min_duration_in_seconds="0.5"
--max_duration_in_seconds="30.0"
--use_auth_token
--seed="42"
--fp16
--group_by_length
--do_train --do_eval
--push_to_hub
--preprocessing_num_workers="32"
```
Using these settings, the training might take 3-4 days on an average GPU. You can, however, get a decent model and faster results by tweaking these parameters.
| Parameter| Comment |
|:-------------|:-----|
| per_device_train_batch_size | Adjust this to the maximum of available memory. 16 or 24 might be good settings depending on your system |
|gradient_accumulation_steps |Can be adjusted even further up to increase batch size and speed up training without running into memory issues |
| learning_rate|Can be increased, maybe as high as 1e-4. Speeds up training but might add instability |
| epochs| Can be decreased significantly. This is a huge dataset and you might get a decent result already after a couple of epochs|
## Citation
```bibtex
@inproceedings{de-la-rosa-etal-2023-boosting,
title = "Boosting {N}orwegian Automatic Speech Recognition",
author = "De La Rosa, Javier and
Braaten, Rolv-Arild and
Kummervold, Per and
Wetjen, Freddy",
booktitle = "Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa)",
month = may,
year = "2023",
address = "T{\'o}rshavn, Faroe Islands",
publisher = "University of Tartu Library",
url = "https://aclanthology.org/2023.nodalida-1.55",
pages = "555--564",
abstract = "In this paper, we present several baselines for automatic speech recognition (ASR) models for the two official written languages in Norway: Bokm{\aa}l and Nynorsk. We compare the performance of models of varying sizes and pre-training approaches on multiple Norwegian speech datasets. Additionally, we measure the performance of these models against previous state-of-the-art ASR models, as well as on out-of-domain datasets. We improve the state of the art on the Norwegian Parliamentary Speech Corpus (NPSC) from a word error rate (WER) of 17.10{\%} to 7.60{\%}, with models achieving 5.81{\%} for Bokm{\aa}l and 11.54{\%} for Nynorsk. We also discuss the challenges and potential solutions for further improving ASR models for Norwegian.",
}
```
See https://arxiv.org/abs/2307.01672
| {"language": [false, "nb"], "license": "apache-2.0", "tags": ["automatic-speech-recognition"], "datasets": ["NbAiLab/NPSC"], "model-index": [{"name": "nb-wav2vec2-300m-bokmaal", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "NPSC", "type": "NbAiLab/NPSC", "args": "16K_mp3_bokmaal"}, "metrics": [{"type": "wer", "value": 0.0703, "name": "Test (Bokm\u00e5l) WER"}, {"type": "cer", "value": 0.0269, "name": "Test (Bokm\u00e5l) CER"}]}]}]} | automatic-speech-recognition | NbAiLab/nb-wav2vec2-300m-bokmaal | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"no",
"nb",
"dataset:NbAiLab/NPSC",
"arxiv:2307.01672",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2307.01672"
] | [
"no",
"nb"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #no #nb #dataset-NbAiLab/NPSC #arxiv-2307.01672 #license-apache-2.0 #model-index #endpoints_compatible #region-us
| Norwegian Wav2Vec2 Model - 300M - VoxRex - Bokmål
=================================================
This model is finetuned on top of feature extractor VoxRex-model from the National Library of Sweden. The finetuned model achieves the following results on the test set with a 5-gram KenLM. The numbers in parentheses are the results without the language model:
* WER: 0.0703 (0.0979)
* CER: 0.0269 (0.0311)
Model description
-----------------
This is one of several Wav2Vec-models our team created during the hosted Robust Speech Event. This is the complete list of our models and their final scores:
Dataset
-------
In parallel with the event, the team also converted the Norwegian Parliamentary Speech Corpus (NPSC) to the NbAiLab/NPSC in Dataset format and used that as the main source for training.
Code
----
We have released all the code developed during the event so that the Norwegian NLP community can build upon it when developing even better Norwegian ASR models. The finetuning of these models is not very computationally demanding. After following the instructions here, you should be able to train your own automatic speech recognition system in less than a day with an average GPU.
Team
----
The following people contributed to building this model: Rolv-Arild Braaten, Per Egil Kummervold, Andre Kåsen, Javier de la Rosa, Per Erik Solberg, and Freddy Wetjen.
Training procedure
------------------
To reproduce these results, we strongly recommend that you follow the instructions from to train a simple Swedish model.
When you have verified that you are able to do this, create a fresh new repo. You can then start by copying the files and from our repo. Running these will create all the other necessary files, and should let you reproduce our results. With some tweaks to the hyperparameters, you might even be able to build an even better ASR. Good luck!
### Language Model
As the scores indicate, adding even a simple 5-gram language will improve the results. has provided another very nice blog explaining how to add a 5-gram language model to improve the ASR model. You can build this from your own corpus, for instance by extracting some suitable text from the Norwegian Colossal Corpus. You can also skip some of the steps in the guide, and copy the 5-gram model from this repo.
### Parameters
The final model was run using these parameters:
Using these settings, the training might take 3-4 days on an average GPU. You can, however, get a decent model and faster results by tweaking these parameters.
See URL
| [
"### Language Model\n\n\nAs the scores indicate, adding even a simple 5-gram language will improve the results. has provided another very nice blog explaining how to add a 5-gram language model to improve the ASR model. You can build this from your own corpus, for instance by extracting some suitable text from the Norwegian Colossal Corpus. You can also skip some of the steps in the guide, and copy the 5-gram model from this repo.",
"### Parameters\n\n\nThe final model was run using these parameters:\n\n\nUsing these settings, the training might take 3-4 days on an average GPU. You can, however, get a decent model and faster results by tweaking these parameters.\n\n\n\nSee URL"
] | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #no #nb #dataset-NbAiLab/NPSC #arxiv-2307.01672 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Language Model\n\n\nAs the scores indicate, adding even a simple 5-gram language will improve the results. has provided another very nice blog explaining how to add a 5-gram language model to improve the ASR model. You can build this from your own corpus, for instance by extracting some suitable text from the Norwegian Colossal Corpus. You can also skip some of the steps in the guide, and copy the 5-gram model from this repo.",
"### Parameters\n\n\nThe final model was run using these parameters:\n\n\nUsing these settings, the training might take 3-4 days on an average GPU. You can, however, get a decent model and faster results by tweaking these parameters.\n\n\n\nSee URL"
] | [
77,
92,
57
] | [
"passage: TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #no #nb #dataset-NbAiLab/NPSC #arxiv-2307.01672 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n### Language Model\n\n\nAs the scores indicate, adding even a simple 5-gram language will improve the results. has provided another very nice blog explaining how to add a 5-gram language model to improve the ASR model. You can build this from your own corpus, for instance by extracting some suitable text from the Norwegian Colossal Corpus. You can also skip some of the steps in the guide, and copy the 5-gram model from this repo.### Parameters\n\n\nThe final model was run using these parameters:\n\n\nUsing these settings, the training might take 3-4 days on an average GPU. You can, however, get a decent model and faster results by tweaking these parameters.\n\n\n\nSee URL"
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null | null | transformers |
# Norwegian Wav2Vec2 Model - 300M - VoxRex - Nynorsk
This model is finetuned on top of feature extractor [VoxRex-model](https://huggingface.co/KBLab/wav2vec2-large-voxrex) from the National Library of Sweden. The finetuned model achieves the following results on the test set with a 5-gram KenLM. The numbers in parentheses are the results without the language model:
- **WER: 0.1222** (0.1537)
- **CER: 0.0419** (0.0468)
## Model description
This is one of several Wav2Vec-models our team created during the 🤗 hosted [Robust Speech Event](https://discuss.huggingface.co/t/open-to-the-community-robust-speech-recognition-challenge/13614?s=09). This is the complete list of our models and their final scores:
| Model | Final WER | |
|:--------------|:------------|:------------:|
| [NbAiLab/nb-wav2vec2-1b-bokmaal](https://huggingface.co/NbAiLab/nb-wav2vec2-1b-bokmaal) | 6.33 | |
| [NbAiLab/nb-wav2vec2-300m-bokmaal](https://huggingface.co/NbAiLab/nb-wav2vec2-300m-bokmaal) | 7.03 | |
| [NbAiLab/nb-wav2vec2-1b-nynorsk](https://huggingface.co/NbAiLab/nb-wav2vec2-1b-nynorsk) | 11.32 | |
| NbAiLab/nb-wav2vec2-300m-nynorsk (this model) | 12.22 | |
### Dataset
In parallel with the event, the team also converted the [Norwegian Parliamentary Speech Corpus (NPSC)](https://www.nb.no/sprakbanken/en/resource-catalogue/oai-nb-no-sbr-58/) to the [NbAiLab/NPSC](https://huggingface.co/datasets/NbAiLab/NPSC) in 🤗 Dataset format and used that as the main source for training.
## Code
We have released all the code developed during the event so that the Norwegian NLP community can build upon it when developing even better Norwegian ASR models. The finetuning of these models is not very computationally demanding. After following the instructions here, you should be able to train your own automatic speech recognition system in less than a day with an average GPU.
## Team
The following people contributed to building this model: Rolv-Arild Braaten, Per Egil Kummervold, Andre Kåsen, Javier de la Rosa, Per Erik Solberg, and Freddy Wetjen.
## Training procedure
To reproduce these results, we strongly recommend that you follow the [instructions from 🤗](https://github.com/huggingface/transformers/tree/master/examples/research_projects/robust-speech-event#talks) to train a simple Swedish model.
When you have verified that you are able to do this, create a fresh new repo. You can then start by copying the files ```run.sh``` and ```run_speech_recognition_ctc.py``` from our repo. Running these will create all the other necessary files, and should let you reproduce our results. With some tweaks to the hyperparameters, you might even be able to build an even better ASR. Good luck!
### Language Model
As the scores indicate, adding even a simple 5-gram language will improve the results. 🤗 has provided another [very nice blog](https://huggingface.co/blog/wav2vec2-with-ngram) explaining how to add a 5-gram language model to improve the ASR model. You can build this from your own corpus, for instance by extracting some suitable text from the [Norwegian Colossal Corpus](https://huggingface.co/datasets/NbAiLab/NCC). You can also skip some of the steps in the guide, and copy the [5-gram model from this repo](https://huggingface.co/NbAiLab/XLSR-300M-bokmaal/tree/main/language_model).
### Parameters
The final model was run using these parameters:
```
--dataset_name="NbAiLab/NPSC"
--model_name_or_path="KBLab/wav2vec2-large-voxrex"
--dataset_config_name="16K_mp3_nynorsk"
--output_dir="./"
--overwrite_output_dir
--num_train_epochs="80"
--per_device_train_batch_size="16"
--per_device_eval_batch_size="16"
--gradient_accumulation_steps="2"
--learning_rate="1e-4"
--warmup_steps="2000"
--length_column_name="input_length"
--evaluation_strategy="steps"
--text_column_name="text"
--save_steps="500"
--eval_steps="500"
--logging_steps="100"
--layerdrop="0.041"
--attention_dropout="0.094"
--activation_dropout="0.055"
--hidden_dropout="0.047"
--save_total_limit="3"
--freeze_feature_encoder
--feat_proj_dropout="0.04"
--mask_time_prob="0.082"
--mask_time_length="10"
--mask_feature_prob="0.25"
--mask_feature_length="64"
--gradient_checkpointing
--min_duration_in_seconds="0.5"
--max_duration_in_seconds="30.0"
--use_auth_token
--seed="42"
--fp16
--group_by_length
--do_train --do_eval
--push_to_hub
--preprocessing_num_workers="32"
```
Using these settings, the training might take 3-4 days on an average GPU. You can, however, get a decent model and faster results by tweaking these parameters.
| Parameter| Comment |
|:-------------|:-----|
| per_device_train_batch_size | Adjust this to the maximum of available memory. 16 or 24 might be good settings depending on your system |
|gradient_accumulation_steps |Can be adjusted even further up to increase batch size and speed up training without running into memory issues |
| learning_rate|Can be increased, maybe as high as 1e-4. Speeds up training but might add instability |
| epochs| Can be decreased significantly. This is a huge dataset and you might get a decent result already after a couple of epochs|
## Citation
```bibtex
@inproceedings{de-la-rosa-etal-2023-boosting,
title = "Boosting {N}orwegian Automatic Speech Recognition",
author = "De La Rosa, Javier and
Braaten, Rolv-Arild and
Kummervold, Per and
Wetjen, Freddy",
booktitle = "Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa)",
month = may,
year = "2023",
address = "T{\'o}rshavn, Faroe Islands",
publisher = "University of Tartu Library",
url = "https://aclanthology.org/2023.nodalida-1.55",
pages = "555--564",
abstract = "In this paper, we present several baselines for automatic speech recognition (ASR) models for the two official written languages in Norway: Bokm{\aa}l and Nynorsk. We compare the performance of models of varying sizes and pre-training approaches on multiple Norwegian speech datasets. Additionally, we measure the performance of these models against previous state-of-the-art ASR models, as well as on out-of-domain datasets. We improve the state of the art on the Norwegian Parliamentary Speech Corpus (NPSC) from a word error rate (WER) of 17.10{\%} to 7.60{\%}, with models achieving 5.81{\%} for Bokm{\aa}l and 11.54{\%} for Nynorsk. We also discuss the challenges and potential solutions for further improving ASR models for Norwegian.",
}
```
See https://arxiv.org/abs/2307.01672
| {"language": ["nn"], "license": "apache-2.0", "tags": ["automatic-speech-recognition"], "datasets": ["NbAiLab/NPSC"], "model-index": [{"name": "nb-wav2vec2-300m-nynorsk", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "NPSC", "type": "NbAiLab/NPSC", "args": "16K_mp3_nynorsk"}, "metrics": [{"type": "wer", "value": 0.1222, "name": "Test (Nynorsk) WER"}, {"type": "cer", "value": 0.0419, "name": "Test (Nynorsk) CER"}]}]}]} | automatic-speech-recognition | NbAiLab/nb-wav2vec2-300m-nynorsk | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"nn",
"dataset:NbAiLab/NPSC",
"arxiv:2307.01672",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2307.01672"
] | [
"nn"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #nn #dataset-NbAiLab/NPSC #arxiv-2307.01672 #license-apache-2.0 #model-index #endpoints_compatible #region-us
| Norwegian Wav2Vec2 Model - 300M - VoxRex - Nynorsk
==================================================
This model is finetuned on top of feature extractor VoxRex-model from the National Library of Sweden. The finetuned model achieves the following results on the test set with a 5-gram KenLM. The numbers in parentheses are the results without the language model:
* WER: 0.1222 (0.1537)
* CER: 0.0419 (0.0468)
Model description
-----------------
This is one of several Wav2Vec-models our team created during the hosted Robust Speech Event. This is the complete list of our models and their final scores:
### Dataset
In parallel with the event, the team also converted the Norwegian Parliamentary Speech Corpus (NPSC) to the NbAiLab/NPSC in Dataset format and used that as the main source for training.
Code
----
We have released all the code developed during the event so that the Norwegian NLP community can build upon it when developing even better Norwegian ASR models. The finetuning of these models is not very computationally demanding. After following the instructions here, you should be able to train your own automatic speech recognition system in less than a day with an average GPU.
Team
----
The following people contributed to building this model: Rolv-Arild Braaten, Per Egil Kummervold, Andre Kåsen, Javier de la Rosa, Per Erik Solberg, and Freddy Wetjen.
Training procedure
------------------
To reproduce these results, we strongly recommend that you follow the instructions from to train a simple Swedish model.
When you have verified that you are able to do this, create a fresh new repo. You can then start by copying the files and from our repo. Running these will create all the other necessary files, and should let you reproduce our results. With some tweaks to the hyperparameters, you might even be able to build an even better ASR. Good luck!
### Language Model
As the scores indicate, adding even a simple 5-gram language will improve the results. has provided another very nice blog explaining how to add a 5-gram language model to improve the ASR model. You can build this from your own corpus, for instance by extracting some suitable text from the Norwegian Colossal Corpus. You can also skip some of the steps in the guide, and copy the 5-gram model from this repo.
### Parameters
The final model was run using these parameters:
Using these settings, the training might take 3-4 days on an average GPU. You can, however, get a decent model and faster results by tweaking these parameters.
See URL
| [
"### Dataset\n\n\nIn parallel with the event, the team also converted the Norwegian Parliamentary Speech Corpus (NPSC) to the NbAiLab/NPSC in Dataset format and used that as the main source for training.\n\n\nCode\n----\n\n\nWe have released all the code developed during the event so that the Norwegian NLP community can build upon it when developing even better Norwegian ASR models. The finetuning of these models is not very computationally demanding. After following the instructions here, you should be able to train your own automatic speech recognition system in less than a day with an average GPU.\n\n\nTeam\n----\n\n\nThe following people contributed to building this model: Rolv-Arild Braaten, Per Egil Kummervold, Andre Kåsen, Javier de la Rosa, Per Erik Solberg, and Freddy Wetjen.\n\n\nTraining procedure\n------------------\n\n\nTo reproduce these results, we strongly recommend that you follow the instructions from to train a simple Swedish model.\n\n\nWhen you have verified that you are able to do this, create a fresh new repo. You can then start by copying the files and from our repo. Running these will create all the other necessary files, and should let you reproduce our results. With some tweaks to the hyperparameters, you might even be able to build an even better ASR. Good luck!",
"### Language Model\n\n\nAs the scores indicate, adding even a simple 5-gram language will improve the results. has provided another very nice blog explaining how to add a 5-gram language model to improve the ASR model. You can build this from your own corpus, for instance by extracting some suitable text from the Norwegian Colossal Corpus. You can also skip some of the steps in the guide, and copy the 5-gram model from this repo.",
"### Parameters\n\n\nThe final model was run using these parameters:\n\n\nUsing these settings, the training might take 3-4 days on an average GPU. You can, however, get a decent model and faster results by tweaking these parameters.\n\n\n\nSee URL"
] | [
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"### Dataset\n\n\nIn parallel with the event, the team also converted the Norwegian Parliamentary Speech Corpus (NPSC) to the NbAiLab/NPSC in Dataset format and used that as the main source for training.\n\n\nCode\n----\n\n\nWe have released all the code developed during the event so that the Norwegian NLP community can build upon it when developing even better Norwegian ASR models. The finetuning of these models is not very computationally demanding. After following the instructions here, you should be able to train your own automatic speech recognition system in less than a day with an average GPU.\n\n\nTeam\n----\n\n\nThe following people contributed to building this model: Rolv-Arild Braaten, Per Egil Kummervold, Andre Kåsen, Javier de la Rosa, Per Erik Solberg, and Freddy Wetjen.\n\n\nTraining procedure\n------------------\n\n\nTo reproduce these results, we strongly recommend that you follow the instructions from to train a simple Swedish model.\n\n\nWhen you have verified that you are able to do this, create a fresh new repo. You can then start by copying the files and from our repo. Running these will create all the other necessary files, and should let you reproduce our results. With some tweaks to the hyperparameters, you might even be able to build an even better ASR. Good luck!",
"### Language Model\n\n\nAs the scores indicate, adding even a simple 5-gram language will improve the results. has provided another very nice blog explaining how to add a 5-gram language model to improve the ASR model. You can build this from your own corpus, for instance by extracting some suitable text from the Norwegian Colossal Corpus. You can also skip some of the steps in the guide, and copy the 5-gram model from this repo.",
"### Parameters\n\n\nThe final model was run using these parameters:\n\n\nUsing these settings, the training might take 3-4 days on an average GPU. You can, however, get a decent model and faster results by tweaking these parameters.\n\n\n\nSee URL"
] | [
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281,
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] |
null | null | transformers |
## Results
|**Model** | **NoRec** | **NorNe-NB**| **NorNe-NN** | **NorDial** | **DaNe** | **Da-Angry-Tweets** |
|:-----------|------------:|------------:|------------:|------------:|------------:|------------:|
|roberta-base (English) | 51.77 | 79.01/79.53| 79.79/83.02 | 67.18| 75.44/78.07 | 55.51 |
|mBERT-cased | 63.91 | 83.72/86.12| 83.05/87.12 | 66.23| 80.00/81.43 | 57.67 |
|nb-bert-base | 75.60 |**91.98**/**92.95** |**90.93**/**94.06**|69.39| 81.95/84.83| 64.18|
|notram-bert-norwegian-cased | 72.47 | 91.77/93.12|89.79/93.70| **78.55**| **83.69**/**86.55**| **64.19** |
|notram-bert-norwegian-uncased | 73.47 | 89.28/91.61 |87.23/90.23 |74.21 | 80.29/82.31| 61.18|
|notram-bert-norwegian-cased-pod | **76.18** | 91.24/92.24| 90.88/93.21| 76.21| 81.82/84.99| 62.16 |
|nb-roberta-base | 68.77 |87.99/89.43 | 85.43/88.66| 76.34| 75.91/77.94| 61.50 |
|nb-roberta-base-scandinavian | 67.88 | 87.73/89.14| 87.39/90.92| 74.81| 76.22/78.66 | 63.37 |
|nb-roberta-base-v2-200k | 46.87 | 85.57/87.04| - | 64.99| - | - |
|test_long_w5 200k| 60.48 | 88.00/90:00 | 83.93/88.45 | 68.41 |75.22/78.50| 57.95 |
|test_long_w5_roberta_tokenizer 200k| 63.51| 86.28/87.77| 84.95/88.61 | 69.86 | 71.31/74.27 | 59.96 |
|test_long_w5_roberta_tokenizer 400k| 59.76 |87.39/89.06 | 85.16/89.01 | 71.46 | 72.39/75.65| 39.73 |
|test_long_w5_dataset 400k| 66.80 | 86.52/88.55 | 82.81/86.76 | 66.94 | 71.47/74.20| 55.25 |
|test_long_w5_dataset 600k| 67.37 | 89.98/90.95 | 84.53/88.37 | 66.84 | 75.14/76.50| 57.47 |
|roberta-jan-128_ncc - 400k - 128| 67.79 | 91.45/92.33 | 86.41/90.19 | 67.20 | 81.00/82.39| 59.65 |
|roberta-jan-128_ncc - 1000k - 128| 68.17 | 89.34/90.74 | 86.89/89.87 | 68.41 | 80.30/82.17| 61.63 | | {"language": false, "license": "cc-by-4.0", "tags": ["norwegian", "bert"], "pipeline_tag": "fill-mask", "widget": [{"text": "P\u00e5 biblioteket kan du [MASK] en bok."}, {"text": "Dette er et [MASK] eksempel."}, {"text": "Av og til kan en spr\u00e5kmodell gi et [MASK] resultat."}, {"text": "Som ansat f\u00e5r du [MASK] for at bidrage til borgernes adgang til dansk kulturarv, til forskning og til samfundets demokratiske udvikling."}]} | fill-mask | NbAiLab/notram-bert-norwegian-cased-080321 | [
"transformers",
"pytorch",
"tf",
"safetensors",
"bert",
"norwegian",
"fill-mask",
"no",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"no"
] | TAGS
#transformers #pytorch #tf #safetensors #bert #norwegian #fill-mask #no #license-cc-by-4.0 #endpoints_compatible #region-us
| Results
-------
| [] | [
"TAGS\n#transformers #pytorch #tf #safetensors #bert #norwegian #fill-mask #no #license-cc-by-4.0 #endpoints_compatible #region-us \n"
] | [
51
] | [
"passage: TAGS\n#transformers #pytorch #tf #safetensors #bert #norwegian #fill-mask #no #license-cc-by-4.0 #endpoints_compatible #region-us \n"
] | [
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null | null | transformers | Just for performing some experiments. Do not use.
| {} | fill-mask | NbAiLab/roberta_NCC_des_128 | [
"transformers",
"pytorch",
"jax",
"tensorboard",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #tensorboard #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| Just for performing some experiments. Do not use.
| [] | [
"TAGS\n#transformers #pytorch #jax #tensorboard #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
44
] | [
"passage: TAGS\n#transformers #pytorch #jax #tensorboard #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
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null | null | transformers | Just for performing some experiments. Do not use.
| {} | fill-mask | NbAiLab/roberta_NCC_des_128_decayfrom200 | [
"transformers",
"pytorch",
"jax",
"tensorboard",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #tensorboard #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| Just for performing some experiments. Do not use.
| [] | [
"TAGS\n#transformers #pytorch #jax #tensorboard #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
44
] | [
"passage: TAGS\n#transformers #pytorch #jax #tensorboard #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
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null | null | transformers | Just for performing some experiments. Do not use.
This needed to be restarted at 100k. I am getting memory errors at the end of the epoch. Not really sure why.
Step 2 is therefore on train_2__4. Static learning rate for a while. The first 100k ended at 0.59. This is decent so early. No point in running more epochs here though. Changing the corpus and continue training.
| {} | fill-mask | NbAiLab/roberta_des_128 | [
"transformers",
"jax",
"tensorboard",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #jax #tensorboard #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| Just for performing some experiments. Do not use.
This needed to be restarted at 100k. I am getting memory errors at the end of the epoch. Not really sure why.
Step 2 is therefore on train_2__4. Static learning rate for a while. The first 100k ended at 0.59. This is decent so early. No point in running more epochs here though. Changing the corpus and continue training.
| [] | [
"TAGS\n#transformers #jax #tensorboard #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
40
] | [
"passage: TAGS\n#transformers #jax #tensorboard #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
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] |
null | null | transformers | Just for performing some experiments. Do not use.
Since the loss seem to start going up, I did have to restore this from 9e945cb0636bde60bec30bd7df5db30f80401cc7 (2 step 600k/200). I am then restarting with warmup decaying from 1e-4.
That did failed. Checked out c94b5bb43b05fc798f9db013d940b05b3b47cd98 instead and restarted step 3 from here.
| {} | fill-mask | NbAiLab/roberta_des_512 | [
"transformers",
"pytorch",
"jax",
"tensorboard",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #tensorboard #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| Just for performing some experiments. Do not use.
Since the loss seem to start going up, I did have to restore this from 9e945cb0636bde60bec30bd7df5db30f80401cc7 (2 step 600k/200). I am then restarting with warmup decaying from 1e-4.
That did failed. Checked out c94b5bb43b05fc798f9db013d940b05b3b47cd98 instead and restarted step 3 from here.
| [] | [
"TAGS\n#transformers #pytorch #jax #tensorboard #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
44
] | [
"passage: TAGS\n#transformers #pytorch #jax #tensorboard #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
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null | null | transformers | Just for performing some experiments. Do not use.
| {} | fill-mask | NbAiLab/roberta_des_512_4e4 | [
"transformers",
"jax",
"tensorboard",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #jax #tensorboard #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| Just for performing some experiments. Do not use.
| [] | [
"TAGS\n#transformers #jax #tensorboard #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
40
] | [
"passage: TAGS\n#transformers #jax #tensorboard #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
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null | null | transformers | Just for performing some experiments. Do not use.
| {} | fill-mask | NbAiLab/roberta_des_512_6e4 | [
"transformers",
"jax",
"tensorboard",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #jax #tensorboard #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| Just for performing some experiments. Do not use.
| [] | [
"TAGS\n#transformers #jax #tensorboard #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
40
] | [
"passage: TAGS\n#transformers #jax #tensorboard #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
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null | null | transformers | Just for performing some experiments. Do not use.
| {} | fill-mask | NbAiLab/roberta_des_ada_128 | [
"transformers",
"jax",
"tensorboard",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #jax #tensorboard #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| Just for performing some experiments. Do not use.
| [] | [
"TAGS\n#transformers #jax #tensorboard #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
40
] | [
"passage: TAGS\n#transformers #jax #tensorboard #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
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null | null | transformers | Just for performing some experiments. Do not use.
| {} | fill-mask | NbAiLab/roberta_des_ada_128_6e4 | [
"transformers",
"jax",
"tensorboard",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #jax #tensorboard #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| Just for performing some experiments. Do not use.
| [] | [
"TAGS\n#transformers #jax #tensorboard #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
40
] | [
"passage: TAGS\n#transformers #jax #tensorboard #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
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null | null | transformers | Just for performing some experiments. Do not use. | {} | fill-mask | NbAiLabArchive/test_NCC_OSCAR_16w_noada | [
"transformers",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| Just for performing some experiments. Do not use. | [] | [
"TAGS\n#transformers #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
33
] | [
"passage: TAGS\n#transformers #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
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null | null | transformers | Just for performing some experiments. Do not use. | {} | fill-mask | NbAiLabArchive/test_NCC_OSCAR_style | [
"transformers",
"jax",
"tensorboard",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #jax #tensorboard #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| Just for performing some experiments. Do not use. | [] | [
"TAGS\n#transformers #jax #tensorboard #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
40
] | [
"passage: TAGS\n#transformers #jax #tensorboard #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
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null | null | transformers | Just for performing some experiments. Do not use. | {} | fill-mask | NbAiLabArchive/test_NCC_OSCAR_style_98w | [
"transformers",
"jax",
"tensorboard",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #jax #tensorboard #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| Just for performing some experiments. Do not use. | [] | [
"TAGS\n#transformers #jax #tensorboard #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
40
] | [
"passage: TAGS\n#transformers #jax #tensorboard #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
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null | null | transformers | Just for performing some experiments. Do not use. | {} | fill-mask | NbAiLabArchive/test_NCC_small_flax | [
"transformers",
"jax",
"tensorboard",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #jax #tensorboard #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| Just for performing some experiments. Do not use. | [] | [
"TAGS\n#transformers #jax #tensorboard #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
40
] | [
"passage: TAGS\n#transformers #jax #tensorboard #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
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null | null | transformers | Just for performing some experiments. Do not use. | {} | fill-mask | NbAiLabArchive/test_NCC_small_flax_stream | [
"transformers",
"jax",
"tensorboard",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #jax #tensorboard #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| Just for performing some experiments. Do not use. | [] | [
"TAGS\n#transformers #jax #tensorboard #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
40
] | [
"passage: TAGS\n#transformers #jax #tensorboard #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
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null | null | transformers | Just for performing some experiments. Do not use. | {} | fill-mask | NbAiLabArchive/test_NCC_small_flax_stream_100 | [
"transformers",
"jax",
"tensorboard",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #jax #tensorboard #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| Just for performing some experiments. Do not use. | [] | [
"TAGS\n#transformers #jax #tensorboard #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
40
] | [
"passage: TAGS\n#transformers #jax #tensorboard #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
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null | null | transformers | Just for performing some experiments. Do not use. | {} | fill-mask | NbAiLabArchive/test_NCC_small_pytorch | [
"transformers",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| Just for performing some experiments. Do not use. | [] | [
"TAGS\n#transformers #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
33
] | [
"passage: TAGS\n#transformers #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
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null | null | transformers | Just for performing some experiments. Do not use. | {} | fill-mask | NbAiLabArchive/test_OSCAR_flax | [
"transformers",
"jax",
"tensorboard",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #jax #tensorboard #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| Just for performing some experiments. Do not use. | [] | [
"TAGS\n#transformers #jax #tensorboard #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
40
] | [
"passage: TAGS\n#transformers #jax #tensorboard #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
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null | null | transformers | Just for performing some experiments. Do not use. | {} | fill-mask | NbAiLabArchive/test_w4 | [
"transformers",
"jax",
"tensorboard",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #jax #tensorboard #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| Just for performing some experiments. Do not use. | [] | [
"TAGS\n#transformers #jax #tensorboard #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
40
] | [
"passage: TAGS\n#transformers #jax #tensorboard #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
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null | null | transformers | Just for performing some experiments. Do not use. | {} | fill-mask | NbAiLabArchive/test_w5 | [
"transformers",
"pytorch",
"jax",
"tensorboard",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #tensorboard #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| Just for performing some experiments. Do not use. | [] | [
"TAGS\n#transformers #pytorch #jax #tensorboard #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
44
] | [
"passage: TAGS\n#transformers #pytorch #jax #tensorboard #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
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null | null | transformers | Just for performing some experiments. Do not use. | {} | fill-mask | NbAiLabArchive/test_w5_long | [
"transformers",
"pytorch",
"jax",
"tensorboard",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #tensorboard #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| Just for performing some experiments. Do not use. | [] | [
"TAGS\n#transformers #pytorch #jax #tensorboard #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
44
] | [
"passage: TAGS\n#transformers #pytorch #jax #tensorboard #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
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null | null | transformers | Just for performing some experiments. Do not use. | {} | fill-mask | NbAiLabArchive/test_w5_long_dataset | [
"transformers",
"pytorch",
"jax",
"tensorboard",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #tensorboard #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| Just for performing some experiments. Do not use. | [] | [
"TAGS\n#transformers #pytorch #jax #tensorboard #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
44
] | [
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null | null | transformers | Just for performing some experiments. Do not use. | {} | fill-mask | NbAiLabArchive/test_w5_long_roberta_tokenizer | [
"transformers",
"pytorch",
"jax",
"tensorboard",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #tensorboard #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| Just for performing some experiments. Do not use. | [] | [
"TAGS\n#transformers #pytorch #jax #tensorboard #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
44
] | [
"passage: TAGS\n#transformers #pytorch #jax #tensorboard #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
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null | null | transformers | Just for performing some experiments. Do not use. | {} | fill-mask | NbAiLabArchive/test_w5_long_roberta_tokenizer_adafactor | [
"transformers",
"jax",
"tensorboard",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #jax #tensorboard #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| Just for performing some experiments. Do not use. | [] | [
"TAGS\n#transformers #jax #tensorboard #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
40
] | [
"passage: TAGS\n#transformers #jax #tensorboard #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
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null | null | transformers | Just for performing some experiments. Do not use. | {} | fill-mask | NbAiLabArchive/test_w6 | [
"transformers",
"jax",
"tensorboard",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #jax #tensorboard #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| Just for performing some experiments. Do not use. | [] | [
"TAGS\n#transformers #jax #tensorboard #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
40
] | [
"passage: TAGS\n#transformers #jax #tensorboard #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
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null | null | transformers | Just for performing some experiments. Do not use. | {} | fill-mask | NbAiLabArchive/test_w7 | [
"transformers",
"jax",
"tensorboard",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #jax #tensorboard #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| Just for performing some experiments. Do not use. | [] | [
"TAGS\n#transformers #jax #tensorboard #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
40
] | [
"passage: TAGS\n#transformers #jax #tensorboard #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
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null | null | transformers | Just for performing some experiments. Do not use. | {} | fill-mask | NbAiLabArchive/test_w8 | [
"transformers",
"jax",
"tensorboard",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #jax #tensorboard #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| Just for performing some experiments. Do not use. | [] | [
"TAGS\n#transformers #jax #tensorboard #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
40
] | [
"passage: TAGS\n#transformers #jax #tensorboard #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
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null | null | null |
## Citation
```bibtex
@inproceedings{de-la-rosa-etal-2023-boosting,
title = "Boosting {N}orwegian Automatic Speech Recognition",
author = "De La Rosa, Javier and
Braaten, Rolv-Arild and
Kummervold, Per and
Wetjen, Freddy",
booktitle = "Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa)",
month = may,
year = "2023",
address = "T{\'o}rshavn, Faroe Islands",
publisher = "University of Tartu Library",
url = "https://aclanthology.org/2023.nodalida-1.55",
pages = "555--564",
abstract = "In this paper, we present several baselines for automatic speech recognition (ASR) models for the two official written languages in Norway: Bokm{\aa}l and Nynorsk. We compare the performance of models of varying sizes and pre-training approaches on multiple Norwegian speech datasets. Additionally, we measure the performance of these models against previous state-of-the-art ASR models, as well as on out-of-domain datasets. We improve the state of the art on the Norwegian Parliamentary Speech Corpus (NPSC) from a word error rate (WER) of 17.10{\%} to 7.60{\%}, with models achieving 5.81{\%} for Bokm{\aa}l and 11.54{\%} for Nynorsk. We also discuss the challenges and potential solutions for further improving ASR models for Norwegian.",
}
``` | {"license": "apache-2.0"} | null | NbAiLab/nb-wav2vec2-kenlm | [
"license:apache-2.0",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#license-apache-2.0 #region-us
| [] | [
"TAGS\n#license-apache-2.0 #region-us \n"
] | [
14
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|
null | null | transformers |
<!-- 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-voxrex-npsc-bokmaal
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1311
- Wer: 0.1038
## 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: 8.379967082059723e-06
- train_batch_size: 16
- eval_batch_size: 16
- 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
- num_epochs: 0.1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2127 | 0.32 | 500 | 0.1335 | 0.1047 |
| 0.1976 | 0.64 | 1000 | 0.1309 | 0.1039 |
| 0.1887 | 0.97 | 1500 | 0.1306 | 0.1040 |
| 0.18 | 1.29 | 2000 | 0.1311 | 0.1038 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu113
- Datasets 1.18.4.dev0
- Tokenizers 0.11.0
| {"language": ["nb-NO"], "license": "apache-2.0", "tags": ["generated_from_trainer", "automatic-speech-recognition", "NbAiLab/NPSC", "robust-speech-event", false, "nb-NO", "hf-asr-leaderboard"], "datasets": ["NbAiLab/NPSC"], "model-index": [{"name": "wav2vec2-large-voxrex-npsc-bokmaal", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "NPSC", "type": "NbAiLab/NPSC", "args": "16K_mp3_bokmaal"}, "metrics": [{"type": "wer", "value": 0.07028972259374369, "name": "Test (Bokm\u00e5l) WER"}, {"type": "cer", "value": 0.026870600821650645, "name": "Test (Bokm\u00e5l) CER"}]}]}]} | automatic-speech-recognition | NbAiLab/wav2vec2-large-voxrex-npsc-bokmaal | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"dataset:NbAiLab/NPSC",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"nb-NO"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #dataset-NbAiLab/NPSC #license-apache-2.0 #model-index #endpoints_compatible #region-us
| wav2vec2-large-voxrex-npsc-bokmaal
==================================
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1311
* Wer: 0.1038
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: 8.379967082059723e-06
* train\_batch\_size: 16
* eval\_batch\_size: 16
* 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
* num\_epochs: 0.1
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.17.0.dev0
* Pytorch 1.10.2+cu113
* Datasets 1.18.4.dev0
* Tokenizers 0.11.0
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 8.379967082059723e-06\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 0.1\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.17.0.dev0\n* Pytorch 1.10.2+cu113\n* Datasets 1.18.4.dev0\n* Tokenizers 0.11.0"
] | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 8.379967082059723e-06\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 0.1\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.17.0.dev0\n* Pytorch 1.10.2+cu113\n* Datasets 1.18.4.dev0\n* Tokenizers 0.11.0"
] | [
64,
147,
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39
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"passage: TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #dataset-NbAiLab/NPSC #license-apache-2.0 #model-index #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 8.379967082059723e-06\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 0.1\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.17.0.dev0\n* Pytorch 1.10.2+cu113\n* Datasets 1.18.4.dev0\n* Tokenizers 0.11.0"
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-0.06848857551813126
] |
null | null | transformers |
<!-- 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-voxrex-npsc-nynorsk
This model is a fine-tuned version of [KBLab/wav2vec2-large-voxrex](https://huggingface.co/KBLab/wav2vec2-large-voxrex) on the NBAILAB/NPSC - 16K_MP3_NYNORSK dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4142
- Wer: 0.1576
## 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: 7.5e-05
- train_batch_size: 16
- eval_batch_size: 16
- 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: 2000
- num_epochs: 40.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.086 | 2.17 | 500 | 3.0773 | 1.0 |
| 2.8532 | 4.35 | 1000 | 2.8393 | 1.0 |
| 0.9738 | 6.52 | 1500 | 0.7283 | 0.4890 |
| 0.6763 | 8.7 | 2000 | 0.5340 | 0.3662 |
| 0.5303 | 10.87 | 2500 | 0.4521 | 0.3140 |
| 0.4765 | 13.04 | 3000 | 0.4181 | 0.2853 |
| 0.4219 | 15.22 | 3500 | 0.4156 | 0.2934 |
| 0.3564 | 17.39 | 4000 | 0.3925 | 0.2509 |
| 0.3282 | 19.57 | 4500 | 0.3824 | 0.2420 |
| 0.3118 | 21.74 | 5000 | 0.3636 | 0.2354 |
| 0.2919 | 23.91 | 5500 | 0.3615 | 0.2281 |
| 0.2961 | 26.09 | 6000 | 0.3548 | 0.2255 |
| 0.284 | 28.26 | 6500 | 0.3526 | 0.2209 |
| 0.2566 | 30.43 | 7000 | 0.3526 | 0.2205 |
| 0.2422 | 32.61 | 7500 | 0.3569 | 0.2173 |
| 0.2472 | 34.78 | 8000 | 0.3592 | 0.2166 |
| 0.2337 | 36.96 | 8500 | 0.3625 | 0.2172 |
| 0.2315 | 39.13 | 9000 | 0.3580 | 0.2155 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
| {"language": ["nn-NO"], "license": "apache-2.0", "tags": ["generated_from_trainer", "automatic-speech-recognition", "NbAiLab/NPSC", "robust-speech-event", "no", "nn-NO", "hf-asr-leaderboard"], "datasets": ["NbAiLab/NPSC"], "model-index": [{"name": "wav2vec2-large-voxrex-npsc-nynorsk", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "NPSC", "type": "NbAiLab/NPSC", "args": "16K_mp3_nynorsk"}, "metrics": [{"type": "wer", "value": 0.12220762155059132, "name": "Test (Nynorsk) WER"}, {"type": "cer", "value": 0.04195612578778549, "name": "Test (Nynorsk) CER"}]}]}]} | automatic-speech-recognition | NbAiLab/wav2vec2-large-voxrex-npsc-nynorsk | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"NbAiLab/NPSC",
"robust-speech-event",
"no",
"nn-NO",
"hf-asr-leaderboard",
"dataset:NbAiLab/NPSC",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"nn-NO"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #NbAiLab/NPSC #robust-speech-event #no #nn-NO #hf-asr-leaderboard #dataset-NbAiLab/NPSC #license-apache-2.0 #model-index #endpoints_compatible #region-us
| wav2vec2-large-voxrex-npsc-nynorsk
==================================
This model is a fine-tuned version of KBLab/wav2vec2-large-voxrex on the NBAILAB/NPSC - 16K\_MP3\_NYNORSK dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4142
* Wer: 0.1576
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: 7.5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* 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: 2000
* num\_epochs: 40.0
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.17.0.dev0
* Pytorch 1.10.0+cu113
* Datasets 1.18.3
* Tokenizers 0.10.3
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 2000\n* num\\_epochs: 40.0\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.17.0.dev0\n* Pytorch 1.10.0+cu113\n* Datasets 1.18.3\n* Tokenizers 0.10.3"
] | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #NbAiLab/NPSC #robust-speech-event #no #nn-NO #hf-asr-leaderboard #dataset-NbAiLab/NPSC #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 2000\n* num\\_epochs: 40.0\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.17.0.dev0\n* Pytorch 1.10.0+cu113\n* Datasets 1.18.3\n* Tokenizers 0.10.3"
] | [
103,
160,
4,
38
] | [
"passage: TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #NbAiLab/NPSC #robust-speech-event #no #nn-NO #hf-asr-leaderboard #dataset-NbAiLab/NPSC #license-apache-2.0 #model-index #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 2000\n* num\\_epochs: 40.0\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.17.0.dev0\n* Pytorch 1.10.0+cu113\n* Datasets 1.18.3\n* Tokenizers 0.10.3"
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null | null | transformers |
<!-- 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-voxrex-npsc
This model is a fine-tuned version of [KBLab/wav2vec2-large-voxrex](https://huggingface.co/KBLab/wav2vec2-large-voxrex) on the NBAILAB/NPSC - 16K_MP3 dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- 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: 16
- eval_batch_size: 16
- 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: 2000
- num_epochs: 15.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 2.9728 | 0.32 | 500 | 2.9449 | 1.0 |
| 2.5099 | 0.64 | 1000 | 1.8492 | 0.9910 |
| 0.7872 | 0.97 | 1500 | 0.4467 | 0.3774 |
| 0.5993 | 1.29 | 2000 | 0.3181 | 0.2819 |
| 0.5134 | 1.61 | 2500 | 0.2638 | 0.2401 |
| 0.4544 | 1.93 | 3000 | 0.2287 | 0.2091 |
| 0.4085 | 2.26 | 3500 | 0.2153 | 0.1918 |
| 0.3921 | 2.58 | 4000 | 0.2004 | 0.1804 |
| 0.4613 | 2.9 | 4500 | 0.1905 | 0.1732 |
| 0.3402 | 3.22 | 5000 | 0.1778 | 0.1659 |
| 0.3258 | 3.55 | 5500 | 0.1732 | 0.1571 |
| 0.3044 | 3.87 | 6000 | 0.1677 | 0.1497 |
| 0.2914 | 4.19 | 6500 | 0.1597 | 0.1420 |
| 0.278 | 4.51 | 7000 | 0.1574 | 0.1386 |
| 0.2858 | 4.84 | 7500 | 0.1552 | 0.1300 |
| 0.2585 | 5.16 | 8000 | 0.1523 | 0.1276 |
| 0.2827 | 5.48 | 8500 | 0.1448 | 0.1265 |
| 0.3365 | 5.8 | 9000 | 0.1411 | 0.1232 |
| 0.2488 | 6.13 | 9500 | 0.1456 | 0.1195 |
| 0.2406 | 6.45 | 10000 | 0.1414 | 0.1194 |
| 0.2488 | 6.77 | 10500 | 0.1393 | 0.1173 |
| 0.3084 | 7.09 | 11000 | 0.1379 | 0.1164 |
| 0.2365 | 7.41 | 11500 | 0.1387 | 0.1165 |
| 0.2217 | 7.74 | 12000 | 0.1381 | 0.1132 |
| 0.2381 | 8.06 | 12500 | 0.1360 | 0.1126 |
| 0.2329 | 8.38 | 13000 | 0.1357 | 0.1124 |
| 0.2103 | 8.7 | 13500 | 0.1335 | 0.1087 |
| 0.2366 | 9.03 | 14000 | 0.1388 | 0.1105 |
| 0.2289 | 9.35 | 14500 | 0.1383 | 0.1098 |
| 0.2486 | 9.67 | 15000 | 0.1386 | 0.1087 |
| **0.2772** | **9.99** | **15500** | **0.1598** | **0.1093** |
| 0.2728 | 10.32 | 16000 | 0.1814 | 0.1110 |
| 0.3437 | 10.64 | 16500 | 0.2505 | 0.1124 |
| 0.431 | 10.96 | 17000 | 0.2828 | 0.1143 |
| 0.3929 | 11.28 | 17500 | 0.2977 | 0.1149 |
| 0.4396 | 11.61 | 18000 | 0.3198 | 0.1170 |
| 0.59 | 11.93 | 18500 | 0.4158 | 0.1315 |
| 0.7813 | 12.25 | 19000 | 0.6123 | 0.2208 |
| 0.9345 | 12.57 | 19500 | 0.6815 | 0.2885 |
| 0.998 | 12.89 | 20000 | 0.7587 | 0.1991 |
| 1.0493 | 13.22 | 20500 | 0.7583 | 0.1996 |
| 1.438 | 13.54 | 21000 | nan | 1.0 |
| 0.0 | 13.86 | 21500 | nan | 1.0 |
| 0.0 | 14.18 | 22000 | nan | 1.0 |
| 0.0 | 14.51 | 22500 | nan | 1.0 |
| 0.0 | 14.83 | 23000 | nan | 1.0 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu113
- Datasets 1.18.3.dev0
- Tokenizers 0.11.0
| {"license": "cc0-1.0", "tags": ["automatic-speech-recognition", "NbAiLab/NPSC", "generated_from_trainer", "robust-speech-event"], "datasets": ["NbAiLab/NPSC"], "base_model": "KBLab/wav2vec2-large-voxrex", "model-index": [{"name": "wav2vec2-large-voxrex-npsc", "results": []}]} | automatic-speech-recognition | NbAiLab/wav2vec2-large-voxrex-npsc | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"NbAiLab/NPSC",
"generated_from_trainer",
"robust-speech-event",
"dataset:NbAiLab/NPSC",
"base_model:KBLab/wav2vec2-large-voxrex",
"license:cc0-1.0",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #NbAiLab/NPSC #generated_from_trainer #robust-speech-event #dataset-NbAiLab/NPSC #base_model-KBLab/wav2vec2-large-voxrex #license-cc0-1.0 #endpoints_compatible #region-us
| wav2vec2-large-voxrex-npsc
==========================
This model is a fine-tuned version of KBLab/wav2vec2-large-voxrex on the NBAILAB/NPSC - 16K\_MP3 dataset.
It achieves the following results on the evaluation set:
* Loss: nan
* 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: 16
* eval\_batch\_size: 16
* 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: 2000
* num\_epochs: 15.0
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.17.0.dev0
* Pytorch 1.10.2+cu113
* Datasets 1.18.3.dev0
* Tokenizers 0.11.0
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 2000\n* num\\_epochs: 15.0\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.17.0.dev0\n* Pytorch 1.10.2+cu113\n* Datasets 1.18.3.dev0\n* Tokenizers 0.11.0"
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"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #NbAiLab/NPSC #generated_from_trainer #robust-speech-event #dataset-NbAiLab/NPSC #base_model-KBLab/wav2vec2-large-voxrex #license-cc0-1.0 #endpoints_compatible #region-us \n",
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"### Training results",
"### Framework versions\n\n\n* Transformers 4.17.0.dev0\n* Pytorch 1.10.2+cu113\n* Datasets 1.18.3.dev0\n* Tokenizers 0.11.0"
] | [
103,
159,
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"passage: TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #NbAiLab/NPSC #generated_from_trainer #robust-speech-event #dataset-NbAiLab/NPSC #base_model-KBLab/wav2vec2-large-voxrex #license-cc0-1.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 2000\n* num\\_epochs: 15.0\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.17.0.dev0\n* Pytorch 1.10.2+cu113\n* Datasets 1.18.3.dev0\n* Tokenizers 0.11.0"
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null | null | transformers | {"language": ["nb-NO"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "NbAiLab/NPSC", "robust-speech-event", false, "nb-NO", "hf-asr-leaderboard"], "datasets": ["NbAiLab/NPSC"], "model-index": [{"name": "wav2vec2-xls-r-1b-npsc-bokmaal-low-27k", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "NPSC", "type": "NbAiLab/NPSC", "args": "16K_mp3_bokmaal"}, "metrics": [{"type": "wer", "value": 0.06332329423537675, "name": "Test (Bokm\u00e5l) WER"}, {"type": "cer", "value": 0.02480899861950731, "name": "Test (Bokm\u00e5l) CER"}]}]}]} | automatic-speech-recognition | NbAiLab/wav2vec2-xls-r-1b-npsc-bokmaal-low-27k | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"dataset:NbAiLab/NPSC",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"nb-NO"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #dataset-NbAiLab/NPSC #license-apache-2.0 #model-index #endpoints_compatible #region-us
| [] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #dataset-NbAiLab/NPSC #license-apache-2.0 #model-index #endpoints_compatible #region-us \n"
] | [
60
] | [
"passage: TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #dataset-NbAiLab/NPSC #license-apache-2.0 #model-index #endpoints_compatible #region-us \n"
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null | null | transformers |
<!-- 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-1b-npsc
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the [NbAiLab/NPSC (16K_mp3_bokmaal)](https://huggingface.co/datasets/NbAiLab/NPSC/viewer/16K_mp3_bokmaal/train) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1598
- WER: 0.0966
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- 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: 2000
- num_epochs: 15.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.8361 | 0.32 | 500 | 0.6304 | 0.4970 |
| 0.5703 | 0.64 | 1000 | 0.3195 | 0.2775 |
| 0.5451 | 0.97 | 1500 | 0.2700 | 0.2246 |
| 0.47 | 1.29 | 2000 | 0.2564 | 0.2329 |
| 0.4063 | 1.61 | 2500 | 0.2459 | 0.2099 |
| 0.374 | 1.93 | 3000 | 0.2175 | 0.1894 |
| 0.3297 | 2.26 | 3500 | 0.2036 | 0.1755 |
| 0.3145 | 2.58 | 4000 | 0.1957 | 0.1757 |
| 0.3989 | 2.9 | 4500 | 0.1923 | 0.1723 |
| 0.271 | 3.22 | 5000 | 0.1889 | 0.1649 |
| 0.2758 | 3.55 | 5500 | 0.1768 | 0.1588 |
| 0.2683 | 3.87 | 6000 | 0.1720 | 0.1534 |
| 0.2341 | 4.19 | 6500 | 0.1689 | 0.1471 |
| 0.2316 | 4.51 | 7000 | 0.1706 | 0.1405 |
| 0.2383 | 4.84 | 7500 | 0.1637 | 0.1426 |
| 0.2148 | 5.16 | 8000 | 0.1584 | 0.1347 |
| 0.2085 | 5.48 | 8500 | 0.1601 | 0.1387 |
| 0.2944 | 5.8 | 9000 | 0.1566 | 0.1294 |
| 0.1944 | 6.13 | 9500 | 0.1494 | 0.1271 |
| 0.1853 | 6.45 | 10000 | 0.1561 | 0.1247 |
| 0.235 | 6.77 | 10500 | 0.1461 | 0.1215 |
| 0.2286 | 7.09 | 11000 | 0.1447 | 0.1167 |
| 0.1781 | 7.41 | 11500 | 0.1502 | 0.1199 |
| 0.1714 | 7.74 | 12000 | 0.1425 | 0.1179 |
| 0.1725 | 8.06 | 12500 | 0.1427 | 0.1173 |
| 0.143 | 8.38 | 13000 | 0.1448 | 0.1142 |
| 0.154 | 8.7 | 13500 | 0.1392 | 0.1104 |
| 0.1447 | 9.03 | 14000 | 0.1404 | 0.1094 |
| 0.1471 | 9.35 | 14500 | 0.1404 | 0.1088 |
| 0.1479 | 9.67 | 15000 | 0.1414 | 0.1133 |
| 0.1607 | 9.99 | 15500 | 0.1458 | 0.1171 |
| 0.166 | 10.32 | 16000 | 0.1652 | 0.1264 |
| 0.188 | 10.64 | 16500 | 0.1713 | 0.1322 |
| 0.1461 | 10.96 | 17000 | 0.1423 | 0.1111 |
| 0.1289 | 11.28 | 17500 | 0.1388 | 0.1097 |
| 0.1273 | 11.61 | 18000 | 0.1438 | 0.1074 |
| 0.1317 | 11.93 | 18500 | 0.1312 | 0.1066 |
| 0.1448 | 12.25 | 19000 | 0.1446 | 0.1042 |
| 0.1424 | 12.57 | 19500 | 0.1386 | 0.1015 |
| 0.1392 | 12.89 | 20000 | 0.1379 | 0.1005 |
| 0.1408 | 13.22 | 20500 | 0.1408 | 0.0992 |
| 0.1239 | 13.54 | 21000 | 0.1338 | 0.0968 |
| 0.1244 | 13.86 | 21500 | 0.1335 | 0.0957 |
| 0.1254 | 14.18 | 22000 | 0.1382 | 0.0950 |
| 0.1597 | 14.51 | 22500 | 0.1544 | 0.0970 |
| 0.1566 | 14.83 | 23000 | 0.1589 | 0.0963 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu113
- Datasets 1.18.3.dev0
- Tokenizers 0.11.0
| {"language": ["nb-NO"], "license": "apache-2.0", "tags": ["generated_from_trainer", "automatic-speech-recognition", "NbAiLab/NPSC", "robust-speech-event", false, "nb-NO", "hf-asr-leaderboard"], "datasets": ["NbAiLab/NPSC"], "model-index": [{"name": "wav2vec2-xls-r-1b-npsc-bokmaal", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "NPSC", "type": "NbAiLab/NPSC", "args": "16K_mp3_bokmaal"}, "metrics": [{"type": "wer", "value": 0.07901700231893541, "name": "Test (Bokm\u00e5l) WER"}, {"type": "cer", "value": 0.029734583252347752, "name": "Test (Bokm\u00e5l) CER"}]}]}]} | automatic-speech-recognition | NbAiLab/wav2vec2-xls-r-1b-npsc-bokmaal | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"dataset:NbAiLab/NPSC",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"nb-NO"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #dataset-NbAiLab/NPSC #license-apache-2.0 #model-index #endpoints_compatible #region-us
| wav2vec2-xls-r-1b-npsc
======================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the NbAiLab/NPSC (16K\_mp3\_bokmaal) dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1598
* WER: 0.0966
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0001
* train\_batch\_size: 16
* eval\_batch\_size: 16
* 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: 2000
* num\_epochs: 15.0
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.17.0.dev0
* Pytorch 1.10.2+cu113
* Datasets 1.18.3.dev0
* Tokenizers 0.11.0
| [
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"### Training results",
"### Framework versions\n\n\n* Transformers 4.17.0.dev0\n* Pytorch 1.10.2+cu113\n* Datasets 1.18.3.dev0\n* Tokenizers 0.11.0"
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"passage: TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #dataset-NbAiLab/NPSC #license-apache-2.0 #model-index #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 2000\n* num\\_epochs: 15.0\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.17.0.dev0\n* Pytorch 1.10.2+cu113\n* Datasets 1.18.3.dev0\n* Tokenizers 0.11.0"
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null | null | transformers |
<!-- 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-npsc-bokmaal
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1663
- Wer: 0.0932
## 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
- 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: 15.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.0969 | 0.32 | 500 | 0.1773 | 0.1054 |
| 0.0929 | 0.64 | 1000 | 0.1672 | 0.1061 |
| 0.1018 | 0.97 | 1500 | 0.1770 | 0.1067 |
| 0.0871 | 1.29 | 2000 | 0.1832 | 0.1087 |
| 0.0908 | 1.61 | 2500 | 0.1830 | 0.1101 |
| 0.0975 | 1.93 | 3000 | 0.1848 | 0.1100 |
| 0.0936 | 2.26 | 3500 | 0.1853 | 0.1113 |
| 0.1025 | 2.58 | 4000 | 0.1958 | 0.1149 |
| 0.0989 | 2.9 | 4500 | 0.1776 | 0.1123 |
| 0.0946 | 3.22 | 5000 | 0.1825 | 0.1097 |
| 0.0859 | 3.55 | 5500 | 0.1864 | 0.1072 |
| 0.0867 | 3.87 | 6000 | 0.1886 | 0.1081 |
| 0.0783 | 4.19 | 6500 | 0.1883 | 0.1063 |
| 0.0804 | 4.51 | 7000 | 0.1831 | 0.1063 |
| 0.0797 | 4.84 | 7500 | 0.1884 | 0.1058 |
| 0.0705 | 5.16 | 8000 | 0.1802 | 0.1057 |
| 0.0795 | 5.48 | 8500 | 0.1854 | 0.1038 |
| 0.0711 | 5.8 | 9000 | 0.1766 | 0.1032 |
| 0.0973 | 6.13 | 9500 | 0.1663 | 0.1014 |
| 0.087 | 6.45 | 10000 | 0.1664 | 0.1014 |
| 0.0962 | 6.77 | 10500 | 0.1631 | 0.1009 |
| 0.0857 | 7.09 | 11000 | 0.1659 | 0.1002 |
| 0.0882 | 7.41 | 11500 | 0.1668 | 0.1007 |
| 0.0784 | 7.74 | 12000 | 0.1688 | 0.0996 |
| 0.0838 | 8.06 | 12500 | 0.1675 | 0.0984 |
| 0.0863 | 8.38 | 13000 | 0.1639 | 0.0979 |
| 0.0763 | 8.7 | 13500 | 0.1638 | 0.0980 |
| 0.0822 | 9.03 | 14000 | 0.1709 | 0.0972 |
| 0.0769 | 9.35 | 14500 | 0.1700 | 0.0965 |
| 0.0838 | 9.67 | 15000 | 0.1703 | 0.0974 |
| 0.0799 | 9.99 | 15500 | 0.1667 | 0.0957 |
| 0.0712 | 10.32 | 16000 | 0.1754 | 0.0960 |
| 0.0737 | 10.64 | 16500 | 0.1725 | 0.0968 |
| 0.0851 | 10.96 | 17000 | 0.1733 | 0.0958 |
| 0.076 | 11.28 | 17500 | 0.1682 | 0.0954 |
| 0.0712 | 11.61 | 18000 | 0.1713 | 0.0943 |
| 0.0745 | 11.93 | 18500 | 0.1662 | 0.0951 |
| 0.0864 | 12.25 | 19000 | 0.1692 | 0.0947 |
| 0.0937 | 12.57 | 19500 | 0.1624 | 0.0943 |
| 0.0915 | 12.89 | 20000 | 0.1678 | 0.0942 |
| 0.0926 | 13.22 | 20500 | 0.1641 | 0.0945 |
| 0.0912 | 13.54 | 21000 | 0.1665 | 0.0937 |
| 0.0917 | 13.86 | 21500 | 0.1648 | 0.0936 |
| 0.094 | 14.18 | 22000 | 0.1635 | 0.0935 |
| 0.0864 | 14.51 | 22500 | 0.1678 | 0.0934 |
| 0.0899 | 14.83 | 23000 | 0.1663 | 0.0932 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu113
- Datasets 1.18.4.dev0
- Tokenizers 0.11.0
| {"language": ["nb-NO"], "license": "apache-2.0", "tags": ["generated_from_trainer", "automatic-speech-recognition", "NbAiLab/NPSC", "robust-speech-event", false, "nb-NO", "hf-asr-leaderboard"], "datasets": ["NbAiLab/NPSC"], "model-index": [{"name": "wav2vec2-xls-r-300m-npsc-bokmaal", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "NPSC", "type": "NbAiLab/NPSC", "args": "16K_mp3_bokmaal"}, "metrics": [{"type": "wer", "value": 0.07556265455560153, "name": "Test (Bokm\u00e5l) WER"}, {"type": "cer", "value": 0.028191288775481386, "name": "Test (Bokm\u00e5l) CER"}]}]}]} | automatic-speech-recognition | NbAiLab/wav2vec2-xls-r-300m-npsc-bokmaal | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"dataset:NbAiLab/NPSC",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"nb-NO"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #dataset-NbAiLab/NPSC #license-apache-2.0 #model-index #endpoints_compatible #region-us
| wav2vec2-xls-r-300m-npsc-bokmaal
================================
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1663
* Wer: 0.0932
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
* 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: 15.0
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.17.0.dev0
* Pytorch 1.10.2+cu113
* Datasets 1.18.4.dev0
* Tokenizers 0.11.0
| [
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"### Training results",
"### Framework versions\n\n\n* Transformers 4.17.0.dev0\n* Pytorch 1.10.2+cu113\n* Datasets 1.18.4.dev0\n* Tokenizers 0.11.0"
] | [
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"passage: TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #dataset-NbAiLab/NPSC #license-apache-2.0 #model-index #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 15.0\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.17.0.dev0\n* Pytorch 1.10.2+cu113\n* Datasets 1.18.4.dev0\n* Tokenizers 0.11.0"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-xlsr-1B-NPSC-NN
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the NBAILAB/NPSC - 16K_MP3 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4562
- Wer: 0.1531
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 6e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 50.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 1.6894 | 1.08 | 500 | 1.2423 | 0.8619 |
| 0.7543 | 2.15 | 1000 | 0.5956 | 0.3817 |
| 0.5481 | 3.23 | 1500 | 0.5043 | 0.3246 |
| 0.4661 | 4.3 | 2000 | 0.4813 | 0.2793 |
| 0.3901 | 5.38 | 2500 | 0.4371 | 0.2592 |
| 0.3512 | 6.45 | 3000 | 0.4216 | 0.2458 |
| 0.3016 | 7.53 | 3500 | 0.3814 | 0.2257 |
| 0.278 | 8.6 | 4000 | 0.4151 | 0.2145 |
| 0.2435 | 9.68 | 4500 | 0.4816 | 0.2130 |
| 0.2122 | 10.75 | 5000 | 0.4489 | 0.2137 |
| 0.1949 | 11.83 | 5500 | 0.3978 | 0.2063 |
| 0.1929 | 12.9 | 6000 | 0.3823 | 0.2026 |
| 0.1757 | 13.98 | 6500 | 0.3409 | 0.1965 |
| 0.1771 | 15.05 | 7000 | 0.3844 | 0.1936 |
| 0.1452 | 16.13 | 7500 | 0.3749 | 0.1900 |
| 0.1341 | 17.2 | 8000 | 0.4407 | 0.2026 |
| 0.13 | 18.28 | 8500 | 0.4253 | 0.1883 |
| 0.1183 | 19.35 | 9000 | 0.4311 | 0.1880 |
| 0.118 | 20.43 | 9500 | 0.4431 | 0.1882 |
| 0.1123 | 21.51 | 10000 | 0.4753 | 0.1820 |
| 0.1037 | 22.58 | 10500 | 0.4087 | 0.1834 |
| 0.1066 | 23.66 | 11000 | 0.4151 | 0.1845 |
| 0.0977 | 24.73 | 11500 | 0.4367 | 0.1783 |
| 0.0968 | 25.81 | 12000 | 0.4237 | 0.1756 |
| 0.0835 | 26.88 | 12500 | 0.4729 | 0.1781 |
| 0.0919 | 27.96 | 13000 | 0.4153 | 0.1701 |
| 0.0677 | 29.03 | 13500 | 0.4317 | 0.1693 |
| 0.0726 | 30.11 | 14000 | 0.4380 | 0.1736 |
| 0.066 | 31.18 | 14500 | 0.4384 | 0.1681 |
| 0.0713 | 32.26 | 15000 | 0.4215 | 0.1629 |
| 0.0605 | 33.33 | 15500 | 0.4574 | 0.1714 |
| 0.0632 | 34.41 | 16000 | 0.4343 | 0.1642 |
| 0.0567 | 35.48 | 16500 | 0.4231 | 0.1601 |
| 0.0556 | 36.56 | 17000 | 0.4404 | 0.1667 |
| 0.0426 | 37.63 | 17500 | 0.4459 | 0.1625 |
| 0.0445 | 38.71 | 18000 | 0.4484 | 0.1629 |
| 0.0463 | 39.78 | 18500 | 0.4508 | 0.1596 |
| 0.0448 | 40.86 | 19000 | 0.4395 | 0.1605 |
| 0.0434 | 41.94 | 19500 | 0.4490 | 0.1607 |
| 0.0347 | 43.01 | 20000 | 0.4772 | 0.1582 |
| 0.0332 | 44.09 | 20500 | 0.4729 | 0.1582 |
| 0.037 | 45.16 | 21000 | 0.4559 | 0.1573 |
| 0.0328 | 46.24 | 21500 | 0.4664 | 0.1560 |
| 0.0366 | 47.31 | 22000 | 0.4543 | 0.1543 |
| 0.0377 | 48.39 | 22500 | 0.4507 | 0.1560 |
| 0.0331 | 49.46 | 23000 | 0.4567 | 0.1533 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0
| {"language": ["nn-NO"], "license": "apache-2.0", "tags": ["generated_from_trainer", "automatic-speech-recognition", "NbAiLab/NPSC", "robust-speech-event", false, "nn-NO", "hf-asr-leaderboard"], "datasets": ["NbAiLab/NPSC"], "model-index": [{"name": "wav2vec2-xlsr-1B-NPSC-NN", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "NPSC", "type": "NbAiLab/NPSC", "args": "16K_mp3_nynorsk"}, "metrics": [{"type": "wer", "value": 0.13347099680871036, "name": "Test (Nynorsk) WER"}, {"type": "cer", "value": 0.04537322093454329, "name": "Test (Nynorsk) CER"}]}]}]} | automatic-speech-recognition | NbAiLab/wav2vec2-xlsr-1B-NPSC-NN | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"dataset:NbAiLab/NPSC",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"nn-NO"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #dataset-NbAiLab/NPSC #license-apache-2.0 #model-index #endpoints_compatible #region-us
| wav2vec2-xlsr-1B-NPSC-NN
========================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the NBAILAB/NPSC - 16K\_MP3 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4562
* Wer: 0.1531
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 6e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 2000
* num\_epochs: 50.0
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.17.0.dev0
* Pytorch 1.10.1+cu102
* Datasets 1.18.2.dev0
* Tokenizers 0.11.0
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 2000\n* num\\_epochs: 50.0\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.17.0.dev0\n* Pytorch 1.10.1+cu102\n* Datasets 1.18.2.dev0\n* Tokenizers 0.11.0"
] | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #dataset-NbAiLab/NPSC #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 2000\n* num\\_epochs: 50.0\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.17.0.dev0\n* Pytorch 1.10.1+cu102\n* Datasets 1.18.2.dev0\n* Tokenizers 0.11.0"
] | [
64,
160,
4,
39
] | [
"passage: TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #dataset-NbAiLab/NPSC #license-apache-2.0 #model-index #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 2000\n* num\\_epochs: 50.0\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.17.0.dev0\n* Pytorch 1.10.1+cu102\n* Datasets 1.18.2.dev0\n* Tokenizers 0.11.0"
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null | null | transformers |
# XLS-R-300M-LM - Norwegian
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the Norwegian [NPSC](https://huggingface.co/datasets/NbAiLab/NPSC) dataset.
### Scores without Language Model
Without using a language model, it achieves the following scores on the NPSC Eval set
It achieves the following results on the evaluation set without a language model:
- WER: 0.2110
- CER: 0.0622
### Scores with Language Model
A 5-gram KenLM was added to boost the models performance. The language model was created on a corpus mainly consisting of online newspapers, public reports and Wikipedia data. After this we are getting these values.
- WER: 0.1540
- CER: 0.0548
## Team
The model is developed by Rolv-Arild Braaten, Per Egil Kummervold, Andre Kåsen, Javier de la Rosa, Per Erik Solberg, and Freddy Wetjen. Name in alphabetic order.
## Model description
This current version is based on checkpoint 8500 of [NbAiLab/wav2vec2-xlsr-300M-NPSC-OH](https://huggingface.co/NbAiLab/wav2vec2-xlsr-300M-NPSC-OH).
## Intended uses & limitations
Demo version only. The model will be updated later this week.
## Training and evaluation data
The model is trained and evaluated on [NPSC](https://huggingface.co/datasets/NbAiLab/NPSC). Unfortunately there is no Norwegian test data in Common Voice, and currently the model is only evaluated on the validation set of NPSC..
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7.5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- 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: 2000
- num_epochs: 30.0 (But interrupted after 8500 steps, approx 6 epochs)
- mixed_precision_training: Native AMP
| {"language": ["nb-NO"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "generated_from_trainer", false, "nb-NO", "robust-speech-event", "model_for_talk", "hf-asr-leaderboard"], "datasets": ["NbAiLab/NPSC"], "model-index": [{"name": "XLS-R-300M-LM - Norwegian", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "NPSC", "type": "NbAiLab/NPSC"}, "metrics": [{"type": "wer", "value": 15.4, "name": "Eval WER"}, {"type": "cer", "value": 5.48, "name": "Eval CER"}]}]}]} | automatic-speech-recognition | NbAiLab/wav2vec2-xlsr-300M-NPSC-LM | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"dataset:NbAiLab/NPSC",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"nb-NO"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #dataset-NbAiLab/NPSC #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# XLS-R-300M-LM - Norwegian
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the Norwegian NPSC dataset.
### Scores without Language Model
Without using a language model, it achieves the following scores on the NPSC Eval set
It achieves the following results on the evaluation set without a language model:
- WER: 0.2110
- CER: 0.0622
### Scores with Language Model
A 5-gram KenLM was added to boost the models performance. The language model was created on a corpus mainly consisting of online newspapers, public reports and Wikipedia data. After this we are getting these values.
- WER: 0.1540
- CER: 0.0548
## Team
The model is developed by Rolv-Arild Braaten, Per Egil Kummervold, Andre Kåsen, Javier de la Rosa, Per Erik Solberg, and Freddy Wetjen. Name in alphabetic order.
## Model description
This current version is based on checkpoint 8500 of NbAiLab/wav2vec2-xlsr-300M-NPSC-OH.
## Intended uses & limitations
Demo version only. The model will be updated later this week.
## Training and evaluation data
The model is trained and evaluated on NPSC. Unfortunately there is no Norwegian test data in Common Voice, and currently the model is only evaluated on the validation set of NPSC..
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7.5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- 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: 2000
- num_epochs: 30.0 (But interrupted after 8500 steps, approx 6 epochs)
- mixed_precision_training: Native AMP
| [
"# XLS-R-300M-LM - Norwegian\r\n\r\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the Norwegian NPSC dataset.",
"### Scores without Language Model\r\nWithout using a language model, it achieves the following scores on the NPSC Eval set\r\nIt achieves the following results on the evaluation set without a language model:\r\n- WER: 0.2110\r\n- CER: 0.0622",
"### Scores with Language Model\r\nA 5-gram KenLM was added to boost the models performance. The language model was created on a corpus mainly consisting of online newspapers, public reports and Wikipedia data. After this we are getting these values.\r\n\r\n- WER: 0.1540\r\n- CER: 0.0548",
"## Team\r\nThe model is developed by Rolv-Arild Braaten, Per Egil Kummervold, Andre Kåsen, Javier de la Rosa, Per Erik Solberg, and Freddy Wetjen. Name in alphabetic order.",
"## Model description\r\nThis current version is based on checkpoint 8500 of NbAiLab/wav2vec2-xlsr-300M-NPSC-OH.",
"## Intended uses & limitations\r\nDemo version only. The model will be updated later this week.",
"## Training and evaluation data\r\nThe model is trained and evaluated on NPSC. Unfortunately there is no Norwegian test data in Common Voice, and currently the model is only evaluated on the validation set of NPSC..",
"## Training procedure",
"### Training hyperparameters\r\nThe following hyperparameters were used during training:\r\n- learning_rate: 7.5e-05\r\n- train_batch_size: 8\r\n- eval_batch_size: 8\r\n- seed: 42\r\n- gradient_accumulation_steps: 4\r\n- total_train_batch_size: 32\r\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\r\n- lr_scheduler_type: linear\r\n- lr_scheduler_warmup_steps: 2000\r\n- num_epochs: 30.0 (But interrupted after 8500 steps, approx 6 epochs)\r\n- mixed_precision_training: Native AMP"
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #dataset-NbAiLab/NPSC #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# XLS-R-300M-LM - Norwegian\r\n\r\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the Norwegian NPSC dataset.",
"### Scores without Language Model\r\nWithout using a language model, it achieves the following scores on the NPSC Eval set\r\nIt achieves the following results on the evaluation set without a language model:\r\n- WER: 0.2110\r\n- CER: 0.0622",
"### Scores with Language Model\r\nA 5-gram KenLM was added to boost the models performance. The language model was created on a corpus mainly consisting of online newspapers, public reports and Wikipedia data. After this we are getting these values.\r\n\r\n- WER: 0.1540\r\n- CER: 0.0548",
"## Team\r\nThe model is developed by Rolv-Arild Braaten, Per Egil Kummervold, Andre Kåsen, Javier de la Rosa, Per Erik Solberg, and Freddy Wetjen. Name in alphabetic order.",
"## Model description\r\nThis current version is based on checkpoint 8500 of NbAiLab/wav2vec2-xlsr-300M-NPSC-OH.",
"## Intended uses & limitations\r\nDemo version only. The model will be updated later this week.",
"## Training and evaluation data\r\nThe model is trained and evaluated on NPSC. Unfortunately there is no Norwegian test data in Common Voice, and currently the model is only evaluated on the validation set of NPSC..",
"## Training procedure",
"### Training hyperparameters\r\nThe following hyperparameters were used during training:\r\n- learning_rate: 7.5e-05\r\n- train_batch_size: 8\r\n- eval_batch_size: 8\r\n- seed: 42\r\n- gradient_accumulation_steps: 4\r\n- total_train_batch_size: 32\r\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\r\n- lr_scheduler_type: linear\r\n- lr_scheduler_warmup_steps: 2000\r\n- num_epochs: 30.0 (But interrupted after 8500 steps, approx 6 epochs)\r\n- mixed_precision_training: Native AMP"
] | [
60,
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"passage: TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #dataset-NbAiLab/NPSC #license-apache-2.0 #model-index #endpoints_compatible #region-us \n# XLS-R-300M-LM - Norwegian\r\n\r\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the Norwegian NPSC dataset.### Scores without Language Model\r\nWithout using a language model, it achieves the following scores on the NPSC Eval set\r\nIt achieves the following results on the evaluation set without a language model:\r\n- WER: 0.2110\r\n- CER: 0.0622### Scores with Language Model\r\nA 5-gram KenLM was added to boost the models performance. The language model was created on a corpus mainly consisting of online newspapers, public reports and Wikipedia data. After this we are getting these values.\r\n\r\n- WER: 0.1540\r\n- CER: 0.0548## Team\r\nThe model is developed by Rolv-Arild Braaten, Per Egil Kummervold, Andre Kåsen, Javier de la Rosa, Per Erik Solberg, and Freddy Wetjen. Name in alphabetic order.## Model description\r\nThis current version is based on checkpoint 8500 of NbAiLab/wav2vec2-xlsr-300M-NPSC-OH.## Intended uses & limitations\r\nDemo version only. The model will be updated later this week.## Training and evaluation data\r\nThe model is trained and evaluated on NPSC. Unfortunately there is no Norwegian test data in Common Voice, and currently the model is only evaluated on the validation set of NPSC..## Training procedure"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-xlsr-300M-NPSC-OH
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the NBAILAB/NPSC - 16K_MP3 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1692
- Wer: 0.1663
## 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: 7.5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 13
- 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
- lr_scheduler_warmup_steps: 2000
- num_epochs: 15.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 3.1638 | 0.66 | 500 | 3.0686 | 1.0 |
| 2.9311 | 1.31 | 1000 | 2.9208 | 1.0 |
| 2.4175 | 1.97 | 1500 | 1.5009 | 0.9049 |
| 1.4442 | 2.63 | 2000 | 0.4426 | 0.3783 |
| 1.2624 | 3.28 | 2500 | 0.3193 | 0.2998 |
| 1.1889 | 3.94 | 3000 | 0.2867 | 0.2630 |
| 1.1315 | 4.6 | 3500 | 0.2566 | 0.2444 |
| 1.0864 | 5.26 | 4000 | 0.2368 | 0.2294 |
| 1.093 | 5.91 | 4500 | 0.2240 | 0.2151 |
| 1.0368 | 6.57 | 5000 | 0.2117 | 0.2056 |
| 1.0178 | 7.23 | 5500 | 0.2020 | 0.1954 |
| 1.0035 | 7.88 | 6000 | 0.2005 | 0.1924 |
| 0.9759 | 8.54 | 6500 | 0.1971 | 0.1863 |
| 0.9795 | 9.2 | 7000 | 0.1892 | 0.1812 |
| 0.9601 | 9.85 | 7500 | 0.1863 | 0.1795 |
| 0.9673 | 10.51 | 8000 | 0.1809 | 0.1761 |
| 0.9233 | 11.17 | 8500 | 0.1818 | 0.1755 |
| 0.9382 | 11.83 | 9000 | 0.1767 | 0.1741 |
| 0.9242 | 12.48 | 9500 | 0.1743 | 0.1703 |
| 0.9703 | 13.14 | 10000 | 0.1711 | 0.1711 |
| 0.9139 | 13.8 | 10500 | 0.1718 | 0.1672 |
| 0.9073 | 14.45 | 11000 | 0.1700 | 0.1665 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0
| {"license": "apache-2.0", "tags": ["automatic-speech-recognition", "NbAiLab/NPSC", "generated_from_trainer"], "model-index": [{"name": "wav2vec2-xlsr-300M-NPSC-OH", "results": []}]} | automatic-speech-recognition | NbAiLab/wav2vec2-xlsr-300M-NPSC-OH | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"NbAiLab/NPSC",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #NbAiLab/NPSC #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-xlsr-300M-NPSC-OH
==========================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the NBAILAB/NPSC - 16K\_MP3 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1692
* Wer: 0.1663
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: 7.5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 13
* 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
* lr\_scheduler\_warmup\_steps: 2000
* num\_epochs: 15.0
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.17.0.dev0
* Pytorch 1.10.1+cu102
* Datasets 1.18.2.dev0
* Tokenizers 0.11.0
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 13\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 2000\n* num\\_epochs: 15.0\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.17.0.dev0\n* Pytorch 1.10.1+cu102\n* Datasets 1.18.2.dev0\n* Tokenizers 0.11.0"
] | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #NbAiLab/NPSC #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 13\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 2000\n* num\\_epochs: 15.0\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.17.0.dev0\n* Pytorch 1.10.1+cu102\n* Datasets 1.18.2.dev0\n* Tokenizers 0.11.0"
] | [
64,
160,
4,
39
] | [
"passage: TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #NbAiLab/NPSC #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 13\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 2000\n* num\\_epochs: 15.0\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.17.0.dev0\n* Pytorch 1.10.1+cu102\n* Datasets 1.18.2.dev0\n* Tokenizers 0.11.0"
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null | null | transformers |
<!-- 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-npsc-oh
This model is a fine-tuned version of [KBLab/wav2vec2-large-voxrex](https://huggingface.co/KBLab/wav2vec2-large-voxrex) on the NBAILAB/NPSC - 48K_MP3 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2106
- Wer: 0.8586
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 5.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 2.1093 | 2.61 | 1000 | 0.2572 | 0.9293 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.18.1.dev0
- Tokenizers 0.11.0
| {"license": "cc0-1.0", "tags": ["automatic-speech-recognition", "NbAiLab/NPSC", "generated_from_trainer"], "datasets": ["npsc"], "model-index": [{"name": "xls-npsc-oh", "results": []}]} | automatic-speech-recognition | NbAiLab/xls-npsc-oh | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"NbAiLab/NPSC",
"generated_from_trainer",
"dataset:npsc",
"license:cc0-1.0",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #NbAiLab/NPSC #generated_from_trainer #dataset-npsc #license-cc0-1.0 #endpoints_compatible #region-us
| xls-npsc-oh
===========
This model is a fine-tuned version of KBLab/wav2vec2-large-voxrex on the NBAILAB/NPSC - 48K\_MP3 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2106
* Wer: 0.8586
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 5e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 5.0
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.16.0.dev0
* Pytorch 1.10.1+cu102
* Datasets 1.18.1.dev0
* Tokenizers 0.11.0
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 5.0\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.16.0.dev0\n* Pytorch 1.10.1+cu102\n* Datasets 1.18.1.dev0\n* Tokenizers 0.11.0"
] | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #NbAiLab/NPSC #generated_from_trainer #dataset-npsc #license-cc0-1.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 5.0\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.16.0.dev0\n* Pytorch 1.10.1+cu102\n* Datasets 1.18.1.dev0\n* Tokenizers 0.11.0"
] | [
70,
159,
4,
41
] | [
"passage: TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #NbAiLab/NPSC #generated_from_trainer #dataset-npsc #license-cc0-1.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 5.0\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.16.0.dev0\n* Pytorch 1.10.1+cu102\n* Datasets 1.18.1.dev0\n* Tokenizers 0.11.0"
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null | null | transformers |
<!-- 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-npsc
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the NBAILAB/NPSC - 48K_MP3 dataset.
It achieves the following results on the evaluation set:
- Loss: 3.5006
- 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: 7.5e-05
- train_batch_size: 16
- eval_batch_size: 16
- 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
- lr_scheduler_warmup_steps: 500
- num_epochs: 10.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.18.1.dev0
- Tokenizers 0.10.3
| {"license": "apache-2.0", "tags": ["automatic-speech-recognition", "NbAiLab/NPSC", "generated_from_trainer"], "datasets": ["npsc"], "model-index": [{"name": "xls-npsc", "results": []}]} | automatic-speech-recognition | NbAiLab/xls-npsc | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"NbAiLab/NPSC",
"generated_from_trainer",
"dataset:npsc",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #NbAiLab/NPSC #generated_from_trainer #dataset-npsc #license-apache-2.0 #endpoints_compatible #region-us
|
# xls-npsc
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the NBAILAB/NPSC - 48K_MP3 dataset.
It achieves the following results on the evaluation set:
- Loss: 3.5006
- 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: 7.5e-05
- train_batch_size: 16
- eval_batch_size: 16
- 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
- lr_scheduler_warmup_steps: 500
- num_epochs: 10.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.18.1.dev0
- Tokenizers 0.10.3
| [
"# xls-npsc\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the NBAILAB/NPSC - 48K_MP3 dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 3.5006\n- Wer: 1.0",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 7.5e-05\n- train_batch_size: 16\n- eval_batch_size: 16\n- seed: 42\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 64\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 500\n- num_epochs: 10.0\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- Transformers 4.16.0.dev0\n- Pytorch 1.10.1+cu102\n- Datasets 1.18.1.dev0\n- Tokenizers 0.10.3"
] | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #NbAiLab/NPSC #generated_from_trainer #dataset-npsc #license-apache-2.0 #endpoints_compatible #region-us \n",
"# xls-npsc\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the NBAILAB/NPSC - 48K_MP3 dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 3.5006\n- Wer: 1.0",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 7.5e-05\n- train_batch_size: 16\n- eval_batch_size: 16\n- seed: 42\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 64\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 500\n- num_epochs: 10.0\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- Transformers 4.16.0.dev0\n- Pytorch 1.10.1+cu102\n- Datasets 1.18.1.dev0\n- Tokenizers 0.10.3"
] | [
70,
67,
6,
12,
8,
3,
142,
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41
] | [
"passage: TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #NbAiLab/NPSC #generated_from_trainer #dataset-npsc #license-apache-2.0 #endpoints_compatible #region-us \n# xls-npsc\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the NBAILAB/NPSC - 48K_MP3 dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 3.5006\n- Wer: 1.0## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 7.5e-05\n- train_batch_size: 16\n- eval_batch_size: 16\n- seed: 42\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 64\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 500\n- num_epochs: 10.0\n- mixed_precision_training: Native AMP### Training results### Framework versions\n\n- Transformers 4.16.0.dev0\n- Pytorch 1.10.1+cu102\n- Datasets 1.18.1.dev0\n- Tokenizers 0.10.3"
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null | null | transformers | # Harry potter | {"tags": ["conversational"]} | text-generation | Necrozma/harrypotterbot | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # Harry potter | [
"# Harry potter"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Harry potter"
] | [
51,
4
] | [
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Harry potter"
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null | null | transformers |
not for use...
technical data | {"language": ["ru"], "widget": [{"text": "\u0421\u043c\u0435\u0440\u0442\u0438 \u043d\u0435\u0442, "}]} | text-generation | Nehc/adpatres | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"ru",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"ru"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #ru #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
not for use...
technical data | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #ru #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
49
] | [
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #ru #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
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] |
null | null | transformers |
Start from sberbank-ai/rugpt3small_based_on_gpt2 and finetuning on Govard Phillips Lovecraft texts (russian).
On this moment - only 1 epoch (perplexity falls reasons)
on progress...
| {"language": ["ru"], "metrics": [{"loss": 3.3}, {"perplexity": 25.7528}], "widget": [{"text": "\u041d\u0435\u043c\u044b\u0441\u043b\u0438\u043c\u043e, "}]} | text-generation | Nehc/gpt2_lovecraft_ru | [
"transformers",
"pytorch",
"safetensors",
"gpt2",
"text-generation",
"ru",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"ru"
] | TAGS
#transformers #pytorch #safetensors #gpt2 #text-generation #ru #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
Start from sberbank-ai/rugpt3small_based_on_gpt2 and finetuning on Govard Phillips Lovecraft texts (russian).
On this moment - only 1 epoch (perplexity falls reasons)
on progress...
| [] | [
"TAGS\n#transformers #pytorch #safetensors #gpt2 #text-generation #ru #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
54
] | [
"passage: TAGS\n#transformers #pytorch #safetensors #gpt2 #text-generation #ru #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
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null | null | transformers |
Start from sberbank-ai/rugpt3small_based_on_gpt2 and finetuning on Biblie & preaching (russian).
On this moment - only 1 epoch, 1650 seq length
on progress... | {"language": ["ru"], "metrics": [{"loss": 3.3}, {"perplexity": 25.7528}], "widget": [{"text": "\u0411\u043e\u0433, \u044d\u0442\u043e "}]} | text-generation | Nehc/gpt2_priest_ru | [
"transformers",
"pytorch",
"safetensors",
"gpt2",
"text-generation",
"ru",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"ru"
] | TAGS
#transformers #pytorch #safetensors #gpt2 #text-generation #ru #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
Start from sberbank-ai/rugpt3small_based_on_gpt2 and finetuning on Biblie & preaching (russian).
On this moment - only 1 epoch, 1650 seq length
on progress... | [] | [
"TAGS\n#transformers #pytorch #safetensors #gpt2 #text-generation #ru #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
54
] | [
"passage: TAGS\n#transformers #pytorch #safetensors #gpt2 #text-generation #ru #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
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null | null | transformers | #zhongli DialoGTP Model | {"tags": ["conversational"]} | text-generation | Nekoism/Zhongli-Beta | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| #zhongli DialoGTP Model | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
51
] | [
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
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null | null | transformers |
# sea_mammals
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### blue whale
#### dolphin
#### orca whale
 | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | image-classification | Neto71/sea_mammals | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# sea_mammals
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images
#### blue whale
!blue whale
#### dolphin
!dolphin
#### orca whale
!orca whale | [
"# sea_mammals\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### blue whale\n\n!blue whale",
"#### dolphin\n\n!dolphin",
"#### orca whale\n\n!orca whale"
] | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# sea_mammals\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### blue whale\n\n!blue whale",
"#### dolphin\n\n!dolphin",
"#### orca whale\n\n!orca whale"
] | [
49,
43,
4,
9,
9,
11
] | [
"passage: TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n# sea_mammals\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.## Example Images#### blue whale\n\n!blue whale#### dolphin\n\n!dolphin#### orca whale\n\n!orca whale"
] | [
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] |
null | null | transformers | # BERT-Small CORD-19 fine-tuned on SQuAD 2.0
[bert-small-cord19 model](https://huggingface.co/NeuML/bert-small-cord19) fine-tuned on SQuAD 2.0
## Building the model
```bash
python run_squad.py
--model_type bert
--model_name_or_path bert-small-cord19
--do_train
--do_eval
--do_lower_case
--version_2_with_negative
--train_file train-v2.0.json
--predict_file dev-v2.0.json
--per_gpu_train_batch_size 8
--learning_rate 3e-5
--num_train_epochs 3.0
--max_seq_length 384
--doc_stride 128
--output_dir bert-small-cord19-squad2
--save_steps 0
--threads 8
--overwrite_cache
--overwrite_output_dir
| {} | question-answering | NeuML/bert-small-cord19-squad2 | [
"transformers",
"pytorch",
"jax",
"safetensors",
"bert",
"question-answering",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #safetensors #bert #question-answering #endpoints_compatible #has_space #region-us
| # BERT-Small CORD-19 fine-tuned on SQuAD 2.0
bert-small-cord19 model fine-tuned on SQuAD 2.0
## Building the model
'''bash
python run_squad.py
--model_type bert
--model_name_or_path bert-small-cord19
--do_train
--do_eval
--do_lower_case
--version_2_with_negative
--train_file train-v2.0.json
--predict_file dev-v2.0.json
--per_gpu_train_batch_size 8
--learning_rate 3e-5
--num_train_epochs 3.0
--max_seq_length 384
--doc_stride 128
--output_dir bert-small-cord19-squad2
--save_steps 0
--threads 8
--overwrite_cache
--overwrite_output_dir
| [
"# BERT-Small CORD-19 fine-tuned on SQuAD 2.0\n\nbert-small-cord19 model fine-tuned on SQuAD 2.0",
"## Building the model\n\n'''bash\npython run_squad.py\n --model_type bert\n --model_name_or_path bert-small-cord19\n --do_train\n --do_eval\n --do_lower_case\n --version_2_with_negative\n --train_file train-v2.0.json\n --predict_file dev-v2.0.json\n --per_gpu_train_batch_size 8\n --learning_rate 3e-5\n --num_train_epochs 3.0\n --max_seq_length 384\n --doc_stride 128\n --output_dir bert-small-cord19-squad2\n --save_steps 0\n --threads 8\n --overwrite_cache\n --overwrite_output_dir"
] | [
"TAGS\n#transformers #pytorch #jax #safetensors #bert #question-answering #endpoints_compatible #has_space #region-us \n",
"# BERT-Small CORD-19 fine-tuned on SQuAD 2.0\n\nbert-small-cord19 model fine-tuned on SQuAD 2.0",
"## Building the model\n\n'''bash\npython run_squad.py\n --model_type bert\n --model_name_or_path bert-small-cord19\n --do_train\n --do_eval\n --do_lower_case\n --version_2_with_negative\n --train_file train-v2.0.json\n --predict_file dev-v2.0.json\n --per_gpu_train_batch_size 8\n --learning_rate 3e-5\n --num_train_epochs 3.0\n --max_seq_length 384\n --doc_stride 128\n --output_dir bert-small-cord19-squad2\n --save_steps 0\n --threads 8\n --overwrite_cache\n --overwrite_output_dir"
] | [
41,
36,
178
] | [
"passage: TAGS\n#transformers #pytorch #jax #safetensors #bert #question-answering #endpoints_compatible #has_space #region-us \n# BERT-Small CORD-19 fine-tuned on SQuAD 2.0\n\nbert-small-cord19 model fine-tuned on SQuAD 2.0## Building the model\n\n'''bash\npython run_squad.py\n --model_type bert\n --model_name_or_path bert-small-cord19\n --do_train\n --do_eval\n --do_lower_case\n --version_2_with_negative\n --train_file train-v2.0.json\n --predict_file dev-v2.0.json\n --per_gpu_train_batch_size 8\n --learning_rate 3e-5\n --num_train_epochs 3.0\n --max_seq_length 384\n --doc_stride 128\n --output_dir bert-small-cord19-squad2\n --save_steps 0\n --threads 8\n --overwrite_cache\n --overwrite_output_dir"
] | [
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] |
null | null | transformers | # BERT-Small fine-tuned on CORD-19 dataset
[BERT L6_H-512_A-8 model](https://huggingface.co/google/bert_uncased_L-6_H-512_A-8) fine-tuned on the [CORD-19 dataset](https://www.semanticscholar.org/cord19).
## CORD-19 data subset
The training data for this dataset is stored as a [Kaggle dataset](https://www.kaggle.com/davidmezzetti/cord19-qa?select=cord19.txt). The training
data is a subset of the full corpus, focusing on high-quality, study-design detected articles.
## Building the model
```bash
python run_language_modeling.py
--model_type bert
--model_name_or_path google/bert_uncased_L-6_H-512_A-8
--do_train
--mlm
--line_by_line
--block_size 512
--train_data_file cord19.txt
--per_gpu_train_batch_size 4
--learning_rate 3e-5
--num_train_epochs 3.0
--output_dir bert-small-cord19
--save_steps 0
--overwrite_output_dir
| {} | fill-mask | NeuML/bert-small-cord19 | [
"transformers",
"pytorch",
"jax",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| # BERT-Small fine-tuned on CORD-19 dataset
BERT L6_H-512_A-8 model fine-tuned on the CORD-19 dataset.
## CORD-19 data subset
The training data for this dataset is stored as a Kaggle dataset. The training
data is a subset of the full corpus, focusing on high-quality, study-design detected articles.
## Building the model
'''bash
python run_language_modeling.py
--model_type bert
--model_name_or_path google/bert_uncased_L-6_H-512_A-8
--do_train
--mlm
--line_by_line
--block_size 512
--train_data_file URL
--per_gpu_train_batch_size 4
--learning_rate 3e-5
--num_train_epochs 3.0
--output_dir bert-small-cord19
--save_steps 0
--overwrite_output_dir
| [
"# BERT-Small fine-tuned on CORD-19 dataset\n\nBERT L6_H-512_A-8 model fine-tuned on the CORD-19 dataset.",
"## CORD-19 data subset\nThe training data for this dataset is stored as a Kaggle dataset. The training\ndata is a subset of the full corpus, focusing on high-quality, study-design detected articles.",
"## Building the model\n\n'''bash\npython run_language_modeling.py\n --model_type bert\n --model_name_or_path google/bert_uncased_L-6_H-512_A-8\n --do_train\n --mlm\n --line_by_line\n --block_size 512\n --train_data_file URL\n --per_gpu_train_batch_size 4\n --learning_rate 3e-5\n --num_train_epochs 3.0\n --output_dir bert-small-cord19\n --save_steps 0\n --overwrite_output_dir"
] | [
"TAGS\n#transformers #pytorch #jax #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"# BERT-Small fine-tuned on CORD-19 dataset\n\nBERT L6_H-512_A-8 model fine-tuned on the CORD-19 dataset.",
"## CORD-19 data subset\nThe training data for this dataset is stored as a Kaggle dataset. The training\ndata is a subset of the full corpus, focusing on high-quality, study-design detected articles.",
"## Building the model\n\n'''bash\npython run_language_modeling.py\n --model_type bert\n --model_name_or_path google/bert_uncased_L-6_H-512_A-8\n --do_train\n --mlm\n --line_by_line\n --block_size 512\n --train_data_file URL\n --per_gpu_train_batch_size 4\n --learning_rate 3e-5\n --num_train_epochs 3.0\n --output_dir bert-small-cord19\n --save_steps 0\n --overwrite_output_dir"
] | [
39,
40,
50,
134
] | [
"passage: TAGS\n#transformers #pytorch #jax #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n# BERT-Small fine-tuned on CORD-19 dataset\n\nBERT L6_H-512_A-8 model fine-tuned on the CORD-19 dataset.## CORD-19 data subset\nThe training data for this dataset is stored as a Kaggle dataset. The training\ndata is a subset of the full corpus, focusing on high-quality, study-design detected articles.## Building the model\n\n'''bash\npython run_language_modeling.py\n --model_type bert\n --model_name_or_path google/bert_uncased_L-6_H-512_A-8\n --do_train\n --mlm\n --line_by_line\n --block_size 512\n --train_data_file URL\n --per_gpu_train_batch_size 4\n --learning_rate 3e-5\n --num_train_epochs 3.0\n --output_dir bert-small-cord19\n --save_steps 0\n --overwrite_output_dir"
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null | null | transformers | # BERT-Small fine-tuned on CORD-19 QA dataset
[bert-small-cord19-squad model](https://huggingface.co/NeuML/bert-small-cord19-squad2) fine-tuned on the [CORD-19 QA dataset](https://www.kaggle.com/davidmezzetti/cord19-qa?select=cord19-qa.json).
## CORD-19 QA dataset
The CORD-19 QA dataset is a SQuAD 2.0 formatted list of question, context, answer combinations covering the [CORD-19 dataset](https://www.semanticscholar.org/cord19).
## Building the model
```bash
python run_squad.py \
--model_type bert \
--model_name_or_path bert-small-cord19-squad \
--do_train \
--do_lower_case \
--version_2_with_negative \
--train_file cord19-qa.json \
--per_gpu_train_batch_size 8 \
--learning_rate 5e-5 \
--num_train_epochs 10.0 \
--max_seq_length 384 \
--doc_stride 128 \
--output_dir bert-small-cord19qa \
--save_steps 0 \
--threads 8 \
--overwrite_cache \
--overwrite_output_dir
```
## Testing the model
Example usage below:
```python
from transformers import pipeline
qa = pipeline(
"question-answering",
model="NeuML/bert-small-cord19qa",
tokenizer="NeuML/bert-small-cord19qa"
)
qa({
"question": "What is the median incubation period?",
"context": "The incubation period is around 5 days (range: 4-7 days) with a maximum of 12-13 day"
})
qa({
"question": "What is the incubation period range?",
"context": "The incubation period is around 5 days (range: 4-7 days) with a maximum of 12-13 day"
})
qa({
"question": "What type of surfaces does it persist?",
"context": "The virus can survive on surfaces for up to 72 hours such as plastic and stainless steel ."
})
```
```json
{"score": 0.5970273583242793, "start": 32, "end": 38, "answer": "5 days"}
{"score": 0.999555868193891, "start": 39, "end": 56, "answer": "(range: 4-7 days)"}
{"score": 0.9992726505196998, "start": 61, "end": 88, "answer": "plastic and stainless steel"}
```
| {} | question-answering | NeuML/bert-small-cord19qa | [
"transformers",
"pytorch",
"jax",
"bert",
"question-answering",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bert #question-answering #endpoints_compatible #has_space #region-us
| # BERT-Small fine-tuned on CORD-19 QA dataset
bert-small-cord19-squad model fine-tuned on the CORD-19 QA dataset.
## CORD-19 QA dataset
The CORD-19 QA dataset is a SQuAD 2.0 formatted list of question, context, answer combinations covering the CORD-19 dataset.
## Building the model
## Testing the model
Example usage below:
| [
"# BERT-Small fine-tuned on CORD-19 QA dataset\n\nbert-small-cord19-squad model fine-tuned on the CORD-19 QA dataset.",
"## CORD-19 QA dataset\nThe CORD-19 QA dataset is a SQuAD 2.0 formatted list of question, context, answer combinations covering the CORD-19 dataset.",
"## Building the model",
"## Testing the model\n\nExample usage below:"
] | [
"TAGS\n#transformers #pytorch #jax #bert #question-answering #endpoints_compatible #has_space #region-us \n",
"# BERT-Small fine-tuned on CORD-19 QA dataset\n\nbert-small-cord19-squad model fine-tuned on the CORD-19 QA dataset.",
"## CORD-19 QA dataset\nThe CORD-19 QA dataset is a SQuAD 2.0 formatted list of question, context, answer combinations covering the CORD-19 dataset.",
"## Building the model",
"## Testing the model\n\nExample usage below:"
] | [
36,
44,
42,
4,
10
] | [
"passage: TAGS\n#transformers #pytorch #jax #bert #question-answering #endpoints_compatible #has_space #region-us \n# BERT-Small fine-tuned on CORD-19 QA dataset\n\nbert-small-cord19-squad model fine-tuned on the CORD-19 QA dataset.## CORD-19 QA dataset\nThe CORD-19 QA dataset is a SQuAD 2.0 formatted list of question, context, answer combinations covering the CORD-19 dataset.## Building the model## Testing the model\n\nExample usage below:"
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null | null | transformers |
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 24135330
- CO2 Emissions (in grams): 155.8470724053265
## Validation Metrics
- Loss: 1.369327425956726
- Rouge1: 52.6656
- Rouge2: 30.5879
- RougeL: 40.1268
- RougeLsum: 47.4438
- Gen Len: 75.4625
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/Neuralearn/autonlp-Summarization-AutoNLP-24135330
``` | {"language": "unk", "tags": "autonlp", "datasets": ["Neuralearn/autonlp-data-Summarization-AutoNLP"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 155.8470724053265} | text2text-generation | Neuralearn/autonlp-Summarization-AutoNLP-24135330 | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autonlp",
"unk",
"dataset:Neuralearn/autonlp-data-Summarization-AutoNLP",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autonlp #unk #dataset-Neuralearn/autonlp-data-Summarization-AutoNLP #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 24135330
- CO2 Emissions (in grams): 155.8470724053265
## Validation Metrics
- Loss: 1.369327425956726
- Rouge1: 52.6656
- Rouge2: 30.5879
- RougeL: 40.1268
- RougeLsum: 47.4438
- Gen Len: 75.4625
## Usage
You can use cURL to access this model:
| [
"# Model Trained Using AutoNLP\n\n- Problem type: Summarization\n- Model ID: 24135330\n- CO2 Emissions (in grams): 155.8470724053265",
"## Validation Metrics\n\n- Loss: 1.369327425956726\n- Rouge1: 52.6656\n- Rouge2: 30.5879\n- RougeL: 40.1268\n- RougeLsum: 47.4438\n- Gen Len: 75.4625",
"## Usage\n\nYou can use cURL to access this model:"
] | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autonlp #unk #dataset-Neuralearn/autonlp-data-Summarization-AutoNLP #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Summarization\n- Model ID: 24135330\n- CO2 Emissions (in grams): 155.8470724053265",
"## Validation Metrics\n\n- Loss: 1.369327425956726\n- Rouge1: 52.6656\n- Rouge2: 30.5879\n- RougeL: 40.1268\n- RougeLsum: 47.4438\n- Gen Len: 75.4625",
"## Usage\n\nYou can use cURL to access this model:"
] | [
78,
40,
56,
13
] | [
"passage: TAGS\n#transformers #pytorch #pegasus #text2text-generation #autonlp #unk #dataset-Neuralearn/autonlp-data-Summarization-AutoNLP #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n# Model Trained Using AutoNLP\n\n- Problem type: Summarization\n- Model ID: 24135330\n- CO2 Emissions (in grams): 155.8470724053265## Validation Metrics\n\n- Loss: 1.369327425956726\n- Rouge1: 52.6656\n- Rouge2: 30.5879\n- RougeL: 40.1268\n- RougeLsum: 47.4438\n- Gen Len: 75.4625## Usage\n\nYou can use cURL to access this model:"
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] |
null | null | transformers |
# Test
Hf T5: -95.86687088012695
MTF T5: -67.8558578491211
| {"tags": ["t5-new-failed"]} | text2text-generation | NewT5SharedHeadsSharedKeyValues/t5-efficient-base-sh | [
"transformers",
"t5",
"text2text-generation",
"t5-new-failed",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #t5 #text2text-generation #t5-new-failed #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Test
Hf T5: -95.86687088012695
MTF T5: -67.8558578491211
| [
"# Test\nHf T5: -95.86687088012695\nMTF T5: -67.8558578491211"
] | [
"TAGS\n#transformers #t5 #text2text-generation #t5-new-failed #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Test\nHf T5: -95.86687088012695\nMTF T5: -67.8558578491211"
] | [
51,
28
] | [
"passage: TAGS\n#transformers #t5 #text2text-generation #t5-new-failed #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Test\nHf T5: -95.86687088012695\nMTF T5: -67.8558578491211"
] | [
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] |
null | null | transformers |
# Test
Hf T5:
MTF T5: -80.44100952148438
| {"tags": ["t5-new-hf-not-loaded"]} | text2text-generation | NewT5SharedHeadsSharedKeyValues/t5-efficient-base-skv | [
"transformers",
"t5",
"text2text-generation",
"t5-new-hf-not-loaded",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #t5 #text2text-generation #t5-new-hf-not-loaded #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Test
Hf T5:
MTF T5: -80.44100952148438
| [
"# Test\nHf T5: \nMTF T5: -80.44100952148438"
] | [
"TAGS\n#transformers #t5 #text2text-generation #t5-new-hf-not-loaded #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Test\nHf T5: \nMTF T5: -80.44100952148438"
] | [
56,
20
] | [
"passage: TAGS\n#transformers #t5 #text2text-generation #t5-new-hf-not-loaded #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Test\nHf T5: \nMTF T5: -80.44100952148438"
] | [
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] |
null | null | transformers |
# Test
Hf T5: -110.35000801086426
MTF T5: -57.58127975463867
| {"tags": ["t5-new-failed"]} | text2text-generation | NewT5SharedHeadsSharedKeyValues/t5-efficient-large-sh | [
"transformers",
"t5",
"text2text-generation",
"t5-new-failed",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #t5 #text2text-generation #t5-new-failed #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Test
Hf T5: -110.35000801086426
MTF T5: -57.58127975463867
| [
"# Test\nHf T5: -110.35000801086426\nMTF T5: -57.58127975463867"
] | [
"TAGS\n#transformers #t5 #text2text-generation #t5-new-failed #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Test\nHf T5: -110.35000801086426\nMTF T5: -57.58127975463867"
] | [
51,
28
] | [
"passage: TAGS\n#transformers #t5 #text2text-generation #t5-new-failed #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Test\nHf T5: -110.35000801086426\nMTF T5: -57.58127975463867"
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] |
null | null | transformers |
# Test
Hf T5:
MTF T5: -59.432472229003906
| {"tags": ["t5-new-hf-not-loaded"]} | text2text-generation | NewT5SharedHeadsSharedKeyValues/t5-efficient-large-skv | [
"transformers",
"t5",
"text2text-generation",
"t5-new-hf-not-loaded",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #t5 #text2text-generation #t5-new-hf-not-loaded #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Test
Hf T5:
MTF T5: -59.432472229003906
| [
"# Test\nHf T5: \nMTF T5: -59.432472229003906"
] | [
"TAGS\n#transformers #t5 #text2text-generation #t5-new-hf-not-loaded #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Test\nHf T5: \nMTF T5: -59.432472229003906"
] | [
56,
21
] | [
"passage: TAGS\n#transformers #t5 #text2text-generation #t5-new-hf-not-loaded #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Test\nHf T5: \nMTF T5: -59.432472229003906"
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] |
null | null | transformers |
# Test
Hf T5: -146.39734268188477
MTF T5: -72.12132263183594
| {"tags": ["t5-new-failed"]} | text2text-generation | NewT5SharedHeadsSharedKeyValues/t5-efficient-small-sh | [
"transformers",
"t5",
"text2text-generation",
"t5-new-failed",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #t5 #text2text-generation #t5-new-failed #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Test
Hf T5: -146.39734268188477
MTF T5: -72.12132263183594
| [
"# Test\nHf T5: -146.39734268188477\nMTF T5: -72.12132263183594"
] | [
"TAGS\n#transformers #t5 #text2text-generation #t5-new-failed #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Test\nHf T5: -146.39734268188477\nMTF T5: -72.12132263183594"
] | [
51,
29
] | [
"passage: TAGS\n#transformers #t5 #text2text-generation #t5-new-failed #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Test\nHf T5: -146.39734268188477\nMTF T5: -72.12132263183594"
] | [
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] |
null | null | transformers |
# Test
Hf T5:
MTF T5: -277.564697265625
| {"tags": ["t5-new-hf-not-loaded"]} | text2text-generation | NewT5SharedHeadsSharedKeyValues/t5-efficient-small-shkv | [
"transformers",
"t5",
"text2text-generation",
"t5-new-hf-not-loaded",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #t5 #text2text-generation #t5-new-hf-not-loaded #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Test
Hf T5:
MTF T5: -277.564697265625
| [
"# Test\nHf T5: \nMTF T5: -277.564697265625"
] | [
"TAGS\n#transformers #t5 #text2text-generation #t5-new-hf-not-loaded #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Test\nHf T5: \nMTF T5: -277.564697265625"
] | [
56,
20
] | [
"passage: TAGS\n#transformers #t5 #text2text-generation #t5-new-hf-not-loaded #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Test\nHf T5: \nMTF T5: -277.564697265625"
] | [
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] |
null | null | transformers |
# Test
Hf T5: -149.6728801727295
MTF T5: -74.4166259765625
| {"tags": ["t5-new-failed"]} | text2text-generation | NewT5SharedHeadsSharedKeyValues/t5-efficient-tiny-sh | [
"transformers",
"t5",
"text2text-generation",
"t5-new-failed",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #t5 #text2text-generation #t5-new-failed #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Test
Hf T5: -149.6728801727295
MTF T5: -74.4166259765625
| [
"# Test\nHf T5: -149.6728801727295\nMTF T5: -74.4166259765625"
] | [
"TAGS\n#transformers #t5 #text2text-generation #t5-new-failed #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Test\nHf T5: -149.6728801727295\nMTF T5: -74.4166259765625"
] | [
51,
28
] | [
"passage: TAGS\n#transformers #t5 #text2text-generation #t5-new-failed #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Test\nHf T5: -149.6728801727295\nMTF T5: -74.4166259765625"
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] |
null | null | transformers |
# Test
Hf T5:
MTF T5: -138.18275451660156
| {"tags": ["t5-new-hf-not-loaded"]} | text2text-generation | NewT5SharedHeadsSharedKeyValues/t5-efficient-tiny-skv | [
"transformers",
"t5",
"text2text-generation",
"t5-new-hf-not-loaded",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #t5 #text2text-generation #t5-new-hf-not-loaded #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Test
Hf T5:
MTF T5: -138.18275451660156
| [
"# Test\nHf T5: \nMTF T5: -138.18275451660156"
] | [
"TAGS\n#transformers #t5 #text2text-generation #t5-new-hf-not-loaded #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Test\nHf T5: \nMTF T5: -138.18275451660156"
] | [
56,
21
] | [
"passage: TAGS\n#transformers #t5 #text2text-generation #t5-new-hf-not-loaded #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Test\nHf T5: \nMTF T5: -138.18275451660156"
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null | null | transformers |
# Test
Hf T5: -118.6875057220459
MTF T5: -76.85459899902344
| {"tags": ["t5-new-failed"]} | text2text-generation | NewT5SharedHeadsSharedKeyValues/t5-efficient-xl-sh | [
"transformers",
"t5",
"text2text-generation",
"t5-new-failed",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #t5 #text2text-generation #t5-new-failed #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Test
Hf T5: -118.6875057220459
MTF T5: -76.85459899902344
| [
"# Test\nHf T5: -118.6875057220459\nMTF T5: -76.85459899902344"
] | [
"TAGS\n#transformers #t5 #text2text-generation #t5-new-failed #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Test\nHf T5: -118.6875057220459\nMTF T5: -76.85459899902344"
] | [
51,
28
] | [
"passage: TAGS\n#transformers #t5 #text2text-generation #t5-new-failed #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Test\nHf T5: -118.6875057220459\nMTF T5: -76.85459899902344"
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] |
null | null | transformers |
# Test
Hf T5:
MTF T5: -66.05513000488281
| {"tags": ["t5-new-hf-not-loaded"]} | text2text-generation | NewT5SharedHeadsSharedKeyValues/t5-efficient-xl-skv | [
"transformers",
"t5",
"text2text-generation",
"t5-new-hf-not-loaded",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #t5 #text2text-generation #t5-new-hf-not-loaded #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Test
Hf T5:
MTF T5: -66.05513000488281
| [
"# Test\nHf T5: \nMTF T5: -66.05513000488281"
] | [
"TAGS\n#transformers #t5 #text2text-generation #t5-new-hf-not-loaded #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Test\nHf T5: \nMTF T5: -66.05513000488281"
] | [
56,
21
] | [
"passage: TAGS\n#transformers #t5 #text2text-generation #t5-new-hf-not-loaded #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Test\nHf T5: \nMTF T5: -66.05513000488281"
] | [
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null | null | transformers |
# xlm-r-finetuned-toxic-political-tweets-es
This model is based on the pre-trained model [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) and was fine-tuned on a dataset of tweets from members of the [Spanish Congress of the Deputies](https://www.congreso.es/) annotated regarding the level of political toxicity they generate.
### Inputs
The model has been trained on the text of Spanish tweets authored by politicians in 2021, so this is the input expected and its performance can degrade when applied to texts from other domains.
### Outputs
The model predicts 2 signals of political toxicity:
* Toxic: the tweet has at least some degree of toxicity.
* Very Toxic: the tweet has a strong degree of toxicity.
A value between 0 and 1 is predicted for each signal.
### Intended uses & limitations
The model was created to be used as a toxicity detector of spanish tweets from Spanish Congress Deputies. If the intended use is other one, for instance; toxicity detection on films reviews, the results won't be reliable and you might look for another model with this concrete purpose.
### How to use
The model can be used directly with a text-classification pipeline:
```python
>>> from transformers import pipeline
>>> text = "Es usted un auténtico impresentable, su señoría."
>>> pipe = pipeline("text-classification", model="Newtral/xlm-r-finetuned-toxic-political-tweets-es")
>>> pipe(text, return_all_scores=True)
[[{'label': 'toxic', 'score': 0.92560875415802},
{'label': 'very toxic', 'score': 0.8310967683792114}]]
```
### Training procedure
The pre-trained model was fine-tuned for sequence classification using the following hyperparameters, which were selected from a validation set:
* Batch size = 32
* Learning rate = 2e-5
* Epochs = 5
* Max length = 64
The optimizer used was AdamW and the loss optimized was binary cross-entropy with class weights proportional to the class imbalance. | {"language": "es", "license": "apache-2.0"} | text-classification | Newtral/xlm-r-finetuned-toxic-political-tweets-es | [
"transformers",
"pytorch",
"safetensors",
"xlm-roberta",
"text-classification",
"es",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #safetensors #xlm-roberta #text-classification #es #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# xlm-r-finetuned-toxic-political-tweets-es
This model is based on the pre-trained model xlm-roberta-base and was fine-tuned on a dataset of tweets from members of the Spanish Congress of the Deputies annotated regarding the level of political toxicity they generate.
### Inputs
The model has been trained on the text of Spanish tweets authored by politicians in 2021, so this is the input expected and its performance can degrade when applied to texts from other domains.
### Outputs
The model predicts 2 signals of political toxicity:
* Toxic: the tweet has at least some degree of toxicity.
* Very Toxic: the tweet has a strong degree of toxicity.
A value between 0 and 1 is predicted for each signal.
### Intended uses & limitations
The model was created to be used as a toxicity detector of spanish tweets from Spanish Congress Deputies. If the intended use is other one, for instance; toxicity detection on films reviews, the results won't be reliable and you might look for another model with this concrete purpose.
### How to use
The model can be used directly with a text-classification pipeline:
### Training procedure
The pre-trained model was fine-tuned for sequence classification using the following hyperparameters, which were selected from a validation set:
* Batch size = 32
* Learning rate = 2e-5
* Epochs = 5
* Max length = 64
The optimizer used was AdamW and the loss optimized was binary cross-entropy with class weights proportional to the class imbalance. | [
"# xlm-r-finetuned-toxic-political-tweets-es\n\nThis model is based on the pre-trained model xlm-roberta-base and was fine-tuned on a dataset of tweets from members of the Spanish Congress of the Deputies annotated regarding the level of political toxicity they generate.",
"### Inputs\n\nThe model has been trained on the text of Spanish tweets authored by politicians in 2021, so this is the input expected and its performance can degrade when applied to texts from other domains.",
"### Outputs\n\nThe model predicts 2 signals of political toxicity:\n\n* Toxic: the tweet has at least some degree of toxicity.\n* Very Toxic: the tweet has a strong degree of toxicity.\n\nA value between 0 and 1 is predicted for each signal.",
"### Intended uses & limitations \n\nThe model was created to be used as a toxicity detector of spanish tweets from Spanish Congress Deputies. If the intended use is other one, for instance; toxicity detection on films reviews, the results won't be reliable and you might look for another model with this concrete purpose.",
"### How to use\n\nThe model can be used directly with a text-classification pipeline:",
"### Training procedure\nThe pre-trained model was fine-tuned for sequence classification using the following hyperparameters, which were selected from a validation set:\n\n* Batch size = 32\n* Learning rate = 2e-5\n* Epochs = 5\n* Max length = 64\n\nThe optimizer used was AdamW and the loss optimized was binary cross-entropy with class weights proportional to the class imbalance."
] | [
"TAGS\n#transformers #pytorch #safetensors #xlm-roberta #text-classification #es #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# xlm-r-finetuned-toxic-political-tweets-es\n\nThis model is based on the pre-trained model xlm-roberta-base and was fine-tuned on a dataset of tweets from members of the Spanish Congress of the Deputies annotated regarding the level of political toxicity they generate.",
"### Inputs\n\nThe model has been trained on the text of Spanish tweets authored by politicians in 2021, so this is the input expected and its performance can degrade when applied to texts from other domains.",
"### Outputs\n\nThe model predicts 2 signals of political toxicity:\n\n* Toxic: the tweet has at least some degree of toxicity.\n* Very Toxic: the tweet has a strong degree of toxicity.\n\nA value between 0 and 1 is predicted for each signal.",
"### Intended uses & limitations \n\nThe model was created to be used as a toxicity detector of spanish tweets from Spanish Congress Deputies. If the intended use is other one, for instance; toxicity detection on films reviews, the results won't be reliable and you might look for another model with this concrete purpose.",
"### How to use\n\nThe model can be used directly with a text-classification pipeline:",
"### Training procedure\nThe pre-trained model was fine-tuned for sequence classification using the following hyperparameters, which were selected from a validation set:\n\n* Batch size = 32\n* Learning rate = 2e-5\n* Epochs = 5\n* Max length = 64\n\nThe optimizer used was AdamW and the loss optimized was binary cross-entropy with class weights proportional to the class imbalance."
] | [
59,
74,
48,
65,
74,
20,
94
] | [
"passage: TAGS\n#transformers #pytorch #safetensors #xlm-roberta #text-classification #es #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n# xlm-r-finetuned-toxic-political-tweets-es\n\nThis model is based on the pre-trained model xlm-roberta-base and was fine-tuned on a dataset of tweets from members of the Spanish Congress of the Deputies annotated regarding the level of political toxicity they generate.### Inputs\n\nThe model has been trained on the text of Spanish tweets authored by politicians in 2021, so this is the input expected and its performance can degrade when applied to texts from other domains.### Outputs\n\nThe model predicts 2 signals of political toxicity:\n\n* Toxic: the tweet has at least some degree of toxicity.\n* Very Toxic: the tweet has a strong degree of toxicity.\n\nA value between 0 and 1 is predicted for each signal.### Intended uses & limitations \n\nThe model was created to be used as a toxicity detector of spanish tweets from Spanish Congress Deputies. If the intended use is other one, for instance; toxicity detection on films reviews, the results won't be reliable and you might look for another model with this concrete purpose.### How to use\n\nThe model can be used directly with a text-classification pipeline:### Training procedure\nThe pre-trained model was fine-tuned for sequence classification using the following hyperparameters, which were selected from a validation set:\n\n* Batch size = 32\n* Learning rate = 2e-5\n* Epochs = 5\n* Max length = 64\n\nThe optimizer used was AdamW and the loss optimized was binary cross-entropy with class weights proportional to the class imbalance."
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null | null | transformers |
<!-- 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. -->
# ## labels
- 0: Object
- 1: Recycle
- 2: Non-Recycle
# vit-base-patch16-224
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1510
- Accuracy: 0.9443
## 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: 60
- eval_batch_size: 60
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 240
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1438 | 1.0 | 150 | 0.1645 | 0.9353 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3
| {"license": "apache-2.0", "tags": ["image-classification", "generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "vit-base-patch16-224", "results": []}]} | image-classification | NhatPham/vit-base-patch16-224-recylce-ft | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| ## labels
=========
* 0: Object
* 1: Recycle
* 2: Non-Recycle
vit-base-patch16-224
====================
This model is a fine-tuned version of google/vit-base-patch16-224 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1510
* Accuracy: 0.9443
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: 60
* eval\_batch\_size: 60
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 240
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 1
### Training results
### Framework versions
* Transformers 4.11.3
* Pytorch 1.10.0+cu111
* Datasets 1.14.0
* Tokenizers 0.10.3
| [
"## labels\n=========\n\n\n* 0: Object\n* 1: Recycle\n* 2: Non-Recycle\n\n\nvit-base-patch16-224\n====================\n\n\nThis model is a fine-tuned version of google/vit-base-patch16-224 on the None dataset.\nIt achieves the following results on the evaluation set:\n\n\n* Loss: 0.1510\n* Accuracy: 0.9443\n\n\nModel description\n-----------------\n\n\nMore information needed\n\n\nIntended uses & limitations\n---------------------------\n\n\nMore information needed\n\n\nTraining and evaluation data\n----------------------------\n\n\nMore information needed\n\n\nTraining procedure\n------------------",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 60\n* eval\\_batch\\_size: 60\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 240\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 1",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.10.0+cu111\n* Datasets 1.14.0\n* Tokenizers 0.10.3"
] | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"## labels\n=========\n\n\n* 0: Object\n* 1: Recycle\n* 2: Non-Recycle\n\n\nvit-base-patch16-224\n====================\n\n\nThis model is a fine-tuned version of google/vit-base-patch16-224 on the None dataset.\nIt achieves the following results on the evaluation set:\n\n\n* Loss: 0.1510\n* Accuracy: 0.9443\n\n\nModel description\n-----------------\n\n\nMore information needed\n\n\nIntended uses & limitations\n---------------------------\n\n\nMore information needed\n\n\nTraining and evaluation data\n----------------------------\n\n\nMore information needed\n\n\nTraining procedure\n------------------",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 60\n* eval\\_batch\\_size: 60\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 240\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 1",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.10.0+cu111\n* Datasets 1.14.0\n* Tokenizers 0.10.3"
] | [
55,
122,
144,
4,
33
] | [
"passage: TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n## labels\n=========\n\n\n* 0: Object\n* 1: Recycle\n* 2: Non-Recycle\n\n\nvit-base-patch16-224\n====================\n\n\nThis model is a fine-tuned version of google/vit-base-patch16-224 on the None dataset.\nIt achieves the following results on the evaluation set:\n\n\n* Loss: 0.1510\n* Accuracy: 0.9443\n\n\nModel description\n-----------------\n\n\nMore information needed\n\n\nIntended uses & limitations\n---------------------------\n\n\nMore information needed\n\n\nTraining and evaluation data\n----------------------------\n\n\nMore information needed\n\n\nTraining procedure\n------------------### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 60\n* eval\\_batch\\_size: 60\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 240\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 1### Training results### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.10.0+cu111\n* Datasets 1.14.0\n* Tokenizers 0.10.3"
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null | null | transformers |
<!-- 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-finetuned-ks
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the superb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1258
- Accuracy: 0.9793
## 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
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1561 | 1.0 | 399 | 1.1127 | 0.6643 |
| 0.4803 | 2.0 | 798 | 0.3547 | 0.9687 |
| 0.2855 | 3.0 | 1197 | 0.1663 | 0.9763 |
| 0.1987 | 4.0 | 1596 | 0.1258 | 0.9793 |
| 0.2097 | 5.0 | 1995 | 0.1171 | 0.9791 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["superb"], "metrics": ["accuracy"], "model-index": [{"name": "wav2vec2-base-finetuned-ks", "results": []}]} | audio-classification | NhatPham/wav2vec2-base-finetuned-ks | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"dataset:superb",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #dataset-superb #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-finetuned-ks
==========================
This model is a fine-tuned version of facebook/wav2vec2-base on the superb dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1258
* Accuracy: 0.9793
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
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 5
### Training results
### Framework versions
* Transformers 4.11.3
* Pytorch 1.9.0+cu111
* Datasets 1.14.0
* Tokenizers 0.10.3
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.9.0+cu111\n* Datasets 1.14.0\n* Tokenizers 0.10.3"
] | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #dataset-superb #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.9.0+cu111\n* Datasets 1.14.0\n* Tokenizers 0.10.3"
] | [
58,
144,
4,
34
] | [
"passage: TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #dataset-superb #license-apache-2.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 5### Training results### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.9.0+cu111\n* Datasets 1.14.0\n* Tokenizers 0.10.3"
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null | null | transformers |
# wav2vec2-large-xlsr-53-french
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in French 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", "fr", split="test[:20%]")
processor = Wav2Vec2Processor.from_pretrained("Nhut/wav2vec2-large-xlsr-french")
model = Wav2Vec2ForCTC.from_pretrained("Nhut/wav2vec2-large-xlsr-french")
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):
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 French test data of Common Voice.
```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
test_dataset = load_dataset("common_voice", "fr")
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("Nhut/wav2vec2-large-xlsr-french")
model = Wav2Vec2ForCTC.from_pretrained("Nhut/wav2vec2-large-xlsr-french")
model.to("cuda")
chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\â€\�]'
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"] = 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)
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**: 29.31 %
## Training
V1 of the Common Voice `train`, `validation` datasets were used for training.
## Testing
20% of V6.1 of the Common Voice `Test` dataset were used for training. | {"language": "fr", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xlsr-53-French by Nhut DOAN NGUYEN", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Common Voice fr", "type": "common_voice", "args": "fr"}, "metrics": [{"type": "wer", "value": "xx.xx", "name": "Test WER"}]}]}]} | automatic-speech-recognition | Nhut/wav2vec2-large-xlsr-french | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"fr",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #fr #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# wav2vec2-large-xlsr-53-french
Fine-tuned facebook/wav2vec2-large-xlsr-53 in French using the 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:
## Evaluation
The model can be evaluated as follows on the French test data of Common Voice.
Test Result: 29.31 %
## Training
V1 of the Common Voice 'train', 'validation' datasets were used for training.
## Testing
20% of V6.1 of the Common Voice 'Test' dataset were used for training. | [
"# wav2vec2-large-xlsr-53-french \n\nFine-tuned facebook/wav2vec2-large-xlsr-53 in French using the Common Voice\n\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model can be evaluated as follows on the French test data of Common Voice.\n\n\n\nTest Result: 29.31 %",
"## Training\n\nV1 of the Common Voice 'train', 'validation' datasets were used for training.",
"## Testing\n\n20% of V6.1 of the Common Voice 'Test' dataset were used for training."
] | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #fr #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# wav2vec2-large-xlsr-53-french \n\nFine-tuned facebook/wav2vec2-large-xlsr-53 in French using the Common Voice\n\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model can be evaluated as follows on the French test data of Common Voice.\n\n\n\nTest Result: 29.31 %",
"## Training\n\nV1 of the Common Voice 'train', 'validation' datasets were used for training.",
"## Testing\n\n20% of V6.1 of the Common Voice 'Test' dataset were used for training."
] | [
80,
62,
20,
27,
26,
21
] | [
"passage: TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #fr #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n# wav2vec2-large-xlsr-53-french \n\nFine-tuned facebook/wav2vec2-large-xlsr-53 in French using the Common Voice\n\nWhen using this model, make sure that your speech input is sampled at 16kHz.## Usage\n\nThe model can be used directly (without a language model) as follows:## Evaluation\n\nThe model can be evaluated as follows on the French test data of Common Voice.\n\n\n\nTest Result: 29.31 %## Training\n\nV1 of the Common Voice 'train', 'validation' datasets were used for training.## Testing\n\n20% of V6.1 of the Common Voice 'Test' dataset were used for training."
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null | null | transformers | # Wav2Vec2-Large-XLSR-53-Vietnamese
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Vietnamese using the [Common Voice](https://huggingface.co/datasets/common_voice), [FOSD](https://data.mendeley.com/datasets/k9sxg2twv4/4) and [VIVOS](https://ailab.hcmus.edu.vn/vivos).
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
ENCODER = {
"ia ": "iê ",
"ìa ": "iề ",
"ía ": "iế ",
"ỉa ": "iể ",
"ĩa ": "iễ ",
"ịa ": "iệ ",
"ya ": "yê ",
"ỳa ": "yề ",
"ýa ": "yế ",
"ỷa ": "yể ",
"ỹa ": "yễ ",
"ỵa ": "yệ ",
"ua ": "uô ",
"ùa ": "uồ ",
"úa ": "uố ",
"ủa ": "uổ ",
"ũa ": "uỗ ",
"ụa ": "uộ ",
"ưa ": "ươ ",
"ừa ": "ườ ",
"ứa ": "ướ ",
"ửa ": "ưở ",
"ữa ": "ưỡ ",
"ựa ": "ượ ",
"ke": "ce",
"kè": "cè",
"ké": "cé",
"kẻ": "cẻ",
"kẽ": "cẽ",
"kẹ": "cẹ",
"kê": "cê",
"kề": "cề",
"kế": "cế",
"kể": "cể",
"kễ": "cễ",
"kệ": "cệ",
"ki": "ci",
"kì": "cì",
"kí": "cí",
"kỉ": "cỉ",
"kĩ": "cĩ",
"kị": "cị",
"ky": "cy",
"kỳ": "cỳ",
"ký": "cý",
"kỷ": "cỷ",
"kỹ": "cỹ",
"kỵ": "cỵ",
"ghe": "ge",
"ghè": "gè",
"ghé": "gé",
"ghẻ": "gẻ",
"ghẽ": "gẽ",
"ghẹ": "gẹ",
"ghê": "gê",
"ghề": "gề",
"ghế": "gế",
"ghể": "gể",
"ghễ": "gễ",
"ghệ": "gệ",
"ngh": "\x80",
"uyê": "\x96",
"uyề": "\x97",
"uyế": "\x98",
"uyể": "\x99",
"uyễ": "\x9a",
"uyệ": "\x9b",
"ng": "\x81",
"ch": "\x82",
"gh": "\x83",
"nh": "\x84",
"gi": "\x85",
"ph": "\x86",
"kh": "\x87",
"th": "\x88",
"tr": "\x89",
"uy": "\x8a",
"uỳ": "\x8b",
"uý": "\x8c",
"uỷ": "\x8d",
"uỹ": "\x8e",
"uỵ": "\x8f",
"iê": "\x90",
"iề": "\x91",
"iế": "\x92",
"iể": "\x93",
"iễ": "\x94",
"iệ": "\x95",
"uô": "\x9c",
"uồ": "\x9d",
"uố": "\x9e",
"uổ": "\x9f",
"uỗ": "\xa0",
"uộ": "\xa1",
"ươ": "\xa2",
"ườ": "\xa3",
"ướ": "\xa4",
"ưở": "\xa5",
"ưỡ": "\xa6",
"ượ": "\xa7",
}
def decode_string(x):
for k, v in list(reversed(list(ENCODER.items()))):
x = x.replace(v, k)
return x
test_dataset = load_dataset("common_voice", "vi", split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("Nhut/wav2vec2-large-xlsr-vietnamese")
model = Wav2Vec2ForCTC.from_pretrained("Nhut/wav2vec2-large-xlsr-vietnamese")
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):
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:", [decode_string(x) for x in processor.batch_decode(predicted_ids)])
print("Reference:", test_dataset["sentence"][:2])
```
## Evaluation
The model can be evaluated as follows on the Vietnamese test data of Common Voice.
```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
ENCODER = {
"ia ": "iê ",
"ìa ": "iề ",
"ía ": "iế ",
"ỉa ": "iể ",
"ĩa ": "iễ ",
"ịa ": "iệ ",
"ya ": "yê ",
"ỳa ": "yề ",
"ýa ": "yế ",
"ỷa ": "yể ",
"ỹa ": "yễ ",
"ỵa ": "yệ ",
"ua ": "uô ",
"ùa ": "uồ ",
"úa ": "uố ",
"ủa ": "uổ ",
"ũa ": "uỗ ",
"ụa ": "uộ ",
"ưa ": "ươ ",
"ừa ": "ườ ",
"ứa ": "ướ ",
"ửa ": "ưở ",
"ữa ": "ưỡ ",
"ựa ": "ượ ",
"ke": "ce",
"kè": "cè",
"ké": "cé",
"kẻ": "cẻ",
"kẽ": "cẽ",
"kẹ": "cẹ",
"kê": "cê",
"kề": "cề",
"kế": "cế",
"kể": "cể",
"kễ": "cễ",
"kệ": "cệ",
"ki": "ci",
"kì": "cì",
"kí": "cí",
"kỉ": "cỉ",
"kĩ": "cĩ",
"kị": "cị",
"ky": "cy",
"kỳ": "cỳ",
"ký": "cý",
"kỷ": "cỷ",
"kỹ": "cỹ",
"kỵ": "cỵ",
"ghe": "ge",
"ghè": "gè",
"ghé": "gé",
"ghẻ": "gẻ",
"ghẽ": "gẽ",
"ghẹ": "gẹ",
"ghê": "gê",
"ghề": "gề",
"ghế": "gế",
"ghể": "gể",
"ghễ": "gễ",
"ghệ": "gệ",
"ngh": "\x80",
"uyê": "\x96",
"uyề": "\x97",
"uyế": "\x98",
"uyể": "\x99",
"uyễ": "\x9a",
"uyệ": "\x9b",
"ng": "\x81",
"ch": "\x82",
"gh": "\x83",
"nh": "\x84",
"gi": "\x85",
"ph": "\x86",
"kh": "\x87",
"th": "\x88",
"tr": "\x89",
"uy": "\x8a",
"uỳ": "\x8b",
"uý": "\x8c",
"uỷ": "\x8d",
"uỹ": "\x8e",
"uỵ": "\x8f",
"iê": "\x90",
"iề": "\x91",
"iế": "\x92",
"iể": "\x93",
"iễ": "\x94",
"iệ": "\x95",
"uô": "\x9c",
"uồ": "\x9d",
"uố": "\x9e",
"uổ": "\x9f",
"uỗ": "\xa0",
"uộ": "\xa1",
"ươ": "\xa2",
"ườ": "\xa3",
"ướ": "\xa4",
"ưở": "\xa5",
"ưỡ": "\xa6",
"ượ": "\xa7",
}
def decode_string(x):
for k, v in list(reversed(list(ENCODER.items()))):
x = x.replace(v, k)
return x
test_dataset = load_dataset("common_voice", "vi", split="test")
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("Nhut/wav2vec2-large-xlsr-vietnamese")
model = Wav2Vec2ForCTC.from_pretrained("Nhut/wav2vec2-large-xlsr-vietnamese")
model.to("cuda")
chars_to_ignore_regex = '[\\\+\@\ǀ\,\?\.\!\-\;\:\"\“\%\‘\”\�]'
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"] = 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)
# decode_string: We replace the encoded letter with the initial letters
batch["pred_strings"] = [decode_string(x) for x in batch["pred_strings"]]
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**: 49.59 %
## Training
The Common Voice `train`, `validation` and FOSD datasets and VIVOS datasets were used for training as well.
The script used for training can be found [here](https://colab.research.google.com/drive/11pP4uVJj4SYZTzGjlCUtOHywlhYqs0cPx) | {"language": "vi", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice", {"FOSD": "https://data.mendeley.com/datasets/k9sxg2twv4/4"}, {"VIVOS": "https://ailab.hcmus.edu.vn/vivos"}], "metrics": ["wer"], "model-index": [{"name": "XLSR Wav2Vec2 Vietnamese by Nhut", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Common Voice vi", "type": "common_voice", "args": "vi"}, "metrics": [{"type": "wer", "value": 49.59, "name": "Test WER"}]}]}]} | automatic-speech-recognition | Nhut/wav2vec2-large-xlsr-vietnamese | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"vi",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"vi"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #vi #license-apache-2.0 #model-index #endpoints_compatible #region-us
| # Wav2Vec2-Large-XLSR-53-Vietnamese
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Vietnamese using the Common Voice, FOSD and VIVOS.
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:
## Evaluation
The model can be evaluated as follows on the Vietnamese test data of Common Voice.
Test Result: 49.59 %
## Training
The Common Voice 'train', 'validation' and FOSD datasets and VIVOS datasets were used for training as well.
The script used for training can be found here | [
"# Wav2Vec2-Large-XLSR-53-Vietnamese\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Vietnamese using the Common Voice, FOSD and VIVOS.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\nThe model can be evaluated as follows on the Vietnamese test data of Common Voice.\n\nTest Result: 49.59 %",
"## Training\nThe Common Voice 'train', 'validation' and FOSD datasets and VIVOS datasets were used for training as well.\nThe script used for training can be found here"
] | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #vi #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Vietnamese\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Vietnamese using the Common Voice, FOSD and VIVOS.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\nThe model can be evaluated as follows on the Vietnamese test data of Common Voice.\n\nTest Result: 49.59 %",
"## Training\nThe Common Voice 'train', 'validation' and FOSD datasets and VIVOS datasets were used for training as well.\nThe script used for training can be found here"
] | [
71,
72,
20,
29,
44
] | [
"passage: TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #vi #license-apache-2.0 #model-index #endpoints_compatible #region-us \n# Wav2Vec2-Large-XLSR-53-Vietnamese\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Vietnamese using the Common Voice, FOSD and VIVOS.\nWhen using this model, make sure that your speech input is sampled at 16kHz.## Usage\nThe model can be used directly (without a language model) as follows:## Evaluation\nThe model can be evaluated as follows on the Vietnamese test data of Common Voice.\n\nTest Result: 49.59 %## Training\nThe Common Voice 'train', 'validation' and FOSD datasets and VIVOS datasets were used for training as well.\nThe script used for training can be found here"
] | [
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null | null | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | text-generation | NibrasShami/DialopGPT-small-HarryPotter | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Harry Potter DialoGPT Model | [
"# Harry Potter DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Harry Potter DialoGPT Model"
] | [
51,
8
] | [
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Harry Potter DialoGPT Model"
] | [
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null | null | null | this project was created to use in wav2vec | {} | null | Niciu/testtest1 | [
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#region-us
| this project was created to use in wav2vec | [] | [
"TAGS\n#region-us \n"
] | [
6
] | [
"passage: TAGS\n#region-us \n"
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null | null | transformers |
# My Awesome Laffy | {"tags": ["conversational"]} | text-generation | NickCavarretta/DialoGPT-small-laffy | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# My Awesome Laffy | [
"# My Awesome Laffy"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# My Awesome Laffy"
] | [
51,
5
] | [
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# My Awesome Laffy"
] | [
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