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sentence-similarity | sentence-transformers |
# valurank/paraphrase-mpnet-base-v2-offensive
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Usin... | {"license": "other", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | valurank/paraphrase-mpnet-base-v2-offensive | null | [
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"transformers",
"license:other",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #mpnet #feature-extraction #sentence-similarity #transformers #license-other #endpoints_compatible #region-us
|
# valurank/paraphrase-mpnet-base-v2-offensive
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transforme... | [
"# valurank/paraphrase-mpnet-base-v2-offensive\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-... | [
"TAGS\n#sentence-transformers #pytorch #mpnet #feature-extraction #sentence-similarity #transformers #license-other #endpoints_compatible #region-us \n",
"# valurank/paraphrase-mpnet-base-v2-offensive\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space ... |
question-answering | 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-uncased-fiqa-flm-sq-flit
This model is a fine-tuned version of bert-base-uncased on a custom dataset created for ques... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "bert-base-uncased-fiqa-flm-sq-flit", "results": []}]} | vanadhi/bert-base-uncased-fiqa-flm-sq-flit | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #question-answering #generated_from_trainer #endpoints_compatible #region-us
|
# bert-base-uncased-fiqa-flm-sq-flit
This model is a fine-tuned version of bert-base-uncased on a custom dataset created for question answering in
financial domain.
## Model description
BERT is a transformers model pretrained on a large corpus of English data in a self-supervised fashion.
The model was further... | [
"# bert-base-uncased-fiqa-flm-sq-flit\n\n\nThis model is a fine-tuned version of bert-base-uncased on a custom dataset created for question answering in \nfinancial domain.",
"## Model description\n\nBERT is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. \nThe mode... | [
"TAGS\n#transformers #pytorch #bert #question-answering #generated_from_trainer #endpoints_compatible #region-us \n",
"# bert-base-uncased-fiqa-flm-sq-flit\n\n\nThis model is a fine-tuned version of bert-base-uncased on a custom dataset created for question answering in \nfinancial domain.",
"## Model descripti... |
question-answering | 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. -->
# roberta-base-fiqa-flm-sq-flit
This model is a fine-tuned version of roberta-base on a custom dataset create for question answeri... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "roberta-base-fiqa-flm-sq-flit", "results": []}]} | vanadhi/roberta-base-fiqa-flm-sq-flit | null | [
"transformers",
"pytorch",
"roberta",
"question-answering",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #question-answering #generated_from_trainer #endpoints_compatible #region-us
|
# roberta-base-fiqa-flm-sq-flit
This model is a fine-tuned version of roberta-base on a custom dataset create for question answering in
financial domain.
## Model description
RoBERTa is a transformers model pretrained on a large corpus of English data in a self-supervised fashion.
The model was further processe... | [
"# roberta-base-fiqa-flm-sq-flit\n\nThis model is a fine-tuned version of roberta-base on a custom dataset create for question answering in \nfinancial domain.",
"## Model description\n\nRoBERTa is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. \nThe model was furt... | [
"TAGS\n#transformers #pytorch #roberta #question-answering #generated_from_trainer #endpoints_compatible #region-us \n",
"# roberta-base-fiqa-flm-sq-flit\n\nThis model is a fine-tuned version of roberta-base on a custom dataset create for question answering in \nfinancial domain.",
"## Model description\n\nRoBE... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Estonian
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Estonian 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 c... | {"language": "et", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice", "NST Estonian ASR Database"], "metrics": ["wer", "cer"], "model-index": [{"name": "XLSR Wav2Vec2 Large 53 - Estonian by Vasilis", "results": [{"task": {"type": "... | vasilis/wav2vec2-large-xlsr-53-estonian | null | [
"transformers",
"pytorch",
"wav2vec2",
"audio",
"automatic-speech-recognition",
"speech",
"xlsr-fine-tuning-week",
"et",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"et"
] | TAGS
#transformers #pytorch #wav2vec2 #audio #automatic-speech-recognition #speech #xlsr-fine-tuning-week #et #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-Estonian
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Estonian 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 a... | [
"# Wav2Vec2-Large-XLSR-53-Estonian\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Estonian using the Common Voice.\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 ca... | [
"TAGS\n#transformers #pytorch #wav2vec2 #audio #automatic-speech-recognition #speech #xlsr-fine-tuning-week #et #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Estonian\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Estonian using the Common Voice.\nWhen using th... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-finnish
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on finnish using the [Common Voice](https://huggingface.co/datasets/common_voice) and [CSS10 finnish: Single Speaker Speech Dataset](https://www.kaggle.com/bryanpark/finnish-single-sp... | {"language": "fi", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice", {"CSS10 finnish": "Single Speaker Speech Dataset"}], "metrics": ["wer", "cer"], "model-index": [{"name": "V XLSR Wav2Vec2 Large 53 - finnish", "results": [{"task... | vasilis/wav2vec2-large-xlsr-53-finnish | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"fi",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fi"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #fi #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-finnish
Fine-tuned facebook/wav2vec2-large-xlsr-53 on finnish using the Common Voice and CSS10 finnish: Single Speaker Speech Dataset.
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:... | [
"# Wav2Vec2-Large-XLSR-53-finnish\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on finnish using the Common Voice and CSS10 finnish: Single Speaker Speech Dataset.\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... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #fi #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-finnish\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on finnish using the Common Voice and CSS10 finnis... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-greek
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on greek using the [Common Voice](https://huggingface.co/datasets/common_voice) and [CSS10 Greek: Single Speaker Speech Dataset](https://www.kaggle.com/bryanpark/greek-single-speaker-sp... | {"language": "el", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice", {"CSS10 Greek": "Single Speaker Speech Dataset"}], "metrics": ["wer", "cer"], "model-index": [{"name": "V XLSR Wav2Vec2 Large 53 - greek", "results": [{"task": {... | vasilis/wav2vec2-large-xlsr-53-greek | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"el",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"el"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #el #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-greek
Fine-tuned facebook/wav2vec2-large-xlsr-53 on greek using the Common Voice and CSS10 Greek: Single Speaker Speech Dataset.
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:
#... | [
"# Wav2Vec2-Large-XLSR-53-greek\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on greek using the Common Voice and CSS10 Greek: Single Speaker Speech Dataset.\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 f... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #el #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-greek\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on greek using the Common Voice and CSS10 Greek: Sin... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Swedish
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Swedish using the [Common Voice](https://huggingface.co/datasets/common_voice) and parts for the [NST Swedish ASR Database](https://www.nb.no/sprakbanken/en/resource-catalogue/oai-... | {"language": "sv-SE", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice", "NST Swedish ASR Database"], "metrics": ["wer", "cer"], "model-index": [{"name": "V XLSR Wav2Vec2 Large 53 - Swedish", "results": [{"task": {"type": "automati... | vasilis/wav2vec2-large-xlsr-53-swedish | null | [
"transformers",
"pytorch",
"wav2vec2",
"audio",
"automatic-speech-recognition",
"speech",
"xlsr-fine-tuning-week",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"sv-SE"
] | TAGS
#transformers #pytorch #wav2vec2 #audio #automatic-speech-recognition #speech #xlsr-fine-tuning-week #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-Swedish
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Swedish using the Common Voice and parts for the NST Swedish ASR Database.
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:
#... | [
"# Wav2Vec2-Large-XLSR-53-Swedish\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Swedish using the Common Voice and parts for the NST Swedish ASR Database.\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 f... | [
"TAGS\n#transformers #pytorch #wav2vec2 #audio #automatic-speech-recognition #speech #xlsr-fine-tuning-week #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Swedish\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Swedish using the Common Voice and parts for the NS... |
automatic-speech-recognition | 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. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MO... | {"language": ["et"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "et", "robust-speech-event", "generated_from_trainer", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "XLS-R-1B - Estonian", "results": [{"t... | vasilis/xls-r-et-V-3 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_8_0",
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"generated_from_trainer",
"hf-asr-leaderboard",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"end... | null | 2022-03-02T23:29:05+00:00 | [] | [
"et"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #et #robust-speech-event #generated_from_trainer #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - ET dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8824
* Wer: 0.5246
Model description
-----------------
More information needed
Intended uses & limitations
------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #et #robust-speech-event #generated_from_trainer #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Trai... |
question-answering | transformers | Moved here: https://huggingface.co/google/bigbird-base-trivia-itc | {} | vasudevgupta/bigbird-base-trivia-itc | null | [
"transformers",
"pytorch",
"big_bird",
"question-answering",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #big_bird #question-answering #endpoints_compatible #region-us
| Moved here: URL | [] | [
"TAGS\n#transformers #pytorch #big_bird #question-answering #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers | Moved here: https://huggingface.co/google/bigbird-pegasus-large-arxiv | {} | vasudevgupta/bigbird-pegasus-large-arxiv | null | [
"transformers",
"pytorch",
"bigbird_pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bigbird_pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| Moved here: URL | [] | [
"TAGS\n#transformers #pytorch #bigbird_pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers | Moved here: https://huggingface.co/google/bigbird-pegasus-large-bigpatent | {} | vasudevgupta/bigbird-pegasus-large-bigpatent | null | [
"transformers",
"pytorch",
"bigbird_pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bigbird_pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| Moved here: URL | [] | [
"TAGS\n#transformers #pytorch #bigbird_pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers | Moved here: https://huggingface.co/google/bigbird-pegasus-large-pubmed | {} | vasudevgupta/bigbird-pegasus-large-pubmed | null | [
"transformers",
"pytorch",
"bigbird_pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bigbird_pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| Moved here: URL | [] | [
"TAGS\n#transformers #pytorch #bigbird_pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers | Moved here: https://huggingface.co/google/bigbird-roberta-base | {} | vasudevgupta/bigbird-roberta-base | null | [
"transformers",
"pytorch",
"big_bird",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #big_bird #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| Moved here: URL | [] | [
"TAGS\n#transformers #pytorch #big_bird #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers | Moved here: https://huggingface.co/google/bigbird-roberta-large | {} | vasudevgupta/bigbird-roberta-large | null | [
"transformers",
"pytorch",
"big_bird",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #big_bird #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| Moved here: URL | [] | [
"TAGS\n#transformers #pytorch #big_bird #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] |
question-answering | transformers |
This checkpoint is obtained after training `BigBirdForQuestionAnswering` (with extra pooler head) on [`natural_questions`](https://huggingface.co/datasets/natural_questions) dataset for ~ 2 weeks on 2 K80 GPUs. Script for training can be found here: https://github.com/vasudevgupta7/bigbird
| Exact Match | 47.44 |
|--... | {"language": "en", "license": "apache-2.0", "datasets": "natural_questions", "widget": [{"text": "Who added BigBird to HuggingFace Transformers?", "context": "BigBird Pegasus just landed! Thanks to Vasudev Gupta, BigBird Pegasus from Google AI is merged into HuggingFace Transformers. Check it out today!!!"}]} | vasudevgupta/bigbird-roberta-natural-questions | null | [
"transformers",
"pytorch",
"big_bird",
"question-answering",
"en",
"dataset:natural_questions",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #big_bird #question-answering #en #dataset-natural_questions #license-apache-2.0 #endpoints_compatible #has_space #region-us
| This checkpoint is obtained after training 'BigBirdForQuestionAnswering' (with extra pooler head) on 'natural\_questions' dataset for ~ 2 weeks on 2 K80 GPUs. Script for training can be found here: URL
Use this model just like any other model from Transformers
In case you are interested in predicting category (nul... | [] | [
"TAGS\n#transformers #pytorch #big_bird #question-answering #en #dataset-natural_questions #license-apache-2.0 #endpoints_compatible #has_space #region-us \n"
] |
text-generation | transformers | DL research papers **Title -> abstract**
**Using this model**
```python
from transformers import pipeline, GPT2LMHeadModel, GPT2Tokenizer
tokenizer = GPT2Tokenizer.from_pretrained("vasudevgupta/dl-hack-distilgpt2")
model = GPT2LMHeadModel.from_pretrained("vasudevgupta/dl-hack-distilgpt2")
agent = pipeline("text-gene... | {} | vasudevgupta/dl-hack-distilgpt2 | null | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| DL research papers Title -> abstract
Using this model
| [] | [
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers | DL research papers **Title -> abstract**
**Using this model**
```python
from transformers import pipeline, GPT2LMHeadModel, GPT2Tokenizer
tokenizer = GPT2Tokenizer.from_pretrained("vasudevgupta/dl-hack-gpt2-large")
model = GPT2LMHeadModel.from_pretrained("vasudevgupta/dl-hack-gpt2-large")
agent = pipeline("text-gene... | {} | vasudevgupta/dl-hack-gpt2-large | null | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| DL research papers Title -> abstract
Using this model
| [] | [
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text2text-generation | transformers | Deep Learning research papers **Title -> abstract** | {} | vasudevgupta/dl-hack-pegasus-large | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| Deep Learning research papers Title -> abstract | [] | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
null | transformers | # finetuned-wav2vec2-960h
This model was trained as a part of my **GSoC'21 (Google Summer of Code)** project. It is fine-tuned on 960h of **LibriSpeech dataset** (`train-clean-100`, `train-clean-360`, `train-other-500`) and evaluated on `test-clean` data.
| WER (word error rate) | 5.67 |
|-----------------------|----... | {} | vasudevgupta/finetuned-wav2vec2-960h | null | [
"transformers",
"tf",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #tf #endpoints_compatible #region-us
| finetuned-wav2vec2-960h
=======================
This model was trained as a part of my GSoC'21 (Google Summer of Code) project. It is fine-tuned on 960h of LibriSpeech dataset ('train-clean-100', 'train-clean-360', 'train-other-500') and evaluated on 'test-clean' data.
You can find code for training here: URL
| [] | [
"TAGS\n#transformers #tf #endpoints_compatible #region-us \n"
] |
null | transformers |
This checkpoint is obtained after training `FlaxBigBirdForQuestionAnswering` (with extra pooler head) on [`natural_questions`](https://huggingface.co/datasets/natural_questions) dataset on TPU v3-8. This dataset takes around ~100 GB on disk. But thanks to Cloud TPUs and Jax, each epoch took just 4.5 hours. Script for ... | {"language": "en", "license": "apache-2.0", "datasets": "natural_questions", "widget": [{"text": "Who added BigBird to HuggingFace Transformers?", "context": "BigBird Pegasus just landed! Thanks to Vasudev Gupta, BigBird Pegasus from Google AI is merged into HuggingFace Transformers. Check it out today!!!"}]} | vasudevgupta/flax-bigbird-natural-questions | null | [
"transformers",
"jax",
"big_bird",
"en",
"dataset:natural_questions",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #jax #big_bird #en #dataset-natural_questions #license-apache-2.0 #endpoints_compatible #has_space #region-us
| This checkpoint is obtained after training 'FlaxBigBirdForQuestionAnswering' (with extra pooler head) on 'natural\_questions' dataset on TPU v3-8. This dataset takes around ~100 GB on disk. But thanks to Cloud TPUs and Jax, each epoch took just 4.5 hours. Script for training can be found here: URL
Use this model just... | [] | [
"TAGS\n#transformers #jax #big_bird #en #dataset-natural_questions #license-apache-2.0 #endpoints_compatible #has_space #region-us \n"
] |
null | transformers | TensorFlow version of [facebook/wav2vec2-base-960h](https://huggingface.co/facebook/wav2vec2-base-960h). Obtained using script from https://github.com/vasudevgupta7/gsoc-wav2vec2. | {} | vasudevgupta/gsoc-wav2vec2-960h | null | [
"transformers",
"tf",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #tf #endpoints_compatible #has_space #region-us
| TensorFlow version of facebook/wav2vec2-base-960h. Obtained using script from URL | [] | [
"TAGS\n#transformers #tf #endpoints_compatible #has_space #region-us \n"
] |
null | transformers | TensorFlow equivalent of [facebook/wav2vec2-large-robust](https://huggingface.co/facebook/wav2vec2-large-robust) | {} | vasudevgupta/gsoc-wav2vec2-robust | null | [
"transformers",
"tf",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #tf #endpoints_compatible #region-us
| TensorFlow equivalent of facebook/wav2vec2-large-robust | [] | [
"TAGS\n#transformers #tf #endpoints_compatible #region-us \n"
] |
null | transformers | TensorFlow equivalent of [`facebook/wav2vec2-large-xlsr-53`](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) | {} | vasudevgupta/gsoc-wav2vec2-xlsr-53 | null | [
"transformers",
"tf",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #tf #endpoints_compatible #region-us
| TensorFlow equivalent of 'facebook/wav2vec2-large-xlsr-53' | [] | [
"TAGS\n#transformers #tf #endpoints_compatible #region-us \n"
] |
null | transformers | Wav2Vec2 Model (initialized from [`facebook/wav2vec2-base`](https://huggingface.co/facebook/wav2vec2-base)) with **no** LM head.
Model weights are converted into TensorFlow using following script:
```shell
python3 convert_torch_to_tf.py --hf_model_id "facebook/wav2vec2-base"
```
**TF SavedModel** is obtained by runn... | {} | vasudevgupta/gsoc-wav2vec2 | null | [
"transformers",
"tf",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #tf #endpoints_compatible #region-us
| Wav2Vec2 Model (initialized from 'facebook/wav2vec2-base') with no LM head.
Model weights are converted into TensorFlow using following script:
TF SavedModel is obtained by running following commands:
Project Link: URL
| [] | [
"TAGS\n#transformers #tf #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers |
mBART (a pre-trained model by Facebook) is pre-trained to de-noise multiple languages simultaneously with BART objective.
Checkpoint available in this repository is obtained after fine-tuning `facebook/mbart-large-cc25` on all samples (~60K) from Bhasha (pib_v1.3) Gujarati-English parallel corpus. This checkpoint giv... | {"datasets": "pib", "widget": [{"text": "\u0ab9\u0ac7\u0aaf! \u0ab9\u0ac1\u0a82 \u0ab5\u0abe\u0ab8\u0ac1\u0aa6\u0ac7\u0ab5 \u0a97\u0ac1\u0aaa\u0acd\u0aa4\u0abe \u0a9b\u0ac1\u0a82"}]} | vasudevgupta/mbart-bhasha-guj-eng | null | [
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"dataset:pib",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #mbart #text2text-generation #dataset-pib #autotrain_compatible #endpoints_compatible #region-us
|
mBART (a pre-trained model by Facebook) is pre-trained to de-noise multiple languages simultaneously with BART objective.
Checkpoint available in this repository is obtained after fine-tuning 'facebook/mbart-large-cc25' on all samples (~60K) from Bhasha (pib_v1.3) Gujarati-English parallel corpus. This checkpoint giv... | [] | [
"TAGS\n#transformers #pytorch #mbart #text2text-generation #dataset-pib #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers |
mBART (a pre-trained model by Facebook) is pre-trained to de-noise multiple languages simultaneously with BART objective.
Checkpoint available in this repository is obtained after fine-tuning `facebook/mbart-large-cc25` on all samples (~260K) from Bhasha (pib_v1.3) Hindi-English parallel corpus. This checkpoint gives... | {"datasets": "pib", "widget": [{"text": "\u0928\u092e\u0938\u094d\u0924\u0947! \u092e\u0948\u0902 \u0935\u093e\u0938\u0941\u0926\u0947\u0935 \u0917\u0941\u092a\u094d\u0924\u093e \u0939\u0942\u0902"}]} | vasudevgupta/mbart-bhasha-hin-eng | null | [
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"dataset:pib",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #mbart #text2text-generation #dataset-pib #autotrain_compatible #endpoints_compatible #has_space #region-us
|
mBART (a pre-trained model by Facebook) is pre-trained to de-noise multiple languages simultaneously with BART objective.
Checkpoint available in this repository is obtained after fine-tuning 'facebook/mbart-large-cc25' on all samples (~260K) from Bhasha (pib_v1.3) Hindi-English parallel corpus. This checkpoint gives... | [] | [
"TAGS\n#transformers #pytorch #mbart #text2text-generation #dataset-pib #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
text2text-generation | transformers |
mBART (a pre-trained model by Facebook) is pre-trained to de-noise multiple languages simultaneously with BART objective.
Checkpoint available in this repository is obtained after fine-tuning `facebook/mbart-large-cc25` on 0.5 M samples from IIT-B Hindi-English parallel corpus. This checkpoint gives decent results fo... | {"datasets": "pib", "widget": [{"text": "\u0928\u092e\u0938\u094d\u0924\u0947! \u092e\u0948\u0902 \u0935\u093e\u0938\u0941\u0926\u0947\u0935 \u0917\u0941\u092a\u094d\u0924\u093e \u0939\u0942\u0902"}]} | vasudevgupta/mbart-iitb-hin-eng | null | [
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"dataset:pib",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #mbart #text2text-generation #dataset-pib #autotrain_compatible #endpoints_compatible #region-us
|
mBART (a pre-trained model by Facebook) is pre-trained to de-noise multiple languages simultaneously with BART objective.
Checkpoint available in this repository is obtained after fine-tuning 'facebook/mbart-large-cc25' on 0.5 M samples from IIT-B Hindi-English parallel corpus. This checkpoint gives decent results fo... | [] | [
"TAGS\n#transformers #pytorch #mbart #text2text-generation #dataset-pib #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers | This model is trained as a part of **InterIIT'21 competition**, on the dataset provided by Bridgei2i. It is able to do multilingual (Hindi, English, Hinglish) summarization (many -> one) & is capable of generating summaries in English regardless of the input language.
| Rouge-L | Sacrebleu | Headline Sim... | {} | vasudevgupta/mbart-summarizer-interiit | null | [
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #mbart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| This model is trained as a part of InterIIT'21 competition, on the dataset provided by Bridgei2i. It is able to do multilingual (Hindi, English, Hinglish) summarization (many -> one) & is capable of generating summaries in English regardless of the input language.
Rouge-L: p=0.46 r=0.49 f1=0.52, Sacrebleu: 23.46, Hea... | [] | [
"TAGS\n#transformers #pytorch #mbart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
null | null | **Project GitHub:** https://github.com/vasudevgupta7/transformers-adapters
**Notes**
* base model can be downloaded from `facebook/mbart-large-cc25`
* `adapters-hin-eng.pt`: adapters hin-eng
* `adapters-guj-eng.pt`: adapters guj-eng
| {} | vasudevgupta/offnote-mbart-adapters-bhasha | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| Project GitHub: URL
Notes
* base model can be downloaded from 'facebook/mbart-large-cc25'
* 'URL': adapters hin-eng
* 'URL': adapters guj-eng
| [] | [
"TAGS\n#region-us \n"
] |
null | transformers | TensorFlow version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base). Obtained using script from https://github.com/vasudevgupta7/gsoc-wav2vec2. | {} | vasudevgupta/tf-wav2vec2-base | null | [
"transformers",
"tf",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #tf #endpoints_compatible #region-us
| TensorFlow version of facebook/wav2vec2-base. Obtained using script from URL | [] | [
"TAGS\n#transformers #tf #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers |
## Introduction
See [blog post](https://towardsdatascience.com/long-form-qa-beyond-eli5-an-updated-dataset-and-approach-319cb841aabb) for more details.
## Usage
```python
import torch
from transformers import AutoTokenizer, AutoModel, AutoModelForSeq2SeqLM
model_name = "vblagoje/bart_lfqa"
device = torch.device('cu... | {"language": "en", "license": "mit", "datasets": ["vblagoje/lfqa", "vblagoje/lfqa_support_docs"]} | vblagoje/bart_lfqa | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"en",
"dataset:vblagoje/lfqa",
"dataset:vblagoje/lfqa_support_docs",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #en #dataset-vblagoje/lfqa #dataset-vblagoje/lfqa_support_docs #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
|
## Introduction
See blog post for more details.
## Usage
## Author
- Vladimir Blagojevic: 'dovlex [at] URL' Twitter | LinkedIn | [
"## Introduction\nSee blog post for more details.",
"## Usage",
"## Author\n- Vladimir Blagojevic: 'dovlex [at] URL' Twitter | LinkedIn"
] | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #en #dataset-vblagoje/lfqa #dataset-vblagoje/lfqa_support_docs #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## Introduction\nSee blog post for more details.",
"## Usage",
"## Author\n- Vladimir Blagojevic: 'dovle... |
null | transformers |
## Introduction
The context/passage encoder model based on [DPRContextEncoder](https://huggingface.co/docs/transformers/master/en/model_doc/dpr#transformers.DPRContextEncoder) architecture. It uses the transformer's pooler outputs as context/passage representations.
## Training
We trained vblagoje/dpr-ctx_encoder-si... | {"language": "en", "license": "mit", "datasets": ["vblagoje/lfqa"]} | vblagoje/dpr-ctx_encoder-single-lfqa-base | null | [
"transformers",
"pytorch",
"dpr",
"en",
"dataset:vblagoje/lfqa",
"license:mit",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #dpr #en #dataset-vblagoje/lfqa #license-mit #endpoints_compatible #has_space #region-us
|
## Introduction
The context/passage encoder model based on DPRContextEncoder architecture. It uses the transformer's pooler outputs as context/passage representations.
## Training
We trained vblagoje/dpr-ctx_encoder-single-lfqa-base using FAIR's dpr-scale starting with PAQ based pretrained checkpoint and fine-tuned ... | [
"## Introduction\nThe context/passage encoder model based on DPRContextEncoder architecture. It uses the transformer's pooler outputs as context/passage representations.",
"## Training\nWe trained vblagoje/dpr-ctx_encoder-single-lfqa-base using FAIR's dpr-scale starting with PAQ based pretrained checkpoint and fi... | [
"TAGS\n#transformers #pytorch #dpr #en #dataset-vblagoje/lfqa #license-mit #endpoints_compatible #has_space #region-us \n",
"## Introduction\nThe context/passage encoder model based on DPRContextEncoder architecture. It uses the transformer's pooler outputs as context/passage representations.",
"## Training\nWe... |
null | transformers |
## Introduction
The context/passage encoder model based on [DPRContextEncoder](https://huggingface.co/docs/transformers/master/en/model_doc/dpr#transformers.DPRContextEncoder) architecture. It uses the transformer's pooler outputs as context/passage representations. See [blog post](https://towardsdatascience.com/long-... | {"language": "en", "license": "mit", "datasets": ["vblagoje/lfqa"]} | vblagoje/dpr-ctx_encoder-single-lfqa-wiki | null | [
"transformers",
"pytorch",
"dpr",
"en",
"dataset:vblagoje/lfqa",
"license:mit",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #dpr #en #dataset-vblagoje/lfqa #license-mit #endpoints_compatible #has_space #region-us
|
## Introduction
The context/passage encoder model based on DPRContextEncoder architecture. It uses the transformer's pooler outputs as context/passage representations. See blog post for more details.
## Training
We trained vblagoje/dpr-ctx_encoder-single-lfqa-wiki using FAIR's dpr-scale in two stages. In the first st... | [
"## Introduction\nThe context/passage encoder model based on DPRContextEncoder architecture. It uses the transformer's pooler outputs as context/passage representations. See blog post for more details.",
"## Training\nWe trained vblagoje/dpr-ctx_encoder-single-lfqa-wiki using FAIR's dpr-scale in two stages. In th... | [
"TAGS\n#transformers #pytorch #dpr #en #dataset-vblagoje/lfqa #license-mit #endpoints_compatible #has_space #region-us \n",
"## Introduction\nThe context/passage encoder model based on DPRContextEncoder architecture. It uses the transformer's pooler outputs as context/passage representations. See blog post for mo... |
feature-extraction | transformers |
## Introduction
The question encoder model based on [DPRQuestionEncoder](https://huggingface.co/docs/transformers/master/en/model_doc/dpr#transformers.DPRQuestionEncoder) architecture. It uses the transformer's pooler outputs as question representations.
## Training
We trained vblagoje/dpr-question_encoder-single-lf... | {"language": "en", "license": "mit", "datasets": ["vblagoje/lfqa"]} | vblagoje/dpr-question_encoder-single-lfqa-base | null | [
"transformers",
"pytorch",
"dpr",
"feature-extraction",
"en",
"dataset:vblagoje/lfqa",
"license:mit",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #dpr #feature-extraction #en #dataset-vblagoje/lfqa #license-mit #endpoints_compatible #has_space #region-us
|
## Introduction
The question encoder model based on DPRQuestionEncoder architecture. It uses the transformer's pooler outputs as question representations.
## Training
We trained vblagoje/dpr-question_encoder-single-lfqa-base using FAIR's dpr-scale starting with PAQ based pretrained checkpoint and fine-tuned the retr... | [
"## Introduction\nThe question encoder model based on DPRQuestionEncoder architecture. It uses the transformer's pooler outputs as question representations.",
"## Training\nWe trained vblagoje/dpr-question_encoder-single-lfqa-base using FAIR's dpr-scale starting with PAQ based pretrained checkpoint and fine-tuned... | [
"TAGS\n#transformers #pytorch #dpr #feature-extraction #en #dataset-vblagoje/lfqa #license-mit #endpoints_compatible #has_space #region-us \n",
"## Introduction\nThe question encoder model based on DPRQuestionEncoder architecture. It uses the transformer's pooler outputs as question representations.",
"## Train... |
feature-extraction | transformers |
## Introduction
The question encoder model based on [DPRQuestionEncoder](https://huggingface.co/docs/transformers/master/en/model_doc/dpr#transformers.DPRQuestionEncoder) architecture. It uses the transformer's pooler outputs as question representations. See [blog post](https://towardsdatascience.com/long-form-qa-beyo... | {"language": "en", "license": "mit", "datasets": ["vblagoje/lfqa"]} | vblagoje/dpr-question_encoder-single-lfqa-wiki | null | [
"transformers",
"pytorch",
"dpr",
"feature-extraction",
"en",
"dataset:vblagoje/lfqa",
"license:mit",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #dpr #feature-extraction #en #dataset-vblagoje/lfqa #license-mit #endpoints_compatible #has_space #region-us
|
## Introduction
The question encoder model based on DPRQuestionEncoder architecture. It uses the transformer's pooler outputs as question representations. See blog post for more details.
## Training
We trained vblagoje/dpr-question_encoder-single-lfqa-wiki using FAIR's dpr-scale in two stages. In the first stage, we... | [
"## Introduction\nThe question encoder model based on DPRQuestionEncoder architecture. It uses the transformer's pooler outputs as question representations. See blog post for more details.",
"## Training\nWe trained vblagoje/dpr-question_encoder-single-lfqa-wiki using FAIR's dpr-scale in two stages. In the first ... | [
"TAGS\n#transformers #pytorch #dpr #feature-extraction #en #dataset-vblagoje/lfqa #license-mit #endpoints_compatible #has_space #region-us \n",
"## Introduction\nThe question encoder model based on DPRQuestionEncoder architecture. It uses the transformer's pooler outputs as question representations. See blog post... |
automatic-speech-recognition | 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-timit-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-colab", "results": []}]} | vdivya/wav2vec2-base-timit-demo-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-timit-demo-colab
==============================
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4630
* Wer: 0.3399
Model description
-----------------
More information needed
Intended uses & limi... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\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* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #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: 0.0001\n* train\\_batch\\_size: 3... |
image-classification | transformers |
# hugging-doge
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/hugging... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | velociraptor/hugging-doge | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# hugging-doge
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
#### corgi
!corgi
#### golden retriever
!golden retriever
#### husky
!husky
#### poodle
!poodle
#### shib... | [
"# hugging-doge\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",
"#### corgi\n\n!corgi",
"#### golden retriever\n\n!golden retriever",
"#### husky\n\n!husky",... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# hugging-doge\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues ... |
text2text-generation | transformers |
# T5 Grammar Correction
This model generates a revised version of inputted text with the goal of containing fewer grammatical errors.
It was trained with [Happy Transformer](https://github.com/EricFillion/happy-transformer)
using a dataset called [JFLEG](https://arxiv.org/abs/1702.04066). Here's a [full article](ht... | {"language": "en", "license": "cc-by-nc-sa-4.0", "tags": ["grammar", "text2text-generation"], "datasets": ["jfleg"]} | vennify/t5-base-grammar-correction | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"grammar",
"en",
"dataset:jfleg",
"arxiv:1702.04066",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1702.04066"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #grammar #en #dataset-jfleg #arxiv-1702.04066 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# T5 Grammar Correction
This model generates a revised version of inputted text with the goal of containing fewer grammatical errors.
It was trained with Happy Transformer
using a dataset called JFLEG. Here's a full article on how to train a similar model.
## Usage
'pip install happytransformer '
| [
"# T5 Grammar Correction \n\nThis model generates a revised version of inputted text with the goal of containing fewer grammatical errors. \nIt was trained with Happy Transformer\nusing a dataset called JFLEG. Here's a full article on how to train a similar model.",
"## Usage \n\n'pip install happytransformer '"
... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #grammar #en #dataset-jfleg #arxiv-1702.04066 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# T5 Grammar Correction \n\nThis model generates a revised version of inputted text with... |
fill-mask | transformers | ### Twitter RoBERTa BR
This is a RoBERTa Twitter in Portuguese model trained on ~7M tweets.
The results will be posted in the future.
### Example of using
```
tokenizer = AutoTokenizer.from_pretrained("verissimomanoel/RobertaTwitterBR")
model = AutoModel.from_pretrained("verissimomanoel/RobertaTwitterBR")
```
| {} | verissimomanoel/RobertaTwitterBR | null | [
"transformers",
"pytorch",
"jax",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| ### Twitter RoBERTa BR
This is a RoBERTa Twitter in Portuguese model trained on ~7M tweets.
The results will be posted in the future.
### Example of using
| [
"### Twitter RoBERTa BR\nThis is a RoBERTa Twitter in Portuguese model trained on ~7M tweets.\nThe results will be posted in the future.",
"### Example of using"
] | [
"TAGS\n#transformers #pytorch #jax #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"### Twitter RoBERTa BR\nThis is a RoBERTa Twitter in Portuguese model trained on ~7M tweets.\nThe results will be posted in the future.",
"### Example of using"
] |
question-answering | transformers | # QA-for-Event-Extraction
## Model description
This is a QA model as part of the event extraction system in the ACL2021 paper: [Zero-shot Event Extraction via Transfer Learning: Challenges and Insights](https://aclanthology.org/2021.acl-short.42/). The pretrained architecture is [roberta-large](https://huggingface.co... | {} | veronica320/QA-for-Event-Extraction | null | [
"transformers",
"pytorch",
"roberta",
"question-answering",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #question-answering #endpoints_compatible #region-us
| # QA-for-Event-Extraction
## Model description
This is a QA model as part of the event extraction system in the ACL2021 paper: Zero-shot Event Extraction via Transfer Learning: Challenges and Insights. The pretrained architecture is roberta-large and the fine-tuning data is QAMR.
## Demo
To see how the model works,... | [
"# QA-for-Event-Extraction",
"## Model description\n\nThis is a QA model as part of the event extraction system in the ACL2021 paper: Zero-shot Event Extraction via Transfer Learning: Challenges and Insights. The pretrained architecture is roberta-large and the fine-tuning data is QAMR.",
"## Demo\nTo see how t... | [
"TAGS\n#transformers #pytorch #roberta #question-answering #endpoints_compatible #region-us \n",
"# QA-for-Event-Extraction",
"## Model description\n\nThis is a QA model as part of the event extraction system in the ACL2021 paper: Zero-shot Event Extraction via Transfer Learning: Challenges and Insights. The pr... |
text-classification | transformers | # TE-for-Event-Extraction
## Model description
This is a TE model as part of the event extraction system in the ACL2021 paper: [Zero-shot Event Extraction via Transfer Learning: Challenges and Insights](https://aclanthology.org/2021.acl-short.42/). The pretrained architecture is [roberta-large](https://huggingface.co... | {} | veronica320/TE-for-Event-Extraction | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us
| # TE-for-Event-Extraction
## Model description
This is a TE model as part of the event extraction system in the ACL2021 paper: Zero-shot Event Extraction via Transfer Learning: Challenges and Insights. The pretrained architecture is roberta-large and the fine-tuning data is MNLI.
The label mapping is:
## Demo
To ... | [
"# TE-for-Event-Extraction",
"## Model description\n\nThis is a TE model as part of the event extraction system in the ACL2021 paper: Zero-shot Event Extraction via Transfer Learning: Challenges and Insights. The pretrained architecture is roberta-large and the fine-tuning data is MNLI.\n\nThe label mapping is:",... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# TE-for-Event-Extraction",
"## Model description\n\nThis is a TE model as part of the event extraction system in the ACL2021 paper: Zero-shot Event Extraction via Transfer Learning: Challeng... |
text2text-generation | 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. -->
# byt5-base-finetuned-modernisa
This model is a fine-tuned version of [google/byt5-base](https://huggingface.co/google/byt5-base) ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["versae/modernisa"], "metrics": ["bleu"], "base_model": "google/byt5-base", "model-index": [{"name": "byt5-base-finetuned-modernisa", "results": []}]} | versae/byt5-base-finetuned-modernisa | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:versae/modernisa",
"base_model:google/byt5-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"reg... | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #dataset-versae/modernisa #base_model-google/byt5-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| byt5-base-finetuned-modernisa
=============================
This model is a fine-tuned version of google/byt5-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1176
* Bleu: 44.888
* Gen Len: 18.4465
Model description
-----------------
More information needed
Intend... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\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: 3",
"### Trainin... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #dataset-versae/modernisa #base_model-google/byt5-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\n... |
text2text-generation | 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. -->
# mt5-base-finetuned-modernisa
This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["versae/modernisa"], "metrics": ["bleu"], "model-index": [{"name": "mt5-base-finetuned-modernisa", "results": []}]} | versae/mt5-base-finetuned-modernisa | null | [
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"generated_from_trainer",
"dataset:versae/modernisa",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #dataset-versae/modernisa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| mt5-base-finetuned-modernisa
============================
This model is a fine-tuned version of google/mt5-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3179
* Bleu: 81.9164
* Gen Len: 11.1876
Model description
-----------------
More information needed
Intended... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\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: 3",
"### Trainin... | [
"TAGS\n#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #dataset-versae/modernisa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during train... |
null | transformers |
# MS Marco Ranking with ColBERT on Vespa.ai
Model is based on [ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT](https://arxiv.org/abs/2004.12832).
This BERT model is based on [cross-encoder/ms-marco-MiniLM-L-6-v2](https://huggingface.co/cross-encoder/ms-marco-MiniLM-L-6... | {"language": ["en"], "license": "mit", "tags": ["search", "ranking", "vespa"], "base_model": "cross-encoder/ms-marco-MiniLM-L-6-v2"} | vespa-engine/col-minilm | null | [
"transformers",
"pytorch",
"onnx",
"bert",
"search",
"ranking",
"vespa",
"en",
"arxiv:2004.12832",
"base_model:cross-encoder/ms-marco-MiniLM-L-6-v2",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.12832"
] | [
"en"
] | TAGS
#transformers #pytorch #onnx #bert #search #ranking #vespa #en #arxiv-2004.12832 #base_model-cross-encoder/ms-marco-MiniLM-L-6-v2 #license-mit #endpoints_compatible #region-us
| MS Marco Ranking with ColBERT on URL
====================================
Model is based on ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT.
This BERT model is based on cross-encoder/ms-marco-MiniLM-L-6-v2 and trained using the
original ColBERT training routine.
This mo... | [] | [
"TAGS\n#transformers #pytorch #onnx #bert #search #ranking #vespa #en #arxiv-2004.12832 #base_model-cross-encoder/ms-marco-MiniLM-L-6-v2 #license-mit #endpoints_compatible #region-us \n"
] |
null | transformers | # MS Marco Ranking with ColBERT on Vespa.ai
Model is based on [ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT](https://arxiv.org/abs/2004.12832).
This BERT model is based on [google/bert_uncased_L-8_H-512_A-8](https://huggingface.co/google/bert_uncased_L-8_H-512_A-8) an... | {} | vespa-engine/colbert-medium | null | [
"transformers",
"pytorch",
"bert",
"arxiv:2004.12832",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.12832"
] | [] | TAGS
#transformers #pytorch #bert #arxiv-2004.12832 #endpoints_compatible #region-us
| MS Marco Ranking with ColBERT on URL
====================================
Model is based on ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT.
This BERT model is based on google/bert\_uncased\_L-8\_H-512\_A-8 and trained using the
original ColBERT training routine.
The mode... | [] | [
"TAGS\n#transformers #pytorch #bert #arxiv-2004.12832 #endpoints_compatible #region-us \n"
] |
question-answering | transformers | ----
language:
- is
thumbnail:
tags:
- icelandic
- qa
license:
datasets:
- ic3
- igc
metrics:
- em
- f1
widget:
- text: "Hvenær var Halldór Laxness í menntaskóla ?"
context: "Halldór Laxness ( Halldór Kiljan ) fæddist í Reykjavík 23. apríl árið 1902 og átti í fyrstu heima við Laugaveg en árið 1905 settist fjölskyld... | {} | vesteinn/IceBERT-QA | null | [
"transformers",
"pytorch",
"roberta",
"question-answering",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #question-answering #endpoints_compatible #region-us
| ----
language:
- is
thumbnail:
tags:
- icelandic
- qa
license:
datasets:
- ic3
- igc
metrics:
- em
- f1
widget:
- text: "Hvenær var Halldór Laxness í menntaskóla ?"
context: "Halldór Laxness ( Halldór Kiljan ) fæddist í Reykjavík 23. apríl árið 1902 og átti í fyrstu heima við Laugaveg en árið 1905 settist fjölskyld... | [
"# IceBERT-QA",
"## Model description\n\nThis is an Icelandic reading comprehension Q&A model.",
"## Intended uses & limitations\n\nThis model is part of my MSc thesis about Q&A for Icelandic.",
"#### How to use",
"#### Limitations and bias",
"## Training data\nTranslated English datasets were used along ... | [
"TAGS\n#transformers #pytorch #roberta #question-answering #endpoints_compatible #region-us \n",
"# IceBERT-QA",
"## Model description\n\nThis is an Icelandic reading comprehension Q&A model.",
"## Intended uses & limitations\n\nThis model is part of my MSc thesis about Q&A for Icelandic.",
"#### How to use... |
text-classification | 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. -->
# IceBERT-finetuned-iec-sentence-bs16
This model is a fine-tuned version of [vesteinn/IceBERT](https://huggingface.co/vesteinn/Ice... | {"license": "gpl-3.0", "tags": ["generated_from_trainer"], "metrics": ["matthews_correlation"], "base_model": "vesteinn/IceBERT", "model-index": [{"name": "IceBERT-finetuned-iec-sentence-bs16", "results": []}]} | vesteinn/IceBERT-finetuned-iec-sentence-bs16 | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:vesteinn/IceBERT",
"license:gpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #text-classification #generated_from_trainer #base_model-vesteinn/IceBERT #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us
| IceBERT-finetuned-iec-sentence-bs16
===================================
This model is a fine-tuned version of vesteinn/IceBERT on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2508
* Matthews Correlation: 0.8169
Model description
-----------------
More information needed... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #generated_from_trainer #base_model-vesteinn/IceBERT #license-gpl-3.0 #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*... |
text-classification | 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. -->
# IceBERT-finetuned-iec-sentence
This model is a fine-tuned version of [vesteinn/IceBERT](https://huggingface.co/vesteinn/IceBERT)... | {"license": "gpl-3.0", "tags": ["generated_from_trainer"], "metrics": ["matthews_correlation"], "model-index": [{"name": "IceBERT-finetuned-iec-sentence", "results": []}]} | vesteinn/IceBERT-finetuned-iec-sentence | null | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"license:gpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #roberta #text-classification #generated_from_trainer #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us
| IceBERT-finetuned-iec-sentence
==============================
This model is a fine-tuned version of vesteinn/IceBERT on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4438
* Matthews Correlation: 0.6062
Model description
-----------------
More information needed
Intende... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Trai... | [
"TAGS\n#transformers #pytorch #safetensors #roberta #text-classification #generated_from_trainer #license-gpl-3.0 #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\\... |
token-classification | 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. -->
# IceBERT-finetuned-ner
This model is a fine-tuned version of [vesteinn/IceBERT](https://huggingface.co/vesteinn/IceBERT) on the m... | {"license": "gpl-3.0", "tags": ["generated_from_trainer"], "datasets": ["mim_gold_ner"], "metrics": ["precision", "recall", "f1", "accuracy"], "base_model": "vesteinn/IceBERT", "model-index": [{"name": "IceBERT-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "datas... | vesteinn/IceBERT-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"token-classification",
"generated_from_trainer",
"dataset:mim_gold_ner",
"base_model:vesteinn/IceBERT",
"license:gpl-3.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #token-classification #generated_from_trainer #dataset-mim_gold_ner #base_model-vesteinn/IceBERT #license-gpl-3.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| IceBERT-finetuned-ner
=====================
This model is a fine-tuned version of vesteinn/IceBERT on the mim\_gold\_ner dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0815
* Precision: 0.8870
* Recall: 0.8576
* F1: 0.8721
* Accuracy: 0.9848
Model description
-----------------
More ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\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",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #token-classification #generated_from_trainer #dataset-mim_gold_ner #base_model-vesteinn/IceBERT #license-gpl-3.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used ... |
token-classification | 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. -->
# IceBERT-finetuned-ner
This model is a fine-tuned version of [vesteinn/IceBERT](https://huggingface.co/vesteinn/IceBERT) on the m... | {"license": "gpl-3.0", "tags": ["generated_from_trainer"], "datasets": ["mim_gold_ner"], "metrics": ["precision", "recall", "f1", "accuracy"], "widget": [{"text": "Systurnar Gu\u00f0r\u00fan og Monique \u00e1tu einar \u00e1 McDonalds og horf\u00f0u \u00e1 St\u00f6\u00f0 2, \u00fear glitti \u00ed Bruce Willis leika \u00... | vesteinn/IceBERT-ner | null | [
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"license:gpl-3.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
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| IceBERT-finetuned-ner
=====================
This model is a fine-tuned version of vesteinn/IceBERT on the mim\_gold\_ner dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0347
* Precision: 0.9352
* Recall: 0.9440
* F1: 0.9396
* Accuracy: 0.9920
Model description
-----------------
More ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\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",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #roberta #token-classification #generated_from_trainer #dataset-mim_gold_ner #base_model-vesteinn/IceBERT #license-gpl-3.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparamete... |
fill-mask | transformers |
# IceBERT
IceBERT was trained with fairseq using the RoBERTa-base architecture. The training data used is shown in the table below.
| Dataset | Size | Tokens |
|------------------------------------------------------|---------|--------|
| Icelandic Gigaword Corpus v20.0... | {"language": "is", "license": "agpl-3.0", "tags": ["roberta", "icelandic", "masked-lm", "pytorch"], "datasets": ["mideind/icelandic-common-crawl-corpus-IC3"], "widget": [{"text": "M\u00e1 bj\u00f3\u00f0a \u00fe\u00e9r <mask> \u00ed kv\u00f6ld?"}, {"text": "Forseti <mask> er \u00e1g\u00e6t."}, {"text": "S\u00fapan var <... | vesteinn/IceBERT | null | [
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"license:agpl-3.0",
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"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"is"
] | TAGS
#transformers #pytorch #safetensors #roberta #fill-mask #icelandic #masked-lm #is #dataset-mideind/icelandic-common-crawl-corpus-IC3 #license-agpl-3.0 #autotrain_compatible #endpoints_compatible #region-us
| IceBERT
=======
IceBERT was trained with fairseq using the RoBERTa-base architecture. The training data used is shown in the table below.
Dataset: Icelandic Gigaword Corpus v20.05 (IGC), Size: 8.2 GB, Tokens: 1,388M
Dataset: Icelandic Common Crawl Corpus (IC3), Size: 4.9 GB, Tokens: 824M
Dataset: Greynir News artic... | [] | [
"TAGS\n#transformers #pytorch #safetensors #roberta #fill-mask #icelandic #masked-lm #is #dataset-mideind/icelandic-common-crawl-corpus-IC3 #license-agpl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers |
# ScandiBERT
Note note: The model has been updated on 2022-09-27
The model was trained on the data shown in the table below. Batch size was 8.8k, the model was trained for 72 epochs on 24 V100 cards for about 2 weeks.
| Language | Data | Size |
|-----------|----------------------... | {"language": ["is", "da", "sv", "no", "fo"], "license": "agpl-3.0", "tags": ["roberta", "icelandic", "norwegian", "faroese", "danish", "swedish", "masked-lm", "pytorch"], "datasets": ["vesteinn/FC3", "vesteinn/IC3", "mideind/icelandic-common-crawl-corpus-IC3", "NbAiLab/NCC", "DDSC/partial-danish-gigaword-no-twitter"], ... | vesteinn/ScandiBERT | null | [
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"dataset:vesteinn/FC3",
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"dataset:mideind/icelandic-common-crawl-corpus-I... | null | 2022-03-02T23:29:05+00:00 | [] | [
"is",
"da",
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"no",
"fo"
] | TAGS
#transformers #pytorch #safetensors #xlm-roberta #fill-mask #roberta #icelandic #norwegian #faroese #danish #swedish #masked-lm #is #da #sv #no #fo #dataset-vesteinn/FC3 #dataset-vesteinn/IC3 #dataset-mideind/icelandic-common-crawl-corpus-IC3 #dataset-NbAiLab/NCC #dataset-DDSC/partial-danish-gigaword-no-twitter #d... | ScandiBERT
==========
Note note: The model has been updated on 2022-09-27
The model was trained on the data shown in the table below. Batch size was 8.8k, the model was trained for 72 epochs on 24 V100 cards for about 2 weeks.
Language: Icelandic, Data: See IceBERT paper, Size: 16 GB
Language: Danish, Data: Danis... | [] | [
"TAGS\n#transformers #pytorch #safetensors #xlm-roberta #fill-mask #roberta #icelandic #norwegian #faroese #danish #swedish #masked-lm #is #da #sv #no #fo #dataset-vesteinn/FC3 #dataset-vesteinn/IC3 #dataset-mideind/icelandic-common-crawl-corpus-IC3 #dataset-NbAiLab/NCC #dataset-DDSC/partial-danish-gigaword-no-twit... |
text-classification | 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. -->
# XLMR-ENIS-finetuned-cola
This model is a fine-tuned version of [vesteinn/XLMR-ENIS](https://huggingface.co/vesteinn/XLMR-ENIS) o... | {"language": ["en", "is", "multilingual"], "license": "agpl-3.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "base_model": "vesteinn/XLMR-ENIS", "model-index": [{"name": "XLMR-ENIS-finetuned-cola", "results": [{"task": {"type": "text-classification", "name": "Text Cla... | vesteinn/XLMR-ENIS-finetuned-cola | null | [
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"endpoints_compatible",
"re... | null | 2022-03-02T23:29:05+00:00 | [] | [
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] | TAGS
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| XLMR-ENIS-finetuned-cola
========================
This model is a fine-tuned version of vesteinn/XLMR-ENIS on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7311
* Matthews Correlation: 0.6306
Model description
-----------------
More information needed
Intended uses & lim... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #xlm-roberta #text-classification #generated_from_trainer #en #is #multilingual #dataset-glue #base_model-vesteinn/XLMR-ENIS #license-agpl-3.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe fol... |
token-classification | 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. -->
# XLMR-ENIS-finetuned-ner
This model is a fine-tuned version of [vesteinn/XLMR-ENIS](https://huggingface.co/vesteinn/XLMR-ENIS) on... | {"language": ["en", "is", "multilingual"], "license": "agpl-3.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "base_model": "vesteinn/XLMR-ENIS", "model-index": [{"name": "XLMR-ENIS-finetuned-ner", "results": [{"task": {"type": "token-classificati... | vesteinn/XLMR-ENIS-finetuned-ner | null | [
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"autotrain_compatible",
"endpoints_compatible",... | null | 2022-03-02T23:29:05+00:00 | [] | [
"en",
"is",
"multilingual"
] | TAGS
#transformers #pytorch #tensorboard #safetensors #xlm-roberta #token-classification #generated_from_trainer #en #is #multilingual #dataset-conll2003 #base_model-vesteinn/XLMR-ENIS #license-agpl-3.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| XLMR-ENIS-finetuned-ner
=======================
This model is a fine-tuned version of vesteinn/XLMR-ENIS on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0671
* Precision: 0.9398
* Recall: 0.9518
* F1: 0.9458
* Accuracy: 0.9854
Model description
-----------------
More... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\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",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #xlm-roberta #token-classification #generated_from_trainer #en #is #multilingual #dataset-conll2003 #base_model-vesteinn/XLMR-ENIS #license-agpl-3.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nT... |
text-classification | 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. -->
# XLMR-ENIS-finetuned-sst2
This model is a fine-tuned version of [vesteinn/XLMR-ENIS](https://huggingface.co/vesteinn/XLMR-ENIS) o... | {"license": "agpl-3.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "base_model": "vesteinn/XLMR-ENIS", "model-index": [{"name": "XLMR-ENIS-finetuned-sst2", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "gl... | vesteinn/XLMR-ENIS-finetuned-sst2 | null | [
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"model-index",
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"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #dataset-glue #base_model-vesteinn/XLMR-ENIS #license-agpl-3.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| XLMR-ENIS-finetuned-sst2
========================
This model is a fine-tuned version of vesteinn/XLMR-ENIS on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3781
* Accuracy: 0.9278
Model description
-----------------
More information needed
Intended uses & limitations
---... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Traini... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used du... |
sentence-similarity | 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. -->
# XLMR-ENIS-finetuned-stsb
This model is a fine-tuned version of [vesteinn/XLMR-ENIS](https://huggingface.co/vesteinn/XLMR-ENIS) o... | {"license": "agpl-3.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["spearmanr"], "pipeline_tag": "sentence-similarity", "base_model": "vesteinn/XLMR-ENIS", "model-index": [{"name": "XLMR-ENIS-finetuned-stsb", "results": [{"task": {"type": "text-classification", "name": "Text Classification"},... | vesteinn/XLMR-ENIS-finetuned-stsb | null | [
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"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #sentence-similarity #dataset-glue #base_model-vesteinn/XLMR-ENIS #license-agpl-3.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| XLMR-ENIS-finetuned-stsb
========================
This model is a fine-tuned version of vesteinn/XLMR-ENIS on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5232
* Pearson: 0.8915
* Spearmanr: 0.8888
Model description
-----------------
More information needed
Intended use... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
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"### Training hyperparameters\n\n\nThe following hyperpa... |
fill-mask | transformers |
# XLMR-ENIS
This is a XLMR model trained on Icelandic and English text.
If you find this model useful please cite
```bibtex
@inproceedings{snaebjarnarson-einarsson-2022-cross,
title = "Cross-Lingual {QA} as a Stepping Stone for Monolingual Open {QA} in {I}celandic",
author = "Sn{\ae}bjarnarson, V{\'e}steinn... | {"language": ["is", "en", "multilingual"], "license": "agpl-3.0", "tags": ["icelandic", "xlmr"], "datasets": ["ic3", "igc", "books3"], "pipeline": "fill-mask", "widget": [{"text": "The capital of Iceland is<mask> ."}]} | vesteinn/XLMR-ENIS | null | [
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"dataset:ic3",
"dataset:igc",
"dataset:books3",
"license:agpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"is",
"en",
"multilingual"
] | TAGS
#transformers #pytorch #safetensors #xlm-roberta #fill-mask #icelandic #xlmr #is #en #multilingual #dataset-ic3 #dataset-igc #dataset-books3 #license-agpl-3.0 #autotrain_compatible #endpoints_compatible #region-us
|
# XLMR-ENIS
This is a XLMR model trained on Icelandic and English text.
If you find this model useful please cite
| [
"# XLMR-ENIS\n\nThis is a XLMR model trained on Icelandic and English text.\n\nIf you find this model useful please cite"
] | [
"TAGS\n#transformers #pytorch #safetensors #xlm-roberta #fill-mask #icelandic #xlmr #is #en #multilingual #dataset-ic3 #dataset-igc #dataset-books3 #license-agpl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# XLMR-ENIS\n\nThis is a XLMR model trained on Icelandic and English text.\n\nIf you fi... |
question-answering | transformers |
# XLMr-ENIS-QA-Is
## Model description
This is an Icelandic reading comprehension Q&A model.
## Intended uses & limitations
This model is part of my MSc thesis about Q&A for Icelandic.
#### How to use
```python
```
#### Limitations and bias
## Training data
Translated English datasets were used along with the ... | {"language": ["is"], "tags": ["icelandic", "qa"], "datasets": ["ic3", "igc"], "metrics": ["em", "f1"], "widget": [{"text": "Hven\u00e6r var Halld\u00f3r Laxness \u00ed menntask\u00f3la ?", "context": "Halld\u00f3r Laxness ( Halld\u00f3r Kiljan ) f\u00e6ddist \u00ed Reykjav\u00edk 23. apr\u00edl \u00e1ri\u00f0 1902 og \... | vesteinn/XLMr-ENIS-QA-Is | null | [
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"question-answering",
"icelandic",
"qa",
"is",
"dataset:ic3",
"dataset:igc",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"is"
] | TAGS
#transformers #pytorch #xlm-roberta #question-answering #icelandic #qa #is #dataset-ic3 #dataset-igc #endpoints_compatible #region-us
|
# XLMr-ENIS-QA-Is
## Model description
This is an Icelandic reading comprehension Q&A model.
## Intended uses & limitations
This model is part of my MSc thesis about Q&A for Icelandic.
#### How to use
#### Limitations and bias
## Training data
Translated English datasets were used along with the Natural Quest... | [
"# XLMr-ENIS-QA-Is",
"## Model description\n\nThis is an Icelandic reading comprehension Q&A model.",
"## Intended uses & limitations\n\nThis model is part of my MSc thesis about Q&A for Icelandic.",
"#### How to use",
"#### Limitations and bias",
"## Training data\nTranslated English datasets were used a... | [
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"## Model description\n\nThis is an Icelandic reading comprehension Q&A model.",
"## Intended uses & limitations\n\nThis model is part of my MS... |
null | null | # FastText model trained on Icelandic
This model is trained on the lemmas of the Icelandic Gigaword Corpus version 20.05. It is trained using the gensim package, version 4.1.0. and parameters were set to default (100 dimensions, windows size 5)
This model can not be loaded directly since it uses gensim, clone the rep... | {"language": ["is"], "license": "agpl-3.0"} | vesteinn/fasttext_is_rmh | null | [
"is",
"license:agpl-3.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"is"
] | TAGS
#is #license-agpl-3.0 #region-us
| # FastText model trained on Icelandic
This model is trained on the lemmas of the Icelandic Gigaword Corpus version 20.05. It is trained using the gensim package, version 4.1.0. and parameters were set to default (100 dimensions, windows size 5)
This model can not be loaded directly since it uses gensim, clone the rep... | [
"# FastText model trained on Icelandic\n\nThis model is trained on the lemmas of the Icelandic Gigaword Corpus version 20.05. It is trained using the gensim package, version 4.1.0. and parameters were set to default (100 dimensions, windows size 5)\n\nThis model can not be loaded directly since it uses gensim, clon... | [
"TAGS\n#is #license-agpl-3.0 #region-us \n",
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translation | transformers |
# Student project - temporary upload | {"language": ["de", "is", "multilingual"], "tags": ["translation"]} | vesteinn/german-icelandic-translation | null | [
"transformers",
"pytorch",
"marian",
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"multilingual",
"autotrain_compatible",
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"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"de",
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"multilingual"
] | TAGS
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|
# Student project - temporary upload | [
"# Student project - temporary upload"
] | [
"TAGS\n#transformers #pytorch #marian #text2text-generation #translation #de #is #multilingual #autotrain_compatible #endpoints_compatible #region-us \n",
"# Student project - temporary upload"
] |
text2text-generation | transformers | Temporary upload - student project | {} | vesteinn/icelandic-weather-summarization | null | [
"transformers",
"pytorch",
"safetensors",
"mt5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #mt5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Temporary upload - student project | [] | [
"TAGS\n#transformers #pytorch #safetensors #mt5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
null | null | # word2vec model trained on Icelandic
This model is trained on the lemmas of the Icelandic Gigaword Corpus version 20.05. It is trained using the gensim package, version 4.1.0. and parameters were set to default (100 dimensions, windows size 5)
This model can not be loaded directly since it uses gensim, clone the rep... | {"language": ["is"], "license": "agpl-3.0"} | vesteinn/word2vec_is_rmh | null | [
"is",
"license:agpl-3.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"is"
] | TAGS
#is #license-agpl-3.0 #region-us
| # word2vec model trained on Icelandic
This model is trained on the lemmas of the Icelandic Gigaword Corpus version 20.05. It is trained using the gensim package, version 4.1.0. and parameters were set to default (100 dimensions, windows size 5)
This model can not be loaded directly since it uses gensim, clone the rep... | [
"# word2vec model trained on Icelandic\n\nThis model is trained on the lemmas of the Icelandic Gigaword Corpus version 20.05. It is trained using the gensim package, version 4.1.0. and parameters were set to default (100 dimensions, windows size 5)\n\nThis model can not be loaded directly since it uses gensim, clon... | [
"TAGS\n#is #license-agpl-3.0 #region-us \n",
"# word2vec model trained on Icelandic\n\nThis model is trained on the lemmas of the Icelandic Gigaword Corpus version 20.05. It is trained using the gensim package, version 4.1.0. and parameters were set to default (100 dimensions, windows size 5)\n\nThis model can no... |
text-generation | transformers |
# Jake Peralta DialoGPT Model | {"tags": ["conversational"]} | vibranium19/DialoGPT-medium-jake | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Jake Peralta DialoGPT Model | [
"# Jake Peralta DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Jake Peralta DialoGPT Model"
] |
zero-shot-image-classification | transformers | CLIP model retrained over some subset of the DPC dataset
### Usage instructions
```
from transformers import AutoTokenizer, AutoModel, CLIPProcessor
tokenizer = AutoTokenizer.from_pretrained("vicgalle/clip-vit-base-patch16-photo-critique")
model = AutoModel.from_pretrained("vicgalle/clip-vit-base-patch16-photo-criti... | {} | vicgalle/clip-vit-base-patch16-photo-critique | null | [
"transformers",
"jax",
"clip",
"zero-shot-image-classification",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #jax #clip #zero-shot-image-classification #endpoints_compatible #region-us
| CLIP model retrained over some subset of the DPC dataset
### Usage instructions
| [
"### Usage instructions"
] | [
"TAGS\n#transformers #jax #clip #zero-shot-image-classification #endpoints_compatible #region-us \n",
"### Usage instructions"
] |
zero-shot-classification | transformers |
### XLM-RoBERTa-large-XNLI-ANLI
XLM-RoBERTa-large model finetunned over several NLI datasets, ready to use for zero-shot classification.
Here are the accuracies for several test datasets:
| | XNLI-es | XNLI-fr | ANLI-R1 | ANLI-R2 | ANLI-R3 |
|-----------------------------|---------|-----... | {"language": "multilingual", "license": "mit", "tags": ["zero-shot-classification", "nli", "pytorch"], "datasets": ["mnli", "xnli", "anli"], "pipeline_tag": "zero-shot-classification", "widget": [{"text": "De pugna erat fantastic. Nam Crixo decem quam dilexit et praeciderunt caput aemulus.", "candidate_labels": "violen... | vicgalle/xlm-roberta-large-xnli-anli | null | [
"transformers",
"pytorch",
"safetensors",
"xlm-roberta",
"text-classification",
"zero-shot-classification",
"nli",
"multilingual",
"dataset:mnli",
"dataset:xnli",
"dataset:anli",
"doi:10.57967/hf/0977",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"regi... | null | 2022-03-02T23:29:05+00:00 | [] | [
"multilingual"
] | TAGS
#transformers #pytorch #safetensors #xlm-roberta #text-classification #zero-shot-classification #nli #multilingual #dataset-mnli #dataset-xnli #dataset-anli #doi-10.57967/hf/0977 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### XLM-RoBERTa-large-XNLI-ANLI
XLM-RoBERTa-large model finetunned over several NLI datasets, ready to use for zero-shot classification.
Here are the accuracies for several test datasets:
The model can be loaded with the zero-shot-classification pipeline like so:
You can then use this pipeline to classify sequ... | [
"### XLM-RoBERTa-large-XNLI-ANLI\n\n\nXLM-RoBERTa-large model finetunned over several NLI datasets, ready to use for zero-shot classification.\n\n\nHere are the accuracies for several test datasets:\n\n\n\nThe model can be loaded with the zero-shot-classification pipeline like so:\n\n\nYou can then use this pipelin... | [
"TAGS\n#transformers #pytorch #safetensors #xlm-roberta #text-classification #zero-shot-classification #nli #multilingual #dataset-mnli #dataset-xnli #dataset-anli #doi-10.57967/hf/0977 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### XLM-RoBERTa-large-XNLI-ANLI\n\n\nXLM-Ro... |
text-classification | 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. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion... | victen/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2265
* Accuracy: 0.9235
* F1: 0.9237
Model description
-----------------
Mo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
token-classification | 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. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-de", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.de"}, "me... | victen/xlm-roberta-base-finetuned-panx-de | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-de
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1352
* F1: 0.8591
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\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",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_... |
image-classification | transformers |
# animals-classifier
Autogenerated by HuggingPics🤗🖼️
 | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | victor/animals-classifier | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# animals-classifier
Autogenerated by HuggingPics️
!hippo | [
"# animals-classifier\n\nAutogenerated by HuggingPics️\n\n!hippo"
] | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# animals-classifier\n\nAutogenerated by HuggingPics️\n\n!hippo"
] |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 329982
## Validation Metrics
- Loss: 0.24620144069194794
- Accuracy: 0.9300053431035799
- Precision: 0.9299029425358188
- Recall: 0.9289012003693444
- AUC: 0.9795001637755057
- F1: 0.9294018015243667
## Usage
You can use cURL to acces... | {"language": "en", "tags": "autonlp", "datasets": ["victor/autonlp-data-imdb-reviews-sentiment"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}]} | victor/autonlp-imdb-reviews-sentiment-329982 | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"autonlp",
"en",
"dataset:victor/autonlp-data-imdb-reviews-sentiment",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roberta #text-classification #autonlp #en #dataset-victor/autonlp-data-imdb-reviews-sentiment #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 329982
## Validation Metrics
- Loss: 0.24620144069194794
- Accuracy: 0.9300053431035799
- Precision: 0.9299029425358188
- Recall: 0.9289012003693444
- AUC: 0.9795001637755057
- F1: 0.9294018015243667
## Usage
You can use cURL to acces... | [
"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 329982",
"## Validation Metrics\n\n- Loss: 0.24620144069194794\n- Accuracy: 0.9300053431035799\n- Precision: 0.9299029425358188\n- Recall: 0.9289012003693444\n- AUC: 0.9795001637755057\n- F1: 0.9294018015243667",
"## Usage\n\nY... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #autonlp #en #dataset-victor/autonlp-data-imdb-reviews-sentiment #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 329982",
"## Validation Metrics\n\n- Loss:... |
question-answering | 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-uncased-finetuned-squad
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-unc... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model_index": [{"name": "bert-base-uncased-finetuned-squad", "results": [{"task": {"name": "Question Answering", "type": "question-answering"}, "dataset": {"name": "squad", "type": "squad", "args": "plain_text"}}]}]} | victoraavila/bert-base-uncased-finetuned-squad | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
| bert-base-uncased-finetuned-squad
=================================
This model is a fine-tuned version of bert-base-uncased on the SQuAD1.1 dataset. It was trained through Transformers' example Colab notebook on Question Answering, available here.
It achieves the following results on the evaluation set:
* Loss: 1.0... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training. They are equal to the ones used to fine-tune distilbert-base-uncased for QA:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsi... | [
"TAGS\n#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training. They are equal to the ones used to fine-tune distilbert-base-uncased ... |
text-generation | transformers |
# Rick DialoGPT Model | {"tags": ["conversational"]} | victordata/DialoGPT-small-Rick | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Rick DialoGPT Model | [
"# Rick DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Rick DialoGPT Model"
] |
text-generation | transformers |
# Gandalf DialoGPT model | {"tags": ["conversational"]} | victorswedspot/DialoGPT-small-gandalf | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Gandalf DialoGPT model | [
"# Gandalf DialoGPT model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Gandalf DialoGPT model"
] |
null | null | bert-classification-model | {} | vigneshv7/data_classification | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| bert-classification-model | [] | [
"TAGS\n#region-us \n"
] |
text-generation | transformers | ## I fine-tuned DialoGPT-small model on "The Big Bang Theory" TV Series dataset from Kaggle (https://www.kaggle.com/mitramir5/the-big-bang-theory-series-transcript)
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
tokenizer = AutoTokenizer.from_pretrained("vijayv500/DialoGPT-small... | {"license": "mit", "tags": ["conversational"]} | vijayv500/DialoGPT-small-Big-Bang-Theory-Series-Transcripts | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| ## I fine-tuned DialoGPT-small model on "The Big Bang Theory" TV Series dataset from Kaggle (URL
| [
"## I fine-tuned DialoGPT-small model on \"The Big Bang Theory\" TV Series dataset from Kaggle (URL"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## I fine-tuned DialoGPT-small model on \"The Big Bang Theory\" TV Series dataset from Kaggle (URL"
] |
text-generation | transformers |
# Morty DialoGPT Model test | {"tags": ["conversational"]} | vijote/DialoGPT-small-Morty | null | [
"transformers",
"pytorch",
"conversational",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #conversational #endpoints_compatible #region-us
|
# Morty DialoGPT Model test | [
"# Morty DialoGPT Model test"
] | [
"TAGS\n#transformers #pytorch #conversational #endpoints_compatible #region-us \n",
"# Morty DialoGPT Model test"
] |
null | null | NER model | {} | vikasdataanalyst/NER | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| NER model | [] | [
"TAGS\n#region-us \n"
] |
feature-extraction | transformers | # <a name="introduction"></a> BARTpho: Pre-trained Sequence-to-Sequence Models for Vietnamese
Two BARTpho versions `BARTpho-syllable` and `BARTpho-word` are the first public large-scale monolingual sequence-to-sequence models pre-trained for Vietnamese. BARTpho uses the "large" architecture and pre-training scheme of... | {} | vinai/bartpho-syllable | null | [
"transformers",
"pytorch",
"tf",
"mbart",
"feature-extraction",
"arxiv:2109.09701",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2109.09701"
] | [] | TAGS
#transformers #pytorch #tf #mbart #feature-extraction #arxiv-2109.09701 #endpoints_compatible #has_space #region-us
| # <a name="introduction"></a> BARTpho: Pre-trained Sequence-to-Sequence Models for Vietnamese
Two BARTpho versions 'BARTpho-syllable' and 'BARTpho-word' are the first public large-scale monolingual sequence-to-sequence models pre-trained for Vietnamese. BARTpho uses the "large" architecture and pre-training scheme of... | [
"# <a name=\"introduction\"></a> BARTpho: Pre-trained Sequence-to-Sequence Models for Vietnamese\n\n\nTwo BARTpho versions 'BARTpho-syllable' and 'BARTpho-word' are the first public large-scale monolingual sequence-to-sequence models pre-trained for Vietnamese. BARTpho uses the \"large\" architecture and pre-traini... | [
"TAGS\n#transformers #pytorch #tf #mbart #feature-extraction #arxiv-2109.09701 #endpoints_compatible #has_space #region-us \n",
"# <a name=\"introduction\"></a> BARTpho: Pre-trained Sequence-to-Sequence Models for Vietnamese\n\n\nTwo BARTpho versions 'BARTpho-syllable' and 'BARTpho-word' are the first public larg... |
feature-extraction | transformers | # <a name="introduction"></a> BARTpho: Pre-trained Sequence-to-Sequence Models for Vietnamese
Two BARTpho versions `BARTpho-syllable` and `BARTpho-word` are the first public large-scale monolingual sequence-to-sequence models pre-trained for Vietnamese. BARTpho uses the "large" architecture and pre-training scheme of... | {} | vinai/bartpho-word | null | [
"transformers",
"pytorch",
"tf",
"mbart",
"feature-extraction",
"arxiv:2109.09701",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2109.09701"
] | [] | TAGS
#transformers #pytorch #tf #mbart #feature-extraction #arxiv-2109.09701 #endpoints_compatible #region-us
| # <a name="introduction"></a> BARTpho: Pre-trained Sequence-to-Sequence Models for Vietnamese
Two BARTpho versions 'BARTpho-syllable' and 'BARTpho-word' are the first public large-scale monolingual sequence-to-sequence models pre-trained for Vietnamese. BARTpho uses the "large" architecture and pre-training scheme of... | [
"# <a name=\"introduction\"></a> BARTpho: Pre-trained Sequence-to-Sequence Models for Vietnamese\n\n\nTwo BARTpho versions 'BARTpho-syllable' and 'BARTpho-word' are the first public large-scale monolingual sequence-to-sequence models pre-trained for Vietnamese. BARTpho uses the \"large\" architecture and pre-traini... | [
"TAGS\n#transformers #pytorch #tf #mbart #feature-extraction #arxiv-2109.09701 #endpoints_compatible #region-us \n",
"# <a name=\"introduction\"></a> BARTpho: Pre-trained Sequence-to-Sequence Models for Vietnamese\n\n\nTwo BARTpho versions 'BARTpho-syllable' and 'BARTpho-word' are the first public large-scale mon... |
fill-mask | transformers | # <a name="introduction"></a> BERTweet: A pre-trained language model for English Tweets
BERTweet is the first public large-scale language model pre-trained for English Tweets. BERTweet is trained based on the [RoBERTa](https://github.com/pytorch/fairseq/blob/master/examples/roberta/README.md) pre-training procedure.... | {} | vinai/bertweet-base | null | [
"transformers",
"pytorch",
"tf",
"jax",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #jax #roberta #fill-mask #autotrain_compatible #endpoints_compatible #has_space #region-us
| # <a name="introduction"></a> BERTweet: A pre-trained language model for English Tweets
BERTweet is the first public large-scale language model pre-trained for English Tweets. BERTweet is trained based on the RoBERTa pre-training procedure. The corpus used to pre-train BERTweet consists of 850M English Tweets (16B w... | [
"# <a name=\"introduction\"></a> BERTweet: A pre-trained language model for English Tweets \n\nBERTweet is the first public large-scale language model pre-trained for English Tweets. BERTweet is trained based on the RoBERTa pre-training procedure. The corpus used to pre-train BERTweet consists of 850M English Twee... | [
"TAGS\n#transformers #pytorch #tf #jax #roberta #fill-mask #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# <a name=\"introduction\"></a> BERTweet: A pre-trained language model for English Tweets \n\nBERTweet is the first public large-scale language model pre-trained for English Tweets. B... |
fill-mask | transformers | # <a name="introduction"></a> BERTweet: A pre-trained language model for English Tweets
BERTweet is the first public large-scale language model pre-trained for English Tweets. BERTweet is trained based on the [RoBERTa](https://github.com/pytorch/fairseq/blob/master/examples/roberta/README.md) pre-training procedure.... | {} | vinai/bertweet-covid19-base-cased | null | [
"transformers",
"pytorch",
"tf",
"jax",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #jax #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| # <a name="introduction"></a> BERTweet: A pre-trained language model for English Tweets
BERTweet is the first public large-scale language model pre-trained for English Tweets. BERTweet is trained based on the RoBERTa pre-training procedure. The corpus used to pre-train BERTweet consists of 850M English Tweets (16B w... | [
"# <a name=\"introduction\"></a> BERTweet: A pre-trained language model for English Tweets \n\nBERTweet is the first public large-scale language model pre-trained for English Tweets. BERTweet is trained based on the RoBERTa pre-training procedure. The corpus used to pre-train BERTweet consists of 850M English Twee... | [
"TAGS\n#transformers #pytorch #tf #jax #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"# <a name=\"introduction\"></a> BERTweet: A pre-trained language model for English Tweets \n\nBERTweet is the first public large-scale language model pre-trained for English Tweets. BERTweet is ... |
fill-mask | transformers | # <a name="introduction"></a> BERTweet: A pre-trained language model for English Tweets
BERTweet is the first public large-scale language model pre-trained for English Tweets. BERTweet is trained based on the [RoBERTa](https://github.com/pytorch/fairseq/blob/master/examples/roberta/README.md) pre-training procedure.... | {} | vinai/bertweet-covid19-base-uncased | null | [
"transformers",
"pytorch",
"tf",
"jax",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #jax #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| # <a name="introduction"></a> BERTweet: A pre-trained language model for English Tweets
BERTweet is the first public large-scale language model pre-trained for English Tweets. BERTweet is trained based on the RoBERTa pre-training procedure. The corpus used to pre-train BERTweet consists of 850M English Tweets (16B w... | [
"# <a name=\"introduction\"></a> BERTweet: A pre-trained language model for English Tweets \n\nBERTweet is the first public large-scale language model pre-trained for English Tweets. BERTweet is trained based on the RoBERTa pre-training procedure. The corpus used to pre-train BERTweet consists of 850M English Twee... | [
"TAGS\n#transformers #pytorch #tf #jax #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"# <a name=\"introduction\"></a> BERTweet: A pre-trained language model for English Tweets \n\nBERTweet is the first public large-scale language model pre-trained for English Tweets. BERTweet is ... |
fill-mask | transformers | # <a name="introduction"></a> BERTweet: A pre-trained language model for English Tweets
BERTweet is the first public large-scale language model pre-trained for English Tweets. BERTweet is trained based on the [RoBERTa](https://github.com/pytorch/fairseq/blob/master/examples/roberta/README.md) pre-training procedure.... | {} | vinai/bertweet-large | null | [
"transformers",
"pytorch",
"tf",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| # <a name="introduction"></a> BERTweet: A pre-trained language model for English Tweets
BERTweet is the first public large-scale language model pre-trained for English Tweets. BERTweet is trained based on the RoBERTa pre-training procedure. The corpus used to pre-train BERTweet consists of 850M English Tweets (16B w... | [
"# <a name=\"introduction\"></a> BERTweet: A pre-trained language model for English Tweets \n\nBERTweet is the first public large-scale language model pre-trained for English Tweets. BERTweet is trained based on the RoBERTa pre-training procedure. The corpus used to pre-train BERTweet consists of 850M English Twee... | [
"TAGS\n#transformers #pytorch #tf #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"# <a name=\"introduction\"></a> BERTweet: A pre-trained language model for English Tweets \n\nBERTweet is the first public large-scale language model pre-trained for English Tweets. BERTweet is train... |
fill-mask | transformers | # <a name="introduction"></a> PhoBERT: Pre-trained language models for Vietnamese
Pre-trained PhoBERT models are the state-of-the-art language models for Vietnamese ([Pho](https://en.wikipedia.org/wiki/Pho), i.e. "Phở", is a popular food in Vietnam):
- Two PhoBERT versions of "base" and "large" are the first publ... | {} | vinai/phobert-base | null | [
"transformers",
"pytorch",
"tf",
"jax",
"roberta",
"fill-mask",
"arxiv:2003.00744",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2003.00744"
] | [] | TAGS
#transformers #pytorch #tf #jax #roberta #fill-mask #arxiv-2003.00744 #autotrain_compatible #endpoints_compatible #has_space #region-us
| # <a name="introduction"></a> PhoBERT: Pre-trained language models for Vietnamese
Pre-trained PhoBERT models are the state-of-the-art language models for Vietnamese (Pho, i.e. "Phở", is a popular food in Vietnam):
- Two PhoBERT versions of "base" and "large" are the first public large-scale monolingual language m... | [
"# <a name=\"introduction\"></a> PhoBERT: Pre-trained language models for Vietnamese \n \nPre-trained PhoBERT models are the state-of-the-art language models for Vietnamese (Pho, i.e. \"Phở\", is a popular food in Vietnam):\n\n - Two PhoBERT versions of \"base\" and \"large\" are the first public large-scale monol... | [
"TAGS\n#transformers #pytorch #tf #jax #roberta #fill-mask #arxiv-2003.00744 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# <a name=\"introduction\"></a> PhoBERT: Pre-trained language models for Vietnamese \n \nPre-trained PhoBERT models are the state-of-the-art language models for Vie... |
fill-mask | transformers | # <a name="introduction"></a> PhoBERT: Pre-trained language models for Vietnamese
Pre-trained PhoBERT models are the state-of-the-art language models for Vietnamese ([Pho](https://en.wikipedia.org/wiki/Pho), i.e. "Phở", is a popular food in Vietnam):
- Two PhoBERT versions of "base" and "large" are the first publ... | {} | vinai/phobert-large | null | [
"transformers",
"pytorch",
"tf",
"jax",
"roberta",
"fill-mask",
"arxiv:2003.00744",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2003.00744"
] | [] | TAGS
#transformers #pytorch #tf #jax #roberta #fill-mask #arxiv-2003.00744 #autotrain_compatible #endpoints_compatible #has_space #region-us
| # <a name="introduction"></a> PhoBERT: Pre-trained language models for Vietnamese
Pre-trained PhoBERT models are the state-of-the-art language models for Vietnamese (Pho, i.e. "Phở", is a popular food in Vietnam):
- Two PhoBERT versions of "base" and "large" are the first public large-scale monolingual language m... | [
"# <a name=\"introduction\"></a> PhoBERT: Pre-trained language models for Vietnamese \n \nPre-trained PhoBERT models are the state-of-the-art language models for Vietnamese (Pho, i.e. \"Phở\", is a popular food in Vietnam):\n\n - Two PhoBERT versions of \"base\" and \"large\" are the first public large-scale monol... | [
"TAGS\n#transformers #pytorch #tf #jax #roberta #fill-mask #arxiv-2003.00744 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# <a name=\"introduction\"></a> PhoBERT: Pre-trained language models for Vietnamese \n \nPre-trained PhoBERT models are the state-of-the-art language models for Vie... |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 496213536
- CO2 Emissions (in grams): 19.964760910364927
## Validation Metrics
- Loss: 0.7149562835693359
- Accuracy: 0.8092592592592592
- Macro F1: 0.8085189591849891
- Micro F1: 0.8092592592592593
- Weighted F1: 0.808518959184988... | {"language": "en", "tags": "autonlp", "datasets": ["vinaydngowda/autonlp-data-case-classify-xlnet"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 19.964760910364927} | vinaydngowda/Robertabase_Ana4 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autonlp",
"en",
"dataset:vinaydngowda/autonlp-data-case-classify-xlnet",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #autonlp #en #dataset-vinaydngowda/autonlp-data-case-classify-xlnet #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 496213536
- CO2 Emissions (in grams): 19.964760910364927
## Validation Metrics
- Loss: 0.7149562835693359
- Accuracy: 0.8092592592592592
- Macro F1: 0.8085189591849891
- Micro F1: 0.8092592592592593
- Weighted F1: 0.808518959184988... | [
"# Model Trained Using AutoNLP\n\n- Problem type: Multi-class Classification\n- Model ID: 496213536\n- CO2 Emissions (in grams): 19.964760910364927",
"## Validation Metrics\n\n- Loss: 0.7149562835693359\n- Accuracy: 0.8092592592592592\n- Macro F1: 0.8085189591849891\n- Micro F1: 0.8092592592592593\n- Weighted F1:... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autonlp #en #dataset-vinaydngowda/autonlp-data-case-classify-xlnet #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Multi-class Classification\n- Model ID: 496213536\n- CO2 Emis... |
fill-mask | transformers |
# ChefBERTo 👨🍳
**chefberto-italian-cased** is a BERT model obtained by MLM adaptive-tuning [**bert-base-italian-xxl-cased**](https://huggingface.co/dbmdz/bert-base-italian-xxl-cased) on Italian cooking recipes, approximately 50k sentences (2.6M words).
**Author:** Cristiano De Nobili ([@denocris](https://twitter... | {"language": "it", "license": "mit", "widget": [{"text": "La pasta pi\u00f9 semplice \u00e8 aglio, [MASK] e peperoncino."}, {"text": "Per fare la carbonara servono le [MASK]."}, {"text": "A tavola non pu\u00f2 mancare del buon [MASK]."}]} | vinhood/chefberto-italian-cased | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"it",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"it"
] | TAGS
#transformers #pytorch #bert #fill-mask #it #license-mit #autotrain_compatible #endpoints_compatible #region-us
| ChefBERTo
===========
chefberto-italian-cased is a BERT model obtained by MLM adaptive-tuning bert-base-italian-xxl-cased on Italian cooking recipes, approximately 50k sentences (2.6M words).
Author: Cristiano De Nobili (@denocris on Twitter, LinkedIn) for VINHOOD.
Perplexity
==========
T... | [] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #it #license-mit #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers |
# WineBERTo 🍷🥂
**wineberto-italian-cased** is a BERT model obtained by MLM adaptive-tuning [**bert-base-italian-xxl-cased**](https://huggingface.co/dbmdz/bert-base-italian-xxl-cased) on Italian drink recipes and wine descriptions, approximately 77k sentences (3.3M words).
**Author:** Cristiano De Nobili ([@denocri... | {"language": "it", "license": "mit", "widget": [{"text": "Con del pesce bisogna bere un bicchiere di vino [MASK]."}, {"text": "Con la carne c'\u00e8 bisogno del vino [MASK]."}, {"text": "A tavola non pu\u00f2 mancare del buon [MASK]."}]} | vinhood/wineberto-italian-cased | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"it",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"it"
] | TAGS
#transformers #pytorch #bert #fill-mask #it #license-mit #autotrain_compatible #endpoints_compatible #region-us
| WineBERTo
=========
wineberto-italian-cased is a BERT model obtained by MLM adaptive-tuning bert-base-italian-xxl-cased on Italian drink recipes and wine descriptions, approximately 77k sentences (3.3M words).
Author: Cristiano De Nobili (@denocris on Twitter, LinkedIn) for VINHOOD.
Perplexit... | [] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #it #license-mit #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers | This T5 small model finetuned on Reddit data.
It has two subtasks:
1. title generation
2. tag classification
| {} | vionwinnie/t5-reddit | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| This T5 small model finetuned on Reddit data.
It has two subtasks:
1. title generation
2. tag classification
| [] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text2text-generation | transformers | pegasus paraphraser model
using <a href="https://huggingface.co/tuner007/pegasus_paraphrase" target="_blank">tuner007/pegasus_paraphrase</a> | {} | vishalz/paraphrase_model | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| pegasus paraphraser model
using <a href="URL target="_blank">tuner007/pegasus_paraphrase</a> | [] | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers | # Tamil Mix Sentiment analysis
Model is trained on tamil-mix-sentiment dataset and finetuned with backend as bert-base-cased model
## Inference usage
On the hosted Inference type in the text for which you want to classify.
Eg: Super a iruku bro intha work, vera level mass | {} | vishnun/bert-base-cased-tamil-mix-sentiment | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| # Tamil Mix Sentiment analysis
Model is trained on tamil-mix-sentiment dataset and finetuned with backend as bert-base-cased model
## Inference usage
On the hosted Inference type in the text for which you want to classify.
Eg: Super a iruku bro intha work, vera level mass | [
"# Tamil Mix Sentiment analysis\n\nModel is trained on tamil-mix-sentiment dataset and finetuned with backend as bert-base-cased model",
"## Inference usage\n\nOn the hosted Inference type in the text for which you want to classify. \nEg: Super a iruku bro intha work, vera level mass"
] | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# Tamil Mix Sentiment analysis\n\nModel is trained on tamil-mix-sentiment dataset and finetuned with backend as bert-base-cased model",
"## Inference usage\n\nOn the hosted Inference type in the... |
text-generation | 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. -->
# distilgpt2-finetuned-distilgpt2-med_articles
This model is a fine-tuned version of [vishnun/distilgpt2-finetuned-distilgpt2-med_... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": [], "model_index": [{"name": "distilgpt2-finetuned-distilgpt2-med_articles", "results": [{"task": {"name": "Causal Language Modeling", "type": "text-generation"}}]}]} | vishnun/distilgpt2-finetuned-distilgpt2-med_articles | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| distilgpt2-finetuned-distilgpt2-med\_articles
=============================================
This model is a fine-tuned version of vishnun/distilgpt2-finetuned-distilgpt2-med\_articles on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.3171
Model description
-----------------
... | [
"### 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: 5",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2... |
text-generation | 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. -->
# distilgpt2-finetuned-tamil-gpt
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": [], "model_index": [{"name": "distilgpt2-finetuned-tamil-gpt", "results": [{"task": {"name": "Causal Language Modeling", "type": "text-generation"}}]}]} | vishnun/distilgpt2-finetuned-tamil-gpt | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| distilgpt2-finetuned-tamil-gpt
==============================
This model is a fine-tuned version of distilgpt2 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 4.4097
Model description
-----------------
More information needed
Intended uses & limitations
------------------... | [
"### 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: 5",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2... |
text-generation | 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. -->
# distilgpt2-finetuned-tamilmixsentiment
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": [], "model_index": [{"name": "distilgpt2-finetuned-tamilmixsentiment", "results": [{"task": {"name": "Causal Language Modeling", "type": "text-generation"}}]}]} | vishnun/distilgpt2-finetuned-tamilmixsentiment | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| distilgpt2-finetuned-tamilmixsentiment
======================================
This model is a fine-tuned version of distilgpt2 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 4.4572
Model description
-----------------
More information needed
Intended uses & limitations
--... | [
"### 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: 5",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2... |
automatic-speech-recognition | transformers | #
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the openslr dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4239
- Wer: 0.4221
# Evaluation results on OpenSLR "test" (self-split 10%) (Running ./eval.py):
- WER: 0.4... | {"language": ["km"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "openslr", "robust-speech-event", "km", "generated_from_trainer", "hf-asr-leaderboard"], "datasets": ["openslr"], "model-index": [{"name": "wav2vec2-xls-r-1b-km", "results": [{"task": {"type": "automatic-speech-recognition", "name": ... | vitouphy/wav2vec2-xls-r-1b-khmer | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"openslr",
"robust-speech-event",
"km",
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"hf-asr-leaderboard",
"dataset:openslr",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"km"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #openslr #robust-speech-event #km #generated_from_trainer #hf-asr-leaderboard #dataset-openslr #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us
|
This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the openslr dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4239
* Wer: 0.4221
Evaluation results on OpenSLR "test" (self-split 10%) (Running ./URL):
=====================================================================... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #openslr #robust-speech-event #km #generated_from_trainer #hf-asr-leaderboard #dataset-openslr #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters we... |
automatic-speech-recognition | transformers |
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the librispeech_asr dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1444
- Wer: 0.1167
## Model description
More information needed
## Intended uses & limitat... | {"language": ["en"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "en", "generated_from_trainer", "hf-asr-leaderboard", "librispeech_asr", "robust-speech-event"], "datasets": ["librispeech_asr"], "model-index": [{"name": "XLS-R-300M - English", "results": [{"task": {"type": "automatic-speech-recogn... | vitouphy/wav2vec2-xls-r-300m-english | null | [
"transformers",
"pytorch",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"en",
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"librispeech_asr",
"robust-speech-event",
"dataset:librispeech_asr",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #en #generated_from_trainer #hf-asr-leaderboard #librispeech_asr #robust-speech-event #dataset-librispeech_asr #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the librispeech\_asr dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1444
* Wer: 0.1167
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More in... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
"TAGS\n#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #en #generated_from_trainer #hf-asr-leaderboard #librispeech_asr #robust-speech-event #dataset-librispeech_asr #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following ... |
automatic-speech-recognition | transformers |
#
This model is for transcribing audio into Hiragana, one format of Japanese language.
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the `mozilla-foundation/common_voice_8_0 dataset`. Note that the following results are achieved by:
- Mo... | {"language": ["ja"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "generated_from_trainer", "hf-asr-leaderboard", "ja", "mozilla-foundation/common_voice_8_0", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "XLS-R-300M - Japanese", "results": [{... | vitouphy/wav2vec2-xls-r-300m-japanese | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"ja",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_8_0",
"doi:10.57967/hf/0124",
"license:apache-2.0",
"model-index... | null | 2022-03-02T23:29:05+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #ja #mozilla-foundation/common_voice_8_0 #robust-speech-event #dataset-mozilla-foundation/common_voice_8_0 #doi-10.57967/hf/0124 #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
This model is for transcribing audio into Hiragana, one format of Japanese language.
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the 'mozilla-foundation/common\_voice\_8\_0 dataset'. Note that the following results are achieved by:
* Modify 'URL' to suit the use case.
* Since kanji and ka... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #ja #mozilla-foundation/common_voice_8_0 #robust-speech-event #dataset-mozilla-foundation/common_voice_8_0 #doi-10.57967/hf/0124 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
... |
automatic-speech-recognition | transformers |
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the openslr dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3281
- Wer: 0.3462
# Evaluation results on OpenSLR "test" (self-split 10%) (Running ./eval.py):
- WER... | {"language": ["km"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "openslr", "robust-speech-event", "km", "generated_from_trainer", "hf-asr-leaderboard"], "model-index": [{"name": "xls-r-300m-km", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset"... | vitouphy/wav2vec2-xls-r-300m-khmer | null | [
"transformers",
"pytorch",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"openslr",
"robust-speech-event",
"km",
"generated_from_trainer",
"hf-asr-leaderboard",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"km"
] | TAGS
#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #openslr #robust-speech-event #km #generated_from_trainer #hf-asr-leaderboard #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the openslr dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3281
* Wer: 0.3462
Evaluation results on OpenSLR "test" (self-split 10%) (Running ./URL):
===================================================================... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
"TAGS\n#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #openslr #robust-speech-event #km #generated_from_trainer #hf-asr-leaderboard #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during ... |
question-answering | 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. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad", "results": []}]} | vitusya/distilbert-base-uncased-finetuned-squad | null | [
"transformers",
"pytorch",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-squad
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the squad dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1610
Model description
-----------------
More information needed
Intended uses ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\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",
"### Traini... | [
"TAGS\n#transformers #pytorch #distilbert #question-answering #generated_from_trainer #dataset-squad #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: 2e-05\n* train\\_batch\\_size: 16\n* ev... |
text-generation | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | vivek-g-2009/DialoGPT-medium-harrypotter | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+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"
] |
image-classification | transformers |
# animal_classifier_huggingface
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.co... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | vivekRahul/animal_classifier_huggingface | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# animal_classifier_huggingface
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
#### cat
!cat
#### dog
!dog
#### elephant
!elephant
#### lion
!lion
#### tiger
!tiger | [
"# animal_classifier_huggingface\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",
"#### cat\n\n!cat",
"#### dog\n\n!dog",
"#### elephant\n\n!elephant",
"###... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# animal_classifier_huggingface\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nR... |
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