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text-generation | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | psblade/DialoGPT-medium-PotterBot | null | [
"transformers",
"pytorch",
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"text-generation",
"conversational",
"autotrain_compatible",
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"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"
] |
fill-mask | transformers | ## RoBERTa Latin model
This is a Latin RoBERTa-based LM model.
The data it uses is the same as has been used to compute the text referenced HTR evaluation measures.
The intention of the Transformer-based LM is twofold: on the one hand, it will be used for the evaluation of HTR results, on the other, it should be use... | {} | pstroe/roberta-base-latin-cased | null | [
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"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| ## RoBERTa Latin model
This is a Latin RoBERTa-based LM model.
The data it uses is the same as has been used to compute the text referenced HTR evaluation measures.
The intention of the Transformer-based LM is twofold: on the one hand, it will be used for the evaluation of HTR results, on the other, it should be use... | [
"## RoBERTa Latin model\n\nThis is a Latin RoBERTa-based LM model.\n\nThe data it uses is the same as has been used to compute the text referenced HTR evaluation measures.\n\nThe intention of the Transformer-based LM is twofold: on the one hand, it will be used for the evaluation of HTR results, on the other, it sh... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"## RoBERTa Latin model\n\nThis is a Latin RoBERTa-based LM model.\n\nThe data it uses is the same as has been used to compute the text referenced HTR evaluation measures.\n\nThe intention of the Transfor... |
text-generation | transformers |
# Ballpark Trivia: Size L
Are you frequently asked google-able Trivia questions and annoyed by it? Well, this is the model for you! Ballpark Trivia Bot answers any trivia question with something that sounds plausible but is probably not 100% correct. One might say.. the answers are in the right ballpark. Check out... | {"language": ["en"], "license": "mit", "tags": ["text-generation", "gpt2", "gpt"], "datasets": ["natural questions"], "widget": [{"text": "how many ping-pong balls fit inside a standard 747 jet aeroplane?\nperson beta:\n\n", "example_title": "ping-pong"}, {"text": "What is the capital of Uganda?\nperson beta:\n\n", "ex... | pszemraj/Ballpark-Trivia-L | null | [
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"gpt2",
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"gpt",
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"license:mit",
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"text-generation-inference",
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] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #gpt #en #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# Ballpark Trivia: Size L
Are you frequently asked google-able Trivia questions and annoyed by it? Well, this is the model for you! Ballpark Trivia Bot answers any trivia question with something that sounds plausible but is probably not 100% correct. One might say.. the answers are in the right ballpark. Check out... | [
"# Ballpark Trivia: Size L\n\nAre you frequently asked google-able Trivia questions and annoyed by it? Well, this is the model for you! Ballpark Trivia Bot answers any trivia question with something that sounds plausible but is probably not 100% correct. One might say.. the answers are in the right ballpark. Check ... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #gpt #en #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# Ballpark Trivia: Size L\n\nAre you frequently asked google-able Trivia questions and annoyed by it? Well, this is the model for you! Ballp... |
text-generation | transformers |
# Ballpark Trivia: Size XL
**Check out a demo on HF Spaces [here](https://huggingface.co/spaces/pszemraj/ballpark-trivia).**
Are you frequently asked google-able Trivia questions and annoyed by it? Well, this is the model for you! Ballpark Trivia Bot answers any trivia question with something that sounds plausible b... | {"language": ["en"], "license": "mit", "tags": ["text-generation", "gpt2", "gpt", "trivia", "chatbot"], "widget": [{"text": "how many ping-pong balls fit inside a standard 747 jet aeroplane?\nperson beta:\n\n", "example_title": "ping-pong"}, {"text": "What is the capital of Uganda?\nperson beta:\n\n", "example_title": ... | pszemraj/Ballpark-Trivia-XL | null | [
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"trivia",
"chatbot",
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"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #gpt #trivia #chatbot #en #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# Ballpark Trivia: Size XL
Check out a demo on HF Spaces here.
Are you frequently asked google-able Trivia questions and annoyed by it? Well, this is the model for you! Ballpark Trivia Bot answers any trivia question with something that sounds plausible but is probably not 100% correct. One might say.. the answers a... | [
"# Ballpark Trivia: Size XL\n\nCheck out a demo on HF Spaces here.\n\nAre you frequently asked google-able Trivia questions and annoyed by it? Well, this is the model for you! Ballpark Trivia Bot answers any trivia question with something that sounds plausible but is probably not 100% correct. One might say.. the a... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #gpt #trivia #chatbot #en #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# Ballpark Trivia: Size XL\n\nCheck out a demo on HF Spaces here.\n\nAre you frequently asked google-able Trivia questions ... |
summarization | transformers |
# bigbird pegasus on the booksum dataset
>_this is the "latest" version of the model that has been trained the longest, currently at 70k steps_
- **GOAL:** A summarization model that 1) summarizes the source content accurately 2) _more important IMO_ produces summaries that are easy to read and understand (* cough... | {"language": ["en"], "license": "apache-2.0", "tags": ["summarization", "summarisation", "summary", "notes", "bigbird_pegasus_", "pegasus", "bigbird"], "datasets": ["kmfoda/booksum"], "metrics": ["rouge"], "widget": [{"text": "large earthquakes along a given fault segment do not occur at random intervals because it tak... | pszemraj/bigbird-pegasus-large-K-booksum | null | [
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"model-index",
"a... | null | 2022-03-02T23:29:05+00:00 | [
"2105.08209"
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"en"
] | TAGS
#transformers #pytorch #onnx #safetensors #bigbird_pegasus #text2text-generation #summarization #summarisation #summary #notes #bigbird_pegasus_ #pegasus #bigbird #en #dataset-kmfoda/booksum #arxiv-2105.08209 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# bigbird pegasus on the booksum dataset
>_this is the "latest" version of the model that has been trained the longest, currently at 70k steps_
- GOAL: A summarization model that 1) summarizes the source content accurately 2) _more important IMO_ produces summaries that are easy to read and understand (* cough * u... | [
"# bigbird pegasus on the booksum dataset \n\n>_this is the \"latest\" version of the model that has been trained the longest, currently at 70k steps_\n\n- GOAL: A summarization model that 1) summarizes the source content accurately 2) _more important IMO_ produces summaries that are easy to read and understand (* ... | [
"TAGS\n#transformers #pytorch #onnx #safetensors #bigbird_pegasus #text2text-generation #summarization #summarisation #summary #notes #bigbird_pegasus_ #pegasus #bigbird #en #dataset-kmfoda/booksum #arxiv-2105.08209 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# bi... |
text2text-generation | transformers |
# pegasus does math?
- testing to see how feasible seq2seq math problems are
- answer: at least with 2 epochs, it is uhhhh not super feasible.
| {"language": "en", "tags": ["math", "pegasus"], "datasets": ["competition_math"], "metrics": ["rouge"], "widget": [{"text": "Michael scores a 95, 87, 85, 93, and a 94 on his first 5 math tests. If he wants a 90 average, what must he score on the final math test?", "example_title": "averaging"}, {"text": "If the sum of ... | pszemraj/distill-pegasus-CompMath | null | [
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"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #pegasus #text2text-generation #math #en #dataset-competition_math #autotrain_compatible #endpoints_compatible #region-us
|
# pegasus does math?
- testing to see how feasible seq2seq math problems are
- answer: at least with 2 epochs, it is uhhhh not super feasible.
| [
"# pegasus does math?\n- testing to see how feasible seq2seq math problems are\n- answer: at least with 2 epochs, it is uhhhh not super feasible."
] | [
"TAGS\n#transformers #pytorch #safetensors #pegasus #text2text-generation #math #en #dataset-competition_math #autotrain_compatible #endpoints_compatible #region-us \n",
"# pegasus does math?\n- testing to see how feasible seq2seq math problems are\n- answer: at least with 2 epochs, it is uhhhh not super feasible... |
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. -->
# pszemraj/gpt2-medium-vaguely-human-dialogue
This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medi... | {"language": ["en"], "license": "mit", "tags": ["text-generation", "gpt2", "gpt"], "widget": [{"text": "Do you like my new haircut?\nperson beta:\n\n", "example_title": "haircut"}, {"text": "I love to learn new things.. are you willing to teach me something?\nperson beta:\n\n", "example_title": "teaching"}, {"text": "W... | pszemraj/gpt2-medium-vaguely-human-dialogue | null | [
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"text-generation",
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"en",
"license:mit",
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"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #gpt2 #text-generation #gpt #en #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| pszemraj/gpt2-medium-vaguely-human-dialogue
===========================================
This model is a fine-tuned version of gpt2-medium on a parsed version of Wizard of Wikipedia. Because the batch size was so large, it learned a general understanding of words that makes sense together but does not specifically res... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* distributed\\_type: multi-GPU\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam w... | [
"TAGS\n#transformers #pytorch #safetensors #gpt2 #text-generation #gpt #en #license-mit #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: 2e-05\n* train\\_batch\... |
summarization | transformers | # LED-Based Summarization Model: Condensing Long and Technical Information
<a href="https://colab.research.google.com/gist/pszemraj/36950064ca76161d9d258e5cdbfa6833/led-base-demo-token-batching.ipynb">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>
The Longformer Enco... | {"license": ["apache-2.0", "bsd-3-clause"], "tags": ["summarization", "led", "summary", "longformer", "booksum", "long-document", "long-form"], "datasets": ["kmfoda/booksum"], "metrics": ["rouge"], "widget": [{"text": "large earthquakes along a given fault segment do not occur at random intervals because it takes time ... | pszemraj/led-base-book-summary | null | [
"transformers",
"pytorch",
"safetensors",
"led",
"text2text-generation",
"summarization",
"summary",
"longformer",
"booksum",
"long-document",
"long-form",
"dataset:kmfoda/booksum",
"license:apache-2.0",
"license:bsd-3-clause",
"model-index",
"autotrain_compatible",
"endpoints_compat... | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #led #text2text-generation #summarization #summary #longformer #booksum #long-document #long-form #dataset-kmfoda/booksum #license-apache-2.0 #license-bsd-3-clause #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| # LED-Based Summarization Model: Condensing Long and Technical Information
<a href="URL
<img src="URL alt="Open In Colab"/>
</a>
The Longformer Encoder-Decoder (LED) for Narrative-Esque Long Text Summarization is a model I fine-tuned from allenai/led-base-16384 to condense extensive technical, academic, and narrati... | [
"# LED-Based Summarization Model: Condensing Long and Technical Information\n\n<a href=\"URL\n <img src=\"URL alt=\"Open In Colab\"/>\n</a>\n\nThe Longformer Encoder-Decoder (LED) for Narrative-Esque Long Text Summarization is a model I fine-tuned from allenai/led-base-16384 to condense extensive technical, academ... | [
"TAGS\n#transformers #pytorch #safetensors #led #text2text-generation #summarization #summary #longformer #booksum #long-document #long-form #dataset-kmfoda/booksum #license-apache-2.0 #license-bsd-3-clause #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# LED-Based Summarizat... |
summarization | transformers | # led-large-book-summary
<a href="https://colab.research.google.com/gist/pszemraj/3eba944ddc9fc9a4a1bfb21e83b57620/summarization-token-batching.ipynb">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>
This model is a fine-tuned version of [allenai/led-large-16384](https... | {"language": ["en"], "license": ["apache-2.0", "bsd-3-clause"], "tags": ["summarization", "led", "summary", "longformer", "booksum", "long-document", "long-form"], "datasets": ["kmfoda/booksum"], "metrics": ["rouge"], "widget": [{"text": "large earthquakes along a given fault segment do not occur at random intervals be... | pszemraj/led-large-book-summary | null | [
"transformers",
"pytorch",
"safetensors",
"led",
"text2text-generation",
"summarization",
"summary",
"longformer",
"booksum",
"long-document",
"long-form",
"en",
"dataset:kmfoda/booksum",
"arxiv:2105.08209",
"doi:10.57967/hf/0101",
"license:apache-2.0",
"license:bsd-3-clause",
"mod... | null | 2022-03-02T23:29:05+00:00 | [
"2105.08209"
] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #led #text2text-generation #summarization #summary #longformer #booksum #long-document #long-form #en #dataset-kmfoda/booksum #arxiv-2105.08209 #doi-10.57967/hf/0101 #license-apache-2.0 #license-bsd-3-clause #model-index #autotrain_compatible #endpoints_compatible #has_space #re... | # led-large-book-summary
<a href="URL
<img src="URL alt="Open In Colab"/>
</a>
This model is a fine-tuned version of allenai/led-large-16384 on the 'BookSum' dataset ('kmfoda/booksum'). It aims to generalize well and be useful in summarizing lengthy text for both academic and everyday purposes.
- Handles up to 16... | [
"# led-large-book-summary\n\n<a href=\"URL\n <img src=\"URL alt=\"Open In Colab\"/>\n</a>\n\nThis model is a fine-tuned version of allenai/led-large-16384 on the 'BookSum' dataset ('kmfoda/booksum'). It aims to generalize well and be useful in summarizing lengthy text for both academic and everyday purposes. \n\n-... | [
"TAGS\n#transformers #pytorch #safetensors #led #text2text-generation #summarization #summary #longformer #booksum #long-document #long-form #en #dataset-kmfoda/booksum #arxiv-2105.08209 #doi-10.57967/hf/0101 #license-apache-2.0 #license-bsd-3-clause #model-index #autotrain_compatible #endpoints_compatible #has_spa... |
summarization | transformers |
# checkpoints
This model is a fine-tuned version of [google/pegasus-large](https://huggingface.co/google/pegasus-large) on the [booksum](https://github.com/salesforce/booksum) dataset.
## Model description
More information needed
## Intended uses & limitations
- standard pegasus has a max input length of 1024 to... | {"language": ["en"], "license": "apache-2.0", "tags": ["summarization", "pegasus"], "datasets": ["kmfoda/booksum"], "metrics": ["rouge"], "widget": [{"text": "large earthquakes along a given fault segment do not occur at random intervals because it takes time to accumulate the strain energy for the rupture. The rates a... | pszemraj/pegasus-large-book-summary | null | [
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"summarization",
"en",
"dataset:kmfoda/booksum",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #pegasus #text2text-generation #summarization #en #dataset-kmfoda/booksum #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# checkpoints
This model is a fine-tuned version of google/pegasus-large on the booksum dataset.
## Model description
More information needed
## Intended uses & limitations
- standard pegasus has a max input length of 1024 tokens, therefore the model only saw the first 1024 tokens of a chapter when training, and... | [
"# checkpoints\n\nThis model is a fine-tuned version of google/pegasus-large on the booksum dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\n- standard pegasus has a max input length of 1024 tokens, therefore the model only saw the first 1024 tokens of a chapter w... | [
"TAGS\n#transformers #pytorch #safetensors #pegasus #text2text-generation #summarization #en #dataset-kmfoda/booksum #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# checkpoints\n\nThis model is a fine-tuned version of google/pegasus-large on the booksum dataset.",
"## Model de... |
summarization | transformers |
# pszemraj/pegasus-large-summary-explain
This model is a fine-tuned version of [google/pegasus-large](https://huggingface.co/google/pegasus-large) on the [booksum](https://github.com/salesforce/booksum) dataset for four total epochs.
It achieves the following results on the evaluation set:
- eval_loss: 1.1193
- eva... | {"language": ["en"], "license": "apache-2.0", "tags": ["summarization", "pegasus"], "datasets": ["kmfoda/booksum"], "metrics": ["rouge"], "widget": [{"text": "large earthquakes along a given fault segment do not occur at random intervals because it takes time to accumulate the strain energy for the rupture. The rates a... | pszemraj/pegasus-large-summary-explain | null | [
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"en",
"dataset:kmfoda/booksum",
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"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #pegasus #text2text-generation #summarization #en #dataset-kmfoda/booksum #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# pszemraj/pegasus-large-summary-explain
This model is a fine-tuned version of google/pegasus-large on the booksum dataset for four total epochs.
It achieves the following results on the evaluation set:
- eval_loss: 1.1193
- eval_runtime: 6.6754
- eval_samples_per_second: 27.714
- eval_steps_per_second: 1.798
- epo... | [
"# pszemraj/pegasus-large-summary-explain\n\nThis model is a fine-tuned version of google/pegasus-large on the booksum dataset for four total epochs.\n\nIt achieves the following results on the evaluation set:\n- eval_loss: 1.1193\n- eval_runtime: 6.6754\n- eval_samples_per_second: 27.714\n- eval_steps_per_second: ... | [
"TAGS\n#transformers #pytorch #safetensors #pegasus #text2text-generation #summarization #en #dataset-kmfoda/booksum #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# pszemraj/pegasus-large-summary-explain\n\nThis model is a fine-tuned version of google/pegasus-large ... |
text2text-generation | transformers |
# checkpoints
- This model is a fine-tuned version of [google/t5-v1_1-base](https://huggingface.co/google/t5-v1_1-base) on the `vblagoje/lfqa` dataset, with training duration of 2 epochs, for a (_somewhat_) apples-to-apples comparison with [t5-base](https://huggingface.co/pszemraj/t5-base-askscience) on the standard... | {"language": ["en"], "license": "apache-2.0", "tags": ["t5", "qa", "askscience", "lfqa", "information retrieval"], "datasets": ["vblagoje/lfqa"], "metrics": ["rouge"], "widget": [{"text": "why hasn't humanity expanded to live on other planets in our solar system?", "example_title": "solar system"}, {"text": "question: ... | pszemraj/t5-base-askscience-lfqa | null | [
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"safetensors",
"t5",
"text2text-generation",
"qa",
"askscience",
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"information retrieval",
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"dataset:vblagoje/lfqa",
"base_model:google/t5-v1_1-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-gener... | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #qa #askscience #lfqa #information retrieval #en #dataset-vblagoje/lfqa #base_model-google/t5-v1_1-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# checkpoints
- This model is a fine-tuned version of google/t5-v1_1-base on the 'vblagoje/lfqa' dataset, with training duration of 2 epochs, for a (_somewhat_) apples-to-apples comparison with t5-base on the standard eli5 dataset.
- This checkpoint does seem to be more coherent than t5-base on the original datase... | [
"# checkpoints\n\n- This model is a fine-tuned version of google/t5-v1_1-base on the 'vblagoje/lfqa' dataset, with training duration of 2 epochs, for a (_somewhat_) apples-to-apples comparison with t5-base on the standard eli5 dataset.\n - This checkpoint does seem to be more coherent than t5-base on the original ... | [
"TAGS\n#transformers #pytorch #safetensors #t5 #text2text-generation #qa #askscience #lfqa #information retrieval #en #dataset-vblagoje/lfqa #base_model-google/t5-v1_1-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# checkpoints\n\n- Thi... |
text2text-generation | transformers |
# t5 - base- askscience
- [t5-v1_1](https://huggingface.co/google/t5-v1_1-base) trained on the entirety of the _askscience_ sub-section of the eli5 dataset for one epoch.
- compare to bart on eli5 [here](https://huggingface.co/yjernite/bart_eli5)
- note that for the inference API, the model is restricted to outputtin... | {"language": ["en"], "tags": ["t5", "qa", "askscience", "lfqa", "information retrieval"], "datasets": ["eli5"], "metrics": ["rouge"], "widget": [{"text": "why aren't there more planets in our solar system?", "example_title": "solar system"}, {"text": "question: what is a probability distribution? context: I am just lea... | pszemraj/t5-base-askscience | null | [
"transformers",
"pytorch",
"safetensors",
"t5",
"text2text-generation",
"qa",
"askscience",
"lfqa",
"information retrieval",
"en",
"dataset:eli5",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #qa #askscience #lfqa #information retrieval #en #dataset-eli5 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# t5 - base- askscience
- t5-v1_1 trained on the entirety of the _askscience_ sub-section of the eli5 dataset for one epoch.
- compare to bart on eli5 here
- note that for the inference API, the model is restricted to outputting 96 tokens - by using the model in python with the transformers library, you can get longe... | [
"# t5 - base- askscience\n\n- t5-v1_1 trained on the entirety of the _askscience_ sub-section of the eli5 dataset for one epoch.\n- compare to bart on eli5 here\n- note that for the inference API, the model is restricted to outputting 96 tokens - by using the model in python with the transformers library, you can g... | [
"TAGS\n#transformers #pytorch #safetensors #t5 #text2text-generation #qa #askscience #lfqa #information retrieval #en #dataset-eli5 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# t5 - base- askscience\n\n- t5-v1_1 trained on the entirety of the _askscience_ sub-section o... |
text2text-generation | transformers |
# T5-large for Lexical Analysis
- This model was trained a text-to-text task with input text as a summary of a chapter, and the output text as the analysis of that chapter on the [booksum](https://arxiv.org/abs/2105.08209) dataset.
- it has somewhat learned how to complete literary analysis on an arbitrary input te... | {"language": ["en"], "license": "bsd-3-clause", "library_name": "transformers", "tags": ["t5", "analysis", "book", "notes"], "datasets": ["kmfoda/booksum"], "metrics": ["rouge"], "widget": [{"text": "I'm just a girl standing in front of a boy asking him to love her.", "example_title": "Notting Hill"}, {"text": "Son, yo... | pszemraj/t5-large-for-lexical-analysis | null | [
"transformers",
"pytorch",
"onnx",
"safetensors",
"t5",
"text2text-generation",
"analysis",
"book",
"notes",
"en",
"dataset:kmfoda/booksum",
"arxiv:2105.08209",
"license:bsd-3-clause",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2105.08209"
] | [
"en"
] | TAGS
#transformers #pytorch #onnx #safetensors #t5 #text2text-generation #analysis #book #notes #en #dataset-kmfoda/booksum #arxiv-2105.08209 #license-bsd-3-clause #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# T5-large for Lexical Analysis
- This model was trained a text-to-text task with input text as a summary of a chapter, and the output text as the analysis of that chapter on the booksum dataset.
- it has somewhat learned how to complete literary analysis on an arbitrary input text.
- NOTE: this is fairly intensive... | [
"# T5-large for Lexical Analysis \n\n- This model was trained a text-to-text task with input text as a summary of a chapter, and the output text as the analysis of that chapter on the booksum dataset.\n- it has somewhat learned how to complete literary analysis on an arbitrary input text.\n- NOTE: this is fairly in... | [
"TAGS\n#transformers #pytorch #onnx #safetensors #t5 #text2text-generation #analysis #book #notes #en #dataset-kmfoda/booksum #arxiv-2105.08209 #license-bsd-3-clause #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# T5-large for Lexical Analysis \n\n- This model was trained... |
text2text-generation | transformers |
# literary analysis with t5-base
- t5 sort-of learning to do literary analysis. It was trained on the booksum dataset with `chapter` (original text) as input and `summary_analysis` as the output text, where `summary_analysis` is the sparknotes/cliff notes/etc analysis
- It was trained for 8 epochs
- Testing may need ... | {"language": ["en"], "tags": ["t5", "analysis", "book", "notes"], "datasets": ["kmfoda/booksum"], "metrics": ["rouge"], "widget": [{"text": "A large drop of sun lingered on the horizon and then dripped over and was gone, and the sky was brilliant over the spot where it had gone, and a torn cloud, like a bloody rag, hun... | pszemraj/t5_1_1-base-writing-analysis | null | [
"transformers",
"pytorch",
"safetensors",
"t5",
"text2text-generation",
"analysis",
"book",
"notes",
"en",
"dataset:kmfoda/booksum",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #analysis #book #notes #en #dataset-kmfoda/booksum #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# literary analysis with t5-base
- t5 sort-of learning to do literary analysis. It was trained on the booksum dataset with 'chapter' (original text) as input and 'summary_analysis' as the output text, where 'summary_analysis' is the sparknotes/cliff notes/etc analysis
- It was trained for 8 epochs
- Testing may need ... | [
"# literary analysis with t5-base\n\n- t5 sort-of learning to do literary analysis. It was trained on the booksum dataset with 'chapter' (original text) as input and 'summary_analysis' as the output text, where 'summary_analysis' is the sparknotes/cliff notes/etc analysis\n- It was trained for 8 epochs\n- Testing m... | [
"TAGS\n#transformers #pytorch #safetensors #t5 #text2text-generation #analysis #book #notes #en #dataset-kmfoda/booksum #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# literary analysis with t5-base\n\n- t5 sort-of learning to do literary analysis. It was trained on the b... |
text-classification | transformers |
# yacis-electra-small-cyberbullying
This is an [ELECTRA](https://github.com/google-research/electra) Small model for the Japanese language finetuned for automatic cyberbullying detection.
The original foundation model was originally pretrained on 5.6 billion words [YACIS](https://github.com/ptaszynski/yacis-corpus)... | {"language": "ja", "license": "cc-by-sa-4.0", "datasets": ["YACIS corpus", "Harmful BBS Japanese comments dataset", "Twitter Japanese cyberbullying dataset"]} | ptaszynski/yacis-electra-small-japanese-cyberbullying | null | [
"transformers",
"pytorch",
"electra",
"text-classification",
"ja",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #electra #text-classification #ja #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# yacis-electra-small-cyberbullying
This is an ELECTRA Small model for the Japanese language finetuned for automatic cyberbullying detection.
The original foundation model was originally pretrained on 5.6 billion words YACIS blog corpus, and later finetuned on a balanced dataset created by unifying two datasets, na... | [
"# yacis-electra-small-cyberbullying\n\nThis is an ELECTRA Small model for the Japanese language finetuned for automatic cyberbullying detection. \n\nThe original foundation model was originally pretrained on 5.6 billion words YACIS blog corpus, and later finetuned on a balanced dataset created by unifying two data... | [
"TAGS\n#transformers #pytorch #electra #text-classification #ja #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# yacis-electra-small-cyberbullying\n\nThis is an ELECTRA Small model for the Japanese language finetuned for automatic cyberbullying detection. \n\nThe original found... |
null | transformers |
# yacis-electra-small
This is [ELECTRA](https://github.com/google-research/electra) Small model for Japanese pretrained on 354 million sentences / 5.6 billion words of [YACIS](https://github.com/ptaszynski/yacis-corpus) blog corpus.
The corpus was tokenized for pretraining with [MeCab](https://taku910.github.io/meca... | {"language": "ja", "license": "cc-by-sa-4.0", "datasets": ["YACIS corpus"]} | ptaszynski/yacis-electra-small-japanese | null | [
"transformers",
"pytorch",
"ja",
"license:cc-by-sa-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #ja #license-cc-by-sa-4.0 #endpoints_compatible #region-us
|
# yacis-electra-small
This is ELECTRA Small model for Japanese pretrained on 354 million sentences / 5.6 billion words of YACIS blog corpus.
The corpus was tokenized for pretraining with MeCab. Subword tokenization was done with WordPiece.
## Model architecture
This model uses ELECTRA Small model settings, 12 lay... | [
"# yacis-electra-small\n\nThis is ELECTRA Small model for Japanese pretrained on 354 million sentences / 5.6 billion words of YACIS blog corpus.\n\nThe corpus was tokenized for pretraining with MeCab. Subword tokenization was done with WordPiece.",
"## Model architecture\n\nThis model uses ELECTRA Small model set... | [
"TAGS\n#transformers #pytorch #ja #license-cc-by-sa-4.0 #endpoints_compatible #region-us \n",
"# yacis-electra-small\n\nThis is ELECTRA Small model for Japanese pretrained on 354 million sentences / 5.6 billion words of YACIS blog corpus.\n\nThe corpus was tokenized for pretraining with MeCab. Subword tokenizatio... |
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. -->
# biobert_squad2_cased-finetuned-squad
This model is a fine-tuned version of [clagator/biobert_squad2_cased](https://huggingface.c... | {"tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "biobert_squad2_cased-finetuned-squad", "results": []}]} | ptnv-s/biobert_squad2_cased-finetuned-squad | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #endpoints_compatible #region-us
|
# biobert_squad2_cased-finetuned-squad
This model is a fine-tuned version of clagator/biobert_squad2_cased on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
#... | [
"# biobert_squad2_cased-finetuned-squad\n\nThis model is a fine-tuned version of clagator/biobert_squad2_cased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
... | [
"TAGS\n#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #endpoints_compatible #region-us \n",
"# biobert_squad2_cased-finetuned-squad\n\nThis model is a fine-tuned version of clagator/biobert_squad2_cased on the squad dataset.",
"## Model description\n\nMore information ne... |
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. -->
# model1_test
This model is a fine-tuned version of [DaNLP/da-bert-hatespeech-detection](https://huggingface.co/DaNLP/da-bert-hate... | {"license": "cc-by-sa-4.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "model1_test", "results": []}]} | ptro/model1_test | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
| model1\_test
============
This model is a fine-tuned version of DaNLP/da-bert-hatespeech-detection on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1816
* Accuracy: 0.9667
* F1: 0.3548
Model description
-----------------
More information needed
Intended uses & limitati... | [
"### 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* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch... |
question-answering | transformers | # BioBERTpt-squad-v1.1-portuguese for QA (Question Answering)
This is a clinical and biomedical model trained with generic QA questions. This model was finetuned on SQUAD v1.1, with the dataset SQUAD v1.1 in portuguese, from the Deep Learning Brasil group on Google Colab. See more details [here](https://huggingface.co... | {"language": "pt", "tags": ["question-answering", "bert", "bioBERTpt", "pytorch"], "metrics": ["squad"], "widget": [{"text": "O que \u00e9 AVC?", "context": "O AVC (Acidente vascular cerebral) \u00e9 a segunda principal causa de morte no Brasil e a principal causa de incapacidade em adultos, retirando do mercado de tra... | pucpr/bioBERTpt-squad-v1.1-portuguese | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"question-answering",
"bioBERTpt",
"pt",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"pt"
] | TAGS
#transformers #pytorch #tf #jax #bert #question-answering #bioBERTpt #pt #endpoints_compatible #region-us
| # BioBERTpt-squad-v1.1-portuguese for QA (Question Answering)
This is a clinical and biomedical model trained with generic QA questions. This model was finetuned on SQUAD v1.1, with the dataset SQUAD v1.1 in portuguese, from the Deep Learning Brasil group on Google Colab. See more details here.
## Performance
The res... | [
"# BioBERTpt-squad-v1.1-portuguese for QA (Question Answering)\n\nThis is a clinical and biomedical model trained with generic QA questions. This model was finetuned on SQUAD v1.1, with the dataset SQUAD v1.1 in portuguese, from the Deep Learning Brasil group on Google Colab. See more details here.",
"## Performa... | [
"TAGS\n#transformers #pytorch #tf #jax #bert #question-answering #bioBERTpt #pt #endpoints_compatible #region-us \n",
"# BioBERTpt-squad-v1.1-portuguese for QA (Question Answering)\n\nThis is a clinical and biomedical model trained with generic QA questions. This model was finetuned on SQUAD v1.1, with the datase... |
fill-mask | transformers |
<img src="https://raw.githubusercontent.com/HAILab-PUCPR/BioBERTpt/master/images/logo-biobertpr1.png" alt="Logo BioBERTpt">
# BioBERTpt - Portuguese Clinical and Biomedical BERT
The [BioBERTpt - A Portuguese Neural Language Model for Clinical Named Entity Recognition](https://www.aclweb.org/anthology/2020.clinicalnl... | {"language": "pt", "widget": [{"text": "O paciente recebeu [MASK] do hospital."}, {"text": "O m\u00e9dico receitou a medica\u00e7\u00e3o para controlar a [MASK]."}, {"text": "O principal [MASK] da COVID-19 \u00e9 tosse seca."}, {"text": "O v\u00edrus da gripe apresenta um [MASK] constitu\u00eddo por segmentos de \u00e1... | pucpr/biobertpt-all | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"pt",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"pt"
] | TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #pt #autotrain_compatible #endpoints_compatible #region-us
|
<img src="URL alt="Logo BioBERTpt">
# BioBERTpt - Portuguese Clinical and Biomedical BERT
The BioBERTpt - A Portuguese Neural Language Model for Clinical Named Entity Recognition paper contains clinical and biomedical BERT-based models for Portuguese Language, initialized with BERT-Multilingual-Cased & trained on cl... | [
"# BioBERTpt - Portuguese Clinical and Biomedical BERT\n\nThe BioBERTpt - A Portuguese Neural Language Model for Clinical Named Entity Recognition paper contains clinical and biomedical BERT-based models for Portuguese Language, initialized with BERT-Multilingual-Cased & trained on clinical notes and biomedical lit... | [
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #pt #autotrain_compatible #endpoints_compatible #region-us \n",
"# BioBERTpt - Portuguese Clinical and Biomedical BERT\n\nThe BioBERTpt - A Portuguese Neural Language Model for Clinical Named Entity Recognition paper contains clinical and biomedical BERT-bas... |
fill-mask | transformers |
<img src="https://raw.githubusercontent.com/HAILab-PUCPR/BioBERTpt/master/images/logo-biobertpr1.png" alt="Logo BioBERTpt">
# BioBERTpt - Portuguese Clinical and Biomedical BERT
The [BioBERTpt - A Portuguese Neural Language Model for Clinical Named Entity Recognition](https://www.aclweb.org/anthology/2020.clinicalnl... | {"language": "pt", "widget": [{"text": "O principal [MASK] da COVID-19 \u00e9 tosse seca."}, {"text": "O v\u00edrus da gripe apresenta um [MASK] constitu\u00eddo por segmentos de \u00e1cido ribonucleico."}], "thumbnail": "https://raw.githubusercontent.com/HAILab-PUCPR/BioBERTpt/master/images/logo-biobertpr1.png"} | pucpr/biobertpt-bio | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"pt",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"pt"
] | TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #pt #autotrain_compatible #endpoints_compatible #region-us
|
<img src="URL alt="Logo BioBERTpt">
# BioBERTpt - Portuguese Clinical and Biomedical BERT
The BioBERTpt - A Portuguese Neural Language Model for Clinical Named Entity Recognition paper contains clinical and biomedical BERT-based models for Portuguese Language, initialized with BERT-Multilingual-Cased & trained on cl... | [
"# BioBERTpt - Portuguese Clinical and Biomedical BERT\n\nThe BioBERTpt - A Portuguese Neural Language Model for Clinical Named Entity Recognition paper contains clinical and biomedical BERT-based models for Portuguese Language, initialized with BERT-Multilingual-Cased & trained on clinical notes and biomedical lit... | [
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #pt #autotrain_compatible #endpoints_compatible #region-us \n",
"# BioBERTpt - Portuguese Clinical and Biomedical BERT\n\nThe BioBERTpt - A Portuguese Neural Language Model for Clinical Named Entity Recognition paper contains clinical and biomedical BERT-bas... |
fill-mask | transformers |
<img src="https://raw.githubusercontent.com/HAILab-PUCPR/BioBERTpt/master/images/logo-biobertpr1.png" alt="Logo BioBERTpt">
# BioBERTpt - Portuguese Clinical and Biomedical BERT
The [BioBERTpt - A Portuguese Neural Language Model for Clinical Named Entity Recognition](https://www.aclweb.org/anthology/2020.clinicalnl... | {"language": "pt", "widget": [{"text": "O paciente recebeu [MASK] do hospital."}, {"text": "O m\u00e9dico receitou a medica\u00e7\u00e3o para controlar a [MASK]."}], "thumbnail": "https://raw.githubusercontent.com/HAILab-PUCPR/BioBERTpt/master/images/logo-biobertpr1.png"} | pucpr/biobertpt-clin | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"pt",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"pt"
] | TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #pt #autotrain_compatible #endpoints_compatible #region-us
|
<img src="URL alt="Logo BioBERTpt">
# BioBERTpt - Portuguese Clinical and Biomedical BERT
The BioBERTpt - A Portuguese Neural Language Model for Clinical Named Entity Recognition paper contains clinical and biomedical BERT-based models for Portuguese Language, initialized with BERT-Multilingual-Cased & trained on cl... | [
"# BioBERTpt - Portuguese Clinical and Biomedical BERT\n\nThe BioBERTpt - A Portuguese Neural Language Model for Clinical Named Entity Recognition paper contains clinical and biomedical BERT-based models for Portuguese Language, initialized with BERT-Multilingual-Cased & trained on clinical notes and biomedical lit... | [
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #pt #autotrain_compatible #endpoints_compatible #region-us \n",
"# BioBERTpt - Portuguese Clinical and Biomedical BERT\n\nThe BioBERTpt - A Portuguese Neural Language Model for Clinical Named Entity Recognition paper contains clinical and biomedical BERT-bas... |
token-classification | transformers |
<img src="https://raw.githubusercontent.com/HAILab-PUCPR/BioBERTpt/master/images/logo-biobertpr1.png" alt="Logo BioBERTpt">
# Portuguese Clinical NER - Chemical & Drugs
The Chemical&Drugs NER model is part of the [BioBERTpt project](https://www.aclweb.org/anthology/2020.clinicalnlp-1.7/), where 13 models of clinical... | {"language": "pt", "datasets": ["SemClinBr"], "widget": [{"text": "Dispneia venoso central em subclavia D duplolumen recebendo solu\u00e7\u00e3o salina e glicosada em BI."}, {"text": "Paciente com Sepse pulmonar em D8 tazocin (paciente n\u00e3o recebeu por 2 dias Atb)."}, {"text": "FOI REALIZADO CURSO DE ATB COM LEVOFL... | pucpr/clinicalnerpt-chemical | null | [
"transformers",
"pytorch",
"jax",
"bert",
"token-classification",
"pt",
"dataset:SemClinBr",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"pt"
] | TAGS
#transformers #pytorch #jax #bert #token-classification #pt #dataset-SemClinBr #autotrain_compatible #endpoints_compatible #has_space #region-us
|
<img src="URL alt="Logo BioBERTpt">
# Portuguese Clinical NER - Chemical & Drugs
The Chemical&Drugs NER model is part of the BioBERTpt project, where 13 models of clinical entities (compatible with UMLS) were trained. All NER model from "pucpr" user was trained from the Brazilian clinical corpus SemClinBr, with 10 e... | [
"# Portuguese Clinical NER - Chemical & Drugs\n\nThe Chemical&Drugs NER model is part of the BioBERTpt project, where 13 models of clinical entities (compatible with UMLS) were trained. All NER model from \"pucpr\" user was trained from the Brazilian clinical corpus SemClinBr, with 10 epochs and IOB2 format, from B... | [
"TAGS\n#transformers #pytorch #jax #bert #token-classification #pt #dataset-SemClinBr #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Portuguese Clinical NER - Chemical & Drugs\n\nThe Chemical&Drugs NER model is part of the BioBERTpt project, where 13 models of clinical entities (compati... |
token-classification | transformers |
<img src="https://raw.githubusercontent.com/HAILab-PUCPR/BioBERTpt/master/images/logo-biobertpr1.png" alt="Logo BioBERTpt">
# Portuguese Clinical NER - Diagnostic
The Diagnostic NER model is part of the [BioBERTpt project](https://www.aclweb.org/anthology/2020.clinicalnlp-1.7/), where 13 models of clinical entities ... | {"language": "pt", "datasets": ["SemClinBr"], "widget": [{"text": "Uretrocistografia miccional, residuo pos miccional significativo."}, {"text": "No exame, apresentou apenas leve hiperemia no local do choque."}], "thumbnail": "https://raw.githubusercontent.com/HAILab-PUCPR/BioBERTpt/master/images/logo-biobertpr1.png"} | pucpr/clinicalnerpt-diagnostic | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"pt",
"dataset:SemClinBr",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"pt"
] | TAGS
#transformers #pytorch #bert #token-classification #pt #dataset-SemClinBr #autotrain_compatible #endpoints_compatible #region-us
|
<img src="URL alt="Logo BioBERTpt">
# Portuguese Clinical NER - Diagnostic
The Diagnostic NER model is part of the BioBERTpt project, where 13 models of clinical entities (compatible with UMLS) were trained. All NER model from "pucpr" user was trained from the Brazilian clinical corpus SemClinBr, with 10 epochs and ... | [
"# Portuguese Clinical NER - Diagnostic\n\nThe Diagnostic NER model is part of the BioBERTpt project, where 13 models of clinical entities (compatible with UMLS) were trained. All NER model from \"pucpr\" user was trained from the Brazilian clinical corpus SemClinBr, with 10 epochs and IOB2 format, from BioBERTpt(a... | [
"TAGS\n#transformers #pytorch #bert #token-classification #pt #dataset-SemClinBr #autotrain_compatible #endpoints_compatible #region-us \n",
"# Portuguese Clinical NER - Diagnostic\n\nThe Diagnostic NER model is part of the BioBERTpt project, where 13 models of clinical entities (compatible with UMLS) were traine... |
token-classification | transformers |
<img src="https://raw.githubusercontent.com/HAILab-PUCPR/BioBERTpt/master/images/logo-biobertpr1.png" alt="Logo BioBERTpt">
# Portuguese Clinical NER - Disease
The Disease NER model is part of the [BioBERTpt project](https://www.aclweb.org/anthology/2020.clinicalnlp-1.7/), where 13 models of clinical entities (compa... | {"language": "pt", "datasets": ["SemClinBr"], "widget": [{"text": "DEVIDO AO FATO DE TER DPOC E APRESENTADO DISFUN\u00c7\u00c3O RESPIRAT\u00d3RIA AGUDA COM INFILTRADO PULMONAR EM BASE DIREITA"}, {"text": "Paciente com Sepse pulmonar em D8 tazocin (paciente n\u00e3o recebeu por 2 dias Atb)."}], "thumbnail": "https://raw... | pucpr/clinicalnerpt-disease | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"pt",
"dataset:SemClinBr",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"pt"
] | TAGS
#transformers #pytorch #bert #token-classification #pt #dataset-SemClinBr #autotrain_compatible #endpoints_compatible #has_space #region-us
|
<img src="URL alt="Logo BioBERTpt">
# Portuguese Clinical NER - Disease
The Disease NER model is part of the BioBERTpt project, where 13 models of clinical entities (compatible with UMLS) were trained. All NER model from "pucpr" user was trained from the Brazilian clinical corpus SemClinBr, with 10 epochs and IOB2 f... | [
"# Portuguese Clinical NER - Disease\n\nThe Disease NER model is part of the BioBERTpt project, where 13 models of clinical entities (compatible with UMLS) were trained. All NER model from \"pucpr\" user was trained from the Brazilian clinical corpus SemClinBr, with 10 epochs and IOB2 format, from BioBERTpt(all) mo... | [
"TAGS\n#transformers #pytorch #bert #token-classification #pt #dataset-SemClinBr #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Portuguese Clinical NER - Disease\n\nThe Disease NER model is part of the BioBERTpt project, where 13 models of clinical entities (compatible with UMLS) were t... |
token-classification | transformers |
<img src="https://raw.githubusercontent.com/HAILab-PUCPR/BioBERTpt/master/images/logo-biobertpr1.png" alt="Logo BioBERTpt">
# Portuguese Clinical NER - Disorder
The Disorder NER model is part of the [BioBERTpt project](https://www.aclweb.org/anthology/2020.clinicalnlp-1.7/), where 13 models of clinical entities (com... | {"language": "pt", "datasets": ["SemClinBr"], "widget": [{"text": "PACIENTE DE 69 ANOS COM ICC DE ETIOLOGIA ISQU\u00caMICA "}, {"text": "Paciente com Sepse pulmonar em D8 tazocin (paciente n\u00e3o recebeu por 2 dias Atb)."}], "thumbnail": "https://raw.githubusercontent.com/HAILab-PUCPR/BioBERTpt/master/images/logo-bio... | pucpr/clinicalnerpt-disorder | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"pt",
"dataset:SemClinBr",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"pt"
] | TAGS
#transformers #pytorch #bert #token-classification #pt #dataset-SemClinBr #autotrain_compatible #endpoints_compatible #region-us
|
<img src="URL alt="Logo BioBERTpt">
# Portuguese Clinical NER - Disorder
The Disorder NER model is part of the BioBERTpt project, where 13 models of clinical entities (compatible with UMLS) were trained. All NER model from "pucpr" user was trained from the Brazilian clinical corpus SemClinBr, with 10 epochs and IOB2... | [
"# Portuguese Clinical NER - Disorder\n\nThe Disorder NER model is part of the BioBERTpt project, where 13 models of clinical entities (compatible with UMLS) were trained. All NER model from \"pucpr\" user was trained from the Brazilian clinical corpus SemClinBr, with 10 epochs and IOB2 format, from BioBERTpt(all) ... | [
"TAGS\n#transformers #pytorch #bert #token-classification #pt #dataset-SemClinBr #autotrain_compatible #endpoints_compatible #region-us \n",
"# Portuguese Clinical NER - Disorder\n\nThe Disorder NER model is part of the BioBERTpt project, where 13 models of clinical entities (compatible with UMLS) were trained. A... |
token-classification | transformers |
<img src="https://raw.githubusercontent.com/HAILab-PUCPR/BioBERTpt/master/images/logo-biobertpr1.png" alt="Logo BioBERTpt">
# Portuguese Clinical NER - Finding
The Finding NER model is part of the [BioBERTpt project](https://www.aclweb.org/anthology/2020.clinicalnlp-1.7/), where 13 models of clinical entities (compa... | {"language": "pt", "datasets": ["SemClinBr"], "widget": [{"text": "RECEBE ALTA EM BOM ESTADO GERAL, COM PLANO DE ACOMPANHAR NO AMBULAT\u00d3RIO."}, {"text": "PACIENTE APRESENTOU BOA EVOLU\u00c7\u00c3O CL\u00cdNICA AP\u00d3S OTIMIZA\u00c7\u00c3O DO TTO DA ICC."}], "thumbnail": "https://raw.githubusercontent.com/HAILab-... | pucpr/clinicalnerpt-finding | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"pt",
"dataset:SemClinBr",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"pt"
] | TAGS
#transformers #pytorch #bert #token-classification #pt #dataset-SemClinBr #autotrain_compatible #endpoints_compatible #region-us
|
<img src="URL alt="Logo BioBERTpt">
# Portuguese Clinical NER - Finding
The Finding NER model is part of the BioBERTpt project, where 13 models of clinical entities (compatible with UMLS) were trained. All NER model from "pucpr" user was trained from the Brazilian clinical corpus SemClinBr, with 10 epochs and IOB2 f... | [
"# Portuguese Clinical NER - Finding\n\nThe Finding NER model is part of the BioBERTpt project, where 13 models of clinical entities (compatible with UMLS) were trained. All NER model from \"pucpr\" user was trained from the Brazilian clinical corpus SemClinBr, with 10 epochs and IOB2 format, from BioBERTpt(all) mo... | [
"TAGS\n#transformers #pytorch #bert #token-classification #pt #dataset-SemClinBr #autotrain_compatible #endpoints_compatible #region-us \n",
"# Portuguese Clinical NER - Finding\n\nThe Finding NER model is part of the BioBERTpt project, where 13 models of clinical entities (compatible with UMLS) were trained. All... |
token-classification | transformers |
<img src="https://raw.githubusercontent.com/HAILab-PUCPR/BioBERTpt/master/images/logo-biobertpr1.png" alt="Logo BioBERTpt">
# Portuguese Clinical NER - HealthCare
The HealthCare NER model is part of the [BioBERTpt project](https://www.aclweb.org/anthology/2020.clinicalnlp-1.7/), where 13 models of clinical entities ... | {"language": "pt", "datasets": ["SemClinBr"], "widget": [{"text": "Acompanhamento da diabetes, paciente encaminhado da unidade de sa\u00fade."}, {"text": "Paciente encaminhado por altera\u00e7\u00e3o na fun\u00e7\u00e3o renal."}], "thumbnail": "https://raw.githubusercontent.com/HAILab-PUCPR/BioBERTpt/master/images/logo... | pucpr/clinicalnerpt-healthcare | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"pt",
"dataset:SemClinBr",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"pt"
] | TAGS
#transformers #pytorch #bert #token-classification #pt #dataset-SemClinBr #autotrain_compatible #endpoints_compatible #region-us
|
<img src="URL alt="Logo BioBERTpt">
# Portuguese Clinical NER - HealthCare
The HealthCare NER model is part of the BioBERTpt project, where 13 models of clinical entities (compatible with UMLS) were trained. All NER model from "pucpr" user was trained from the Brazilian clinical corpus SemClinBr, with 10 epochs and ... | [
"# Portuguese Clinical NER - HealthCare\n\nThe HealthCare NER model is part of the BioBERTpt project, where 13 models of clinical entities (compatible with UMLS) were trained. All NER model from \"pucpr\" user was trained from the Brazilian clinical corpus SemClinBr, with 10 epochs and IOB2 format, from BioBERTpt(a... | [
"TAGS\n#transformers #pytorch #bert #token-classification #pt #dataset-SemClinBr #autotrain_compatible #endpoints_compatible #region-us \n",
"# Portuguese Clinical NER - HealthCare\n\nThe HealthCare NER model is part of the BioBERTpt project, where 13 models of clinical entities (compatible with UMLS) were traine... |
token-classification | transformers |
<img src="https://raw.githubusercontent.com/HAILab-PUCPR/BioBERTpt/master/images/logo-biobertpr1.png" alt="Logo BioBERTpt">
# Portuguese Clinical NER - Laboratory
The Laboratory NER model is part of the [BioBERTpt project](https://www.aclweb.org/anthology/2020.clinicalnlp-1.7/), where 13 models of clinical entities ... | {"language": "pt", "datasets": ["SemClinBr"], "widget": [{"text": "Exame de creatinina urinaria: 41, 8 mg/dL."}, {"text": "Parcial de urina com 150mg/dL de priteinas, ph de 5,0 e 1034 leucocitos."}], "thumbnail": "https://raw.githubusercontent.com/HAILab-PUCPR/BioBERTpt/master/images/logo-biobertpr1.png"} | pucpr/clinicalnerpt-laboratory | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"pt",
"dataset:SemClinBr",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"pt"
] | TAGS
#transformers #pytorch #bert #token-classification #pt #dataset-SemClinBr #autotrain_compatible #endpoints_compatible #region-us
|
<img src="URL alt="Logo BioBERTpt">
# Portuguese Clinical NER - Laboratory
The Laboratory NER model is part of the BioBERTpt project, where 13 models of clinical entities (compatible with UMLS) were trained. All NER model from "pucpr" user was trained from the Brazilian clinical corpus SemClinBr, with 10 epochs and ... | [
"# Portuguese Clinical NER - Laboratory\n\nThe Laboratory NER model is part of the BioBERTpt project, where 13 models of clinical entities (compatible with UMLS) were trained. All NER model from \"pucpr\" user was trained from the Brazilian clinical corpus SemClinBr, with 10 epochs and IOB2 format, from BioBERTpt(a... | [
"TAGS\n#transformers #pytorch #bert #token-classification #pt #dataset-SemClinBr #autotrain_compatible #endpoints_compatible #region-us \n",
"# Portuguese Clinical NER - Laboratory\n\nThe Laboratory NER model is part of the BioBERTpt project, where 13 models of clinical entities (compatible with UMLS) were traine... |
token-classification | transformers |
<img src="https://raw.githubusercontent.com/HAILab-PUCPR/BioBERTpt/master/images/logo-biobertpr1.png" alt="Logo BioBERTpt">
# Portuguese Clinical NER - Medical
The Medical NER model is part of the [BioBERTpt project](https://www.aclweb.org/anthology/2020.clinicalnlp-1.7/), where 13 models of clinical entities (compa... | {"language": "pt", "datasets": ["SemClinBr"], "widget": [{"text": "Hoje realizou avaliacao de mp-cdi, com eletrodos atrial e ventricular."}, {"text": "Paciente encaminhado a c\u00e2mera hiperb\u00e1rica no per\u00edodo da tarde."}], "thumbnail": "https://raw.githubusercontent.com/HAILab-PUCPR/BioBERTpt/master/images/lo... | pucpr/clinicalnerpt-medical | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"pt",
"dataset:SemClinBr",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"pt"
] | TAGS
#transformers #pytorch #bert #token-classification #pt #dataset-SemClinBr #autotrain_compatible #endpoints_compatible #region-us
|
<img src="URL alt="Logo BioBERTpt">
# Portuguese Clinical NER - Medical
The Medical NER model is part of the BioBERTpt project, where 13 models of clinical entities (compatible with UMLS) were trained. All NER model from "pucpr" user was trained from the Brazilian clinical corpus SemClinBr, with 10 epochs and IOB2 f... | [
"# Portuguese Clinical NER - Medical\n\nThe Medical NER model is part of the BioBERTpt project, where 13 models of clinical entities (compatible with UMLS) were trained. All NER model from \"pucpr\" user was trained from the Brazilian clinical corpus SemClinBr, with 10 epochs and IOB2 format, from BioBERTpt(all) mo... | [
"TAGS\n#transformers #pytorch #bert #token-classification #pt #dataset-SemClinBr #autotrain_compatible #endpoints_compatible #region-us \n",
"# Portuguese Clinical NER - Medical\n\nThe Medical NER model is part of the BioBERTpt project, where 13 models of clinical entities (compatible with UMLS) were trained. All... |
token-classification | transformers |
<img src="https://raw.githubusercontent.com/HAILab-PUCPR/BioBERTpt/master/images/logo-biobertpr1.png" alt="Logo BioBERTpt">
# Portuguese Clinical NER - Pharmacologic
The Pharmacologic NER model is part of the [BioBERTpt project](https://www.aclweb.org/anthology/2020.clinicalnlp-1.7/), where 13 models of clinical ent... | {"language": "pt", "datasets": ["SemClinBr"], "widget": [{"text": "COMO ESQUEMA DE MEDICA\u00c7\u00c3O PARA ICC PRESCRITO NO ALTA, RECEBE FUROSEMIDA 40 BID, ISOSSORBIDA 40 TID, DIGOXINA 0,25 /D, CAPTOPRIL 50 TID E ESPIRONOLACTONA 25 /D."}, {"text": "ESTAVA EM USO DE FUROSEMIDA 40 BID, DIGOXINA 0,25 /D, SINVASTATINA 40 ... | pucpr/clinicalnerpt-pharmacologic | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"pt",
"dataset:SemClinBr",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"pt"
] | TAGS
#transformers #pytorch #bert #token-classification #pt #dataset-SemClinBr #autotrain_compatible #endpoints_compatible #region-us
|
<img src="URL alt="Logo BioBERTpt">
# Portuguese Clinical NER - Pharmacologic
The Pharmacologic NER model is part of the BioBERTpt project, where 13 models of clinical entities (compatible with UMLS) were trained. All NER model from "pucpr" user was trained from the Brazilian clinical corpus SemClinBr, with 10 epoch... | [
"# Portuguese Clinical NER - Pharmacologic\n\nThe Pharmacologic NER model is part of the BioBERTpt project, where 13 models of clinical entities (compatible with UMLS) were trained. All NER model from \"pucpr\" user was trained from the Brazilian clinical corpus SemClinBr, with 10 epochs and IOB2 format, from BioBE... | [
"TAGS\n#transformers #pytorch #bert #token-classification #pt #dataset-SemClinBr #autotrain_compatible #endpoints_compatible #region-us \n",
"# Portuguese Clinical NER - Pharmacologic\n\nThe Pharmacologic NER model is part of the BioBERTpt project, where 13 models of clinical entities (compatible with UMLS) were ... |
token-classification | transformers |
<img src="https://raw.githubusercontent.com/HAILab-PUCPR/BioBERTpt/master/images/logo-biobertpr1.png" alt="Logo BioBERTpt">
# Portuguese Clinical NER - Procedure
The Procedure NER model is part of the [BioBERTpt project](https://www.aclweb.org/anthology/2020.clinicalnlp-1.7/), where 13 models of clinical entities (c... | {"language": "pt", "datasets": ["SemClinBr"], "widget": [{"text": "Dispneia venoso central em subclavia D duplolumen recebendo solu\u00e7\u00e3o salina e glicosada em BI."}, {"text": "FOI REALIZADO CURSO DE ATB COM LEVOFLOXACINA POR 7 DIAS."}], "thumbnail": "https://raw.githubusercontent.com/HAILab-PUCPR/BioBERTpt/mast... | pucpr/clinicalnerpt-procedure | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"pt",
"dataset:SemClinBr",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"pt"
] | TAGS
#transformers #pytorch #bert #token-classification #pt #dataset-SemClinBr #autotrain_compatible #endpoints_compatible #region-us
|
<img src="URL alt="Logo BioBERTpt">
# Portuguese Clinical NER - Procedure
The Procedure NER model is part of the BioBERTpt project, where 13 models of clinical entities (compatible with UMLS) were trained. All NER model from "pucpr" user was trained from the Brazilian clinical corpus SemClinBr, with 10 epochs and IO... | [
"# Portuguese Clinical NER - Procedure\n\nThe Procedure NER model is part of the BioBERTpt project, where 13 models of clinical entities (compatible with UMLS) were trained. All NER model from \"pucpr\" user was trained from the Brazilian clinical corpus SemClinBr, with 10 epochs and IOB2 format, from BioBERTpt(all... | [
"TAGS\n#transformers #pytorch #bert #token-classification #pt #dataset-SemClinBr #autotrain_compatible #endpoints_compatible #region-us \n",
"# Portuguese Clinical NER - Procedure\n\nThe Procedure NER model is part of the BioBERTpt project, where 13 models of clinical entities (compatible with UMLS) were trained.... |
token-classification | transformers |
<img src="https://raw.githubusercontent.com/HAILab-PUCPR/BioBERTpt/master/images/logo-biobertpr1.png" alt="Logo BioBERTpt">
# Portuguese Clinical NER - Quantitative
The Quantitative NER model is part of the [BioBERTpt project](https://www.aclweb.org/anthology/2020.clinicalnlp-1.7/), where 13 models of clinical entit... | {"language": "pt", "datasets": ["SemClinBr"], "widget": [{"text": "Paciente faz uso de losartana 50mg, HCTZ 25mg DM ha 25 anos."}, {"text": "Paciente com Sepse pulmonar em D8 tazocin (paciente n\u00e3o recebeu por 2 dias Atb)."}], "thumbnail": "https://raw.githubusercontent.com/HAILab-PUCPR/BioBERTpt/master/images/logo... | pucpr/clinicalnerpt-quantitative | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"pt",
"dataset:SemClinBr",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"pt"
] | TAGS
#transformers #pytorch #bert #token-classification #pt #dataset-SemClinBr #autotrain_compatible #endpoints_compatible #region-us
|
<img src="URL alt="Logo BioBERTpt">
# Portuguese Clinical NER - Quantitative
The Quantitative NER model is part of the BioBERTpt project, where 13 models of clinical entities (compatible with UMLS) were trained. All NER model from "pucpr" user was trained from the Brazilian clinical corpus SemClinBr, with 10 epochs ... | [
"# Portuguese Clinical NER - Quantitative\n\nThe Quantitative NER model is part of the BioBERTpt project, where 13 models of clinical entities (compatible with UMLS) were trained. All NER model from \"pucpr\" user was trained from the Brazilian clinical corpus SemClinBr, with 10 epochs and IOB2 format, from BioBERT... | [
"TAGS\n#transformers #pytorch #bert #token-classification #pt #dataset-SemClinBr #autotrain_compatible #endpoints_compatible #region-us \n",
"# Portuguese Clinical NER - Quantitative\n\nThe Quantitative NER model is part of the BioBERTpt project, where 13 models of clinical entities (compatible with UMLS) were tr... |
token-classification | transformers |
<img src="https://raw.githubusercontent.com/HAILab-PUCPR/BioBERTpt/master/images/logo-biobertpr1.png" alt="Logo BioBERTpt">
# Portuguese Clinical NER - Sign
The Sign NER model is part of the [BioBERTpt project](https://www.aclweb.org/anthology/2020.clinicalnlp-1.7/), where 13 models of clinical entities (compatible ... | {"language": "pt", "datasets": ["SemClinBr"], "widget": [{"text": "H\u00e1 15 anos relata dor lombar com irradia\u00e7\u00e3o para coxa direita."}, {"text": "Paciente segue internado, sem presen\u00e7a de edema."}], "thumbnail": "https://raw.githubusercontent.com/HAILab-PUCPR/BioBERTpt/master/images/logo-biobertpr1.png... | pucpr/clinicalnerpt-sign | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"pt",
"dataset:SemClinBr",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"pt"
] | TAGS
#transformers #pytorch #bert #token-classification #pt #dataset-SemClinBr #autotrain_compatible #endpoints_compatible #region-us
|
<img src="URL alt="Logo BioBERTpt">
# Portuguese Clinical NER - Sign
The Sign NER model is part of the BioBERTpt project, where 13 models of clinical entities (compatible with UMLS) were trained. All NER model from "pucpr" user was trained from the Brazilian clinical corpus SemClinBr, with 10 epochs and IOB2 format,... | [
"# Portuguese Clinical NER - Sign\n\nThe Sign NER model is part of the BioBERTpt project, where 13 models of clinical entities (compatible with UMLS) were trained. All NER model from \"pucpr\" user was trained from the Brazilian clinical corpus SemClinBr, with 10 epochs and IOB2 format, from BioBERTpt(all) model.",... | [
"TAGS\n#transformers #pytorch #bert #token-classification #pt #dataset-SemClinBr #autotrain_compatible #endpoints_compatible #region-us \n",
"# Portuguese Clinical NER - Sign\n\nThe Sign NER model is part of the BioBERTpt project, where 13 models of clinical entities (compatible with UMLS) were trained. All NER m... |
token-classification | transformers |
<img src="https://raw.githubusercontent.com/HAILab-PUCPR/BioBERTpt/master/images/logo-biobertpr1.png" alt="Logo BioBERTpt">
# Portuguese Clinical NER - Therapeutic
The Therapeutic NER model is part of the [BioBERTpt project](https://www.aclweb.org/anthology/2020.clinicalnlp-1.7/), where 13 models of clinical entitie... | {"language": "pt", "datasets": ["SemClinBr"], "widget": [{"text": "Dispneia venoso central em subclavia D duplolumen recebendo solu\u00e7\u00e3o salina e glicosada em BI."}, {"text": "Paciente com Sepse pulmonar em D8 tazocin (paciente n\u00e3o recebeu por 2 dias Atb)."}, {"text": "FOI REALIZADO CURSO DE ATB COM LEVOFL... | pucpr/clinicalnerpt-therapeutic | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"pt",
"dataset:SemClinBr",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"pt"
] | TAGS
#transformers #pytorch #bert #token-classification #pt #dataset-SemClinBr #autotrain_compatible #endpoints_compatible #region-us
|
<img src="URL alt="Logo BioBERTpt">
# Portuguese Clinical NER - Therapeutic
The Therapeutic NER model is part of the BioBERTpt project, where 13 models of clinical entities (compatible with UMLS) were trained. All NER model from "pucpr" user was trained from the Brazilian clinical corpus SemClinBr, with 10 epochs an... | [
"# Portuguese Clinical NER - Therapeutic\n\nThe Therapeutic NER model is part of the BioBERTpt project, where 13 models of clinical entities (compatible with UMLS) were trained. All NER model from \"pucpr\" user was trained from the Brazilian clinical corpus SemClinBr, with 10 epochs and IOB2 format, from BioBERTpt... | [
"TAGS\n#transformers #pytorch #bert #token-classification #pt #dataset-SemClinBr #autotrain_compatible #endpoints_compatible #region-us \n",
"# Portuguese Clinical NER - Therapeutic\n\nThe Therapeutic NER model is part of the BioBERTpt project, where 13 models of clinical entities (compatible with UMLS) were trai... |
token-classification | transformers | eHelpBERTpt | {} | pucpr/eHelpBERTpt | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us
| eHelpBERTpt | [] | [
"TAGS\n#transformers #pytorch #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-generation | transformers |
<img src="https://raw.githubusercontent.com/HAILab-PUCPR/gpt2-bio-pt/main/img/logo-gpt2-bio-pt.png" alt="Logo GPt2-Bio-Pt">
# GPT2-BioPT - a Language Model for Portuguese Biomedical text generation
## Introduction
GPT2-BioPT (Portuguese Biomedical GPT-2 small) is a language model for Portuguese based on the OpenAI ... | {"language": "pt", "widget": [{"text": "O paciente recebeu "}, {"text": "A cardiologia provou que "}, {"text": "O paciente chegou no hospital "}, {"text": "Cientistas descobriram que "}, {"text": "O n\u00edvel de atividade biol\u00f3gica "}, {"text": "O DNA e o RNA "}], "thumbnail": "https://raw.githubusercontent.com/H... | pucpr/gpt2-bio-pt | null | [
"transformers",
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"pt",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"pt"
] | TAGS
#transformers #pytorch #tf #jax #gpt2 #text-generation #pt #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
<img src="URL alt="Logo GPt2-Bio-Pt">
# GPT2-BioPT - a Language Model for Portuguese Biomedical text generation
## Introduction
GPT2-BioPT (Portuguese Biomedical GPT-2 small) is a language model for Portuguese based on the OpenAI GPT-2 model, trained from the GPorTuguese-2 with biomedical literature.
We used Trans... | [
"# GPT2-BioPT - a Language Model for Portuguese Biomedical text generation",
"## Introduction\n\nGPT2-BioPT (Portuguese Biomedical GPT-2 small) is a language model for Portuguese based on the OpenAI GPT-2 model, trained from the GPorTuguese-2 with biomedical literature.\n\nWe used Transfer Learning and Fine-tunin... | [
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"# GPT2-BioPT - a Language Model for Portuguese Biomedical text generation",
"## Introduction\n\nGPT2-BioPT (Portuguese Biomedical GPT-2 small) is a language m... |
fill-mask | transformers |
# Portuguese NER- TempClinBr - BioBERTpt(all)
Treinado com BioBERTpt(all), com o corpus TempClinBr.
Metricas:
```
precision recall f1-score support
0 0.75 0.90 0.82 291
1 0.77 1.00 0.87 33
2 1.00 0.25 ... | {"language": "pt", "datasets": ["TempClinBr"], "widget": [{"text": "Dispneia importante aos esfor\u00e7os + dor tipo peso no peito no esfor\u00e7o."}, {"text": "Obeso, has, icc c # cintilografia miocardica para avaliar angina. Discreto edema mmii pricn a esquerda."}, {"text": "Plastia Mitral ( Insuficiencia ), CRM Saf... | pucpr/tempclin-biobertpt-all | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"pt",
"dataset:TempClinBr",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"pt"
] | TAGS
#transformers #pytorch #bert #fill-mask #pt #dataset-TempClinBr #autotrain_compatible #endpoints_compatible #region-us
|
# Portuguese NER- TempClinBr - BioBERTpt(all)
Treinado com BioBERTpt(all), com o corpus TempClinBr.
Metricas:
Parâmetros:
Eval no conjunto de teste - TempClinBr
OBS: Avaliação com tag "O" (label 7), se necessário fazer a média sem essa tag.
Como citar: em breve | [
"# Portuguese NER- TempClinBr - BioBERTpt(all)\n\nTreinado com BioBERTpt(all), com o corpus TempClinBr.\n\nMetricas:\n\n\n\nParâmetros:\n\n\n\nEval no conjunto de teste - TempClinBr\nOBS: Avaliação com tag \"O\" (label 7), se necessário fazer a média sem essa tag.\n\n\n\n\nComo citar: em breve"
] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #pt #dataset-TempClinBr #autotrain_compatible #endpoints_compatible #region-us \n",
"# Portuguese NER- TempClinBr - BioBERTpt(all)\n\nTreinado com BioBERTpt(all), com o corpus TempClinBr.\n\nMetricas:\n\n\n\nParâmetros:\n\n\n\nEval no conjunto de teste - TempClinBr\n... |
fill-mask | transformers | The language model trained on a fill-mask task with all the North American parent's data in CHILDES.
The parent's data can be found here: https://github.com/xiaomeng-ma/CHILDES
| {} | pulp/CHILDES-ParentBERTo | 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
| The language model trained on a fill-mask task with all the North American parent's data in CHILDES.
The parent's data can be found here: URL
| [] | [
"TAGS\n#transformers #pytorch #jax #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers | This is a Roberta-based model trained on parents' input before 4 years old. | {} | pulp/ParentBERTo-4-years-old | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| This is a Roberta-based model trained on parents' input before 4 years old. | [] | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-generation | transformers |
# Spider-Man DialoGPT Model | {"tags": ["conversational"]} | puugz/DialoGPT-small-spiderman | 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
|
# Spider-Man DialoGPT Model | [
"# Spider-Man DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Spider-Man DialoGPT Model"
] |
null | transformers |
# LABSE BERT
## Model description
Model for "Language-agnostic BERT Sentence Embedding" paper from Fangxiaoyu Feng, Yinfei Yang, Daniel Cer, Naveen Arivazhagan, Wei Wang. Model available in [TensorFlow Hub](https://tfhub.dev/google/LaBSE/1).
## Intended uses & limitations
#### How to use
```python
from transforme... | {"language": "en", "license": "apache-2.0", "tags": ["bert", "embeddings"]} | pvl/labse_bert | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"pretraining",
"embeddings",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #bert #pretraining #embeddings #en #license-apache-2.0 #endpoints_compatible #region-us
|
# LABSE BERT
## Model description
Model for "Language-agnostic BERT Sentence Embedding" paper from Fangxiaoyu Feng, Yinfei Yang, Daniel Cer, Naveen Arivazhagan, Wei Wang. Model available in TensorFlow Hub.
## Intended uses & limitations
#### How to use
| [
"# LABSE BERT",
"## Model description\n\nModel for \"Language-agnostic BERT Sentence Embedding\" paper from Fangxiaoyu Feng, Yinfei Yang, Daniel Cer, Naveen Arivazhagan, Wei Wang. Model available in TensorFlow Hub.",
"## Intended uses & limitations",
"#### How to use"
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"## Model description\n\nModel for \"Language-agnostic BERT Sentence Embedding\" paper from Fangxiaoyu Feng, Yinfei Yang, Daniel Cer, Naveen Arivazhagan, Wei Wang. ... |
null | pyannote-audio |
## Dummy model used for continuous integration purposes
```bash
$ pyannote-audio-train protocol=Debug.SpeakerDiarization.Debug \
task=VoiceActivityDetection \
task.duration=2. \
model=DebugSegmentation \
trainer.max_epochs=10
... | {"license": "mit", "tags": ["pyannote", "pyannote-audio", "pyannote-audio-model"], "inference": false} | pyannote/TestModelForContinuousIntegration | null | [
"pyannote-audio",
"pytorch",
"tensorboard",
"pyannote",
"pyannote-audio-model",
"license:mit",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#pyannote-audio #pytorch #tensorboard #pyannote #pyannote-audio-model #license-mit #region-us
|
## Dummy model used for continuous integration purposes
| [
"## Dummy model used for continuous integration purposes"
] | [
"TAGS\n#pyannote-audio #pytorch #tensorboard #pyannote #pyannote-audio-model #license-mit #region-us \n",
"## Dummy model used for continuous integration purposes"
] |
null | pyannote-audio |
Using this open-source model in production?
Make the most of it thanks to our [consulting services](https://herve.niderb.fr/consulting.html).
# 🎹 Speaker embedding
Relies on pyannote.audio 2.1: see [installation instructions](https://github.com/pyannote/pyannote-audio/).
This model is based on the [canonical x-v... | {"license": "mit", "tags": ["pyannote", "pyannote-audio", "pyannote-audio-model", "audio", "voice", "speech", "speaker", "speaker-recognition", "speaker-verification", "speaker-identification", "speaker-embedding"], "datasets": ["voxceleb"], "inference": false, "extra_gated_prompt": "The collected information will help... | pyannote/embedding | null | [
"pyannote-audio",
"pytorch",
"tensorboard",
"pyannote",
"pyannote-audio-model",
"audio",
"voice",
"speech",
"speaker",
"speaker-recognition",
"speaker-verification",
"speaker-identification",
"speaker-embedding",
"dataset:voxceleb",
"license:mit",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#pyannote-audio #pytorch #tensorboard #pyannote #pyannote-audio-model #audio #voice #speech #speaker #speaker-recognition #speaker-verification #speaker-identification #speaker-embedding #dataset-voxceleb #license-mit #has_space #region-us
|
Using this open-source model in production?
Make the most of it thanks to our consulting services.
# Speaker embedding
Relies on URL 2.1: see installation instructions.
This model is based on the canonical x-vector TDNN-based architecture, but with filter banks replaced with trainable SincNet features. See 'XVec... | [
"# Speaker embedding\n\nRelies on URL 2.1: see installation instructions.\n\nThis model is based on the canonical x-vector TDNN-based architecture, but with filter banks replaced with trainable SincNet features. See 'XVectorSincNet' architecture for implementation details.",
"## Basic usage\n\n\n\n\n\nUsing cosi... | [
"TAGS\n#pyannote-audio #pytorch #tensorboard #pyannote #pyannote-audio-model #audio #voice #speech #speaker #speaker-recognition #speaker-verification #speaker-identification #speaker-embedding #dataset-voxceleb #license-mit #has_space #region-us \n",
"# Speaker embedding\n\nRelies on URL 2.1: see installation i... |
automatic-speech-recognition | pyannote-audio |
# 🎹 Overlapped speech detection
Relies on pyannote.audio 2.1: see [installation instructions](https://github.com/pyannote/pyannote-audio#installation).
```python
# 1. visit hf.co/pyannote/segmentation and accept user conditions
# 2. visit hf.co/settings/tokens to create an access token
# 3. instantiate pretrained o... | {"license": "mit", "tags": ["pyannote", "pyannote-audio", "pyannote-audio-pipeline", "audio", "voice", "speech", "speaker", "overlapped-speech-detection", "automatic-speech-recognition"], "datasets": ["ami", "dihard", "voxconverse"], "extra_gated_prompt": "The collected information will help acquire a better knowledge ... | pyannote/overlapped-speech-detection | null | [
"pyannote-audio",
"pyannote",
"pyannote-audio-pipeline",
"audio",
"voice",
"speech",
"speaker",
"overlapped-speech-detection",
"automatic-speech-recognition",
"dataset:ami",
"dataset:dihard",
"dataset:voxconverse",
"license:mit",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#pyannote-audio #pyannote #pyannote-audio-pipeline #audio #voice #speech #speaker #overlapped-speech-detection #automatic-speech-recognition #dataset-ami #dataset-dihard #dataset-voxconverse #license-mit #has_space #region-us
|
# Overlapped speech detection
Relies on URL 2.1: see installation instructions.
## Support
For commercial enquiries and scientific consulting, please contact me.
For technical questions and bug reports, please check URL Github repository.
| [
"# Overlapped speech detection\n\nRelies on URL 2.1: see installation instructions.",
"## Support\n\nFor commercial enquiries and scientific consulting, please contact me. \nFor technical questions and bug reports, please check URL Github repository."
] | [
"TAGS\n#pyannote-audio #pyannote #pyannote-audio-pipeline #audio #voice #speech #speaker #overlapped-speech-detection #automatic-speech-recognition #dataset-ami #dataset-dihard #dataset-voxconverse #license-mit #has_space #region-us \n",
"# Overlapped speech detection\n\nRelies on URL 2.1: see installation instr... |
voice-activity-detection | pyannote-audio |
Using this open-source model in production?
Make the most of it thanks to our [consulting services](https://herve.niderb.fr/consulting.html).
# 🎹 Speaker segmentation
[Paper](http://arxiv.org/abs/2104.04045) | [Demo](https://huggingface.co/spaces/pyannote/pretrained-pipelines) | [Blog post](https://herve.niderb.... | {"license": "mit", "tags": ["pyannote", "pyannote-audio", "pyannote-audio-model", "audio", "voice", "speech", "speaker", "speaker-segmentation", "voice-activity-detection", "overlapped-speech-detection", "resegmentation"], "inference": false, "extra_gated_prompt": "The collected information will help acquire a better k... | pyannote/segmentation | null | [
"pyannote-audio",
"pytorch",
"pyannote",
"pyannote-audio-model",
"audio",
"voice",
"speech",
"speaker",
"speaker-segmentation",
"voice-activity-detection",
"overlapped-speech-detection",
"resegmentation",
"arxiv:2104.04045",
"license:mit",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2104.04045"
] | [] | TAGS
#pyannote-audio #pytorch #pyannote #pyannote-audio-model #audio #voice #speech #speaker #speaker-segmentation #voice-activity-detection #overlapped-speech-detection #resegmentation #arxiv-2104.04045 #license-mit #has_space #region-us
| Using this open-source model in production?
Make the most of it thanks to our consulting services.
Speaker segmentation
====================
Paper | Demo | Blog post
!Example
Usage
-----
Relies on URL 2.1.1: see installation instructions.
### Voice activity detection
### Overlapped speech detection
#... | [
"### Voice activity detection",
"### Overlapped speech detection",
"### Resegmentation",
"### Raw scores\n\n\nReproducible research\n---------------------\n\n\nIn order to reproduce the results of the paper \"End-to-end speaker segmentation for overlap-aware resegmentation\n\", use 'pyannote/segmentation@Inte... | [
"TAGS\n#pyannote-audio #pytorch #pyannote #pyannote-audio-model #audio #voice #speech #speaker #speaker-segmentation #voice-activity-detection #overlapped-speech-detection #resegmentation #arxiv-2104.04045 #license-mit #has_space #region-us \n",
"### Voice activity detection",
"### Overlapped speech detection",... |
automatic-speech-recognition | pyannote-audio |
Using this open-source pipeline in production?
Make the most of it thanks to our [consulting services](https://herve.niderb.fr/consulting.html).
# 🎹 Speaker diarization
Relies on pyannote.audio 2.1.1: see [installation instructions](https://github.com/pyannote/pyannote-audio#installation).
## TL;DR
```python
#... | {"license": "mit", "tags": ["pyannote", "pyannote-audio", "pyannote-audio-pipeline", "audio", "voice", "speech", "speaker", "speaker-diarization", "speaker-change-detection", "voice-activity-detection", "overlapped-speech-detection", "automatic-speech-recognition"], "datasets": ["ami", "dihard", "voxconverse", "aishell... | pyannote/speaker-diarization | null | [
"pyannote-audio",
"pyannote",
"pyannote-audio-pipeline",
"audio",
"voice",
"speech",
"speaker",
"speaker-diarization",
"speaker-change-detection",
"voice-activity-detection",
"overlapped-speech-detection",
"automatic-speech-recognition",
"dataset:ami",
"dataset:dihard",
"dataset:voxconve... | null | 2022-03-02T23:29:05+00:00 | [
"2012.01477",
"2110.07058",
"2005.08072"
] | [] | TAGS
#pyannote-audio #pyannote #pyannote-audio-pipeline #audio #voice #speech #speaker #speaker-diarization #speaker-change-detection #voice-activity-detection #overlapped-speech-detection #automatic-speech-recognition #dataset-ami #dataset-dihard #dataset-voxconverse #dataset-aishell #dataset-repere #dataset-voxceleb ... | Using this open-source pipeline in production?
Make the most of it thanks to our consulting services.
Speaker diarization
===================
Relies on URL 2.1.1: see installation instructions.
TL;DR
-----
Advanced usage
--------------
In case the number of speakers is known in advance, one can use the 'nu... | [
"### Real-time factor\n\n\nReal-time factor is around 2.5% using one Nvidia Tesla V100 SXM2 GPU (for the neural inference part) and one Intel Cascade Lake 6248 CPU (for the clustering part).\n\n\nIn other words, it takes approximately 1.5 minutes to process a one hour conversation.",
"### Accuracy\n\n\nThis pipel... | [
"TAGS\n#pyannote-audio #pyannote #pyannote-audio-pipeline #audio #voice #speech #speaker #speaker-diarization #speaker-change-detection #voice-activity-detection #overlapped-speech-detection #automatic-speech-recognition #dataset-ami #dataset-dihard #dataset-voxconverse #dataset-aishell #dataset-repere #dataset-vox... |
automatic-speech-recognition | pyannote-audio |
# 🎹 Speaker segmentation
Relies on pyannote.audio 2.1: see [installation instructions](https://github.com/pyannote/pyannote-audio#installation).
```python
# 1. visit hf.co/pyannote/segmentation and accept user conditions
# 2. visit hf.co/settings/tokens to create an access token
# 3. instantiate pretrained speaker ... | {"license": "mit", "tags": ["pyannote", "pyannote-audio", "pyannote-audio-pipeline", "audio", "voice", "speech", "speaker", "speaker-segmentation", "speaker-diarization", "speaker-change-detection", "voice-activity-detection", "overlapped-speech-detection", "automatic-speech-recognition"], "datasets": ["ami", "dihard",... | pyannote/speaker-segmentation | null | [
"pyannote-audio",
"pyannote",
"pyannote-audio-pipeline",
"audio",
"voice",
"speech",
"speaker",
"speaker-segmentation",
"speaker-diarization",
"speaker-change-detection",
"voice-activity-detection",
"overlapped-speech-detection",
"automatic-speech-recognition",
"dataset:ami",
"dataset:di... | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#pyannote-audio #pyannote #pyannote-audio-pipeline #audio #voice #speech #speaker #speaker-segmentation #speaker-diarization #speaker-change-detection #voice-activity-detection #overlapped-speech-detection #automatic-speech-recognition #dataset-ami #dataset-dihard #dataset-voxconverse #license-mit #has_space #regi... |
# Speaker segmentation
Relies on URL 2.1: see installation instructions.
️ This pipeline does not address speaker diarization.
## Support
For commercial enquiries and scientific consulting, please contact me.
For technical questions and bug reports, please check URL Github repository.
| [
"# Speaker segmentation\n\nRelies on URL 2.1: see installation instructions.\n\n\n\n️ This pipeline does not address speaker diarization.",
"## Support\n\nFor commercial enquiries and scientific consulting, please contact me. \nFor technical questions and bug reports, please check URL Github repository."
] | [
"TAGS\n#pyannote-audio #pyannote #pyannote-audio-pipeline #audio #voice #speech #speaker #speaker-segmentation #speaker-diarization #speaker-change-detection #voice-activity-detection #overlapped-speech-detection #automatic-speech-recognition #dataset-ami #dataset-dihard #dataset-voxconverse #license-mit #has_space... |
automatic-speech-recognition | pyannote-audio |
I propose (paid) scientific [consulting services](https://herve.niderb.fr/consulting.html) to companies willing to make the most of their data and open-source speech processing toolkits (and `pyannote` in particular).
# 🎹 Voice activity detection
Relies on pyannote.audio 2.1: see [installation instructions](https:... | {"license": "mit", "tags": ["pyannote", "pyannote-audio", "pyannote-audio-pipeline", "audio", "voice", "speech", "speaker", "voice-activity-detection", "automatic-speech-recognition"], "datasets": ["ami", "dihard", "voxconverse"], "extra_gated_prompt": "The collected information will help acquire a better knowledge of ... | pyannote/voice-activity-detection | null | [
"pyannote-audio",
"pyannote",
"pyannote-audio-pipeline",
"audio",
"voice",
"speech",
"speaker",
"voice-activity-detection",
"automatic-speech-recognition",
"dataset:ami",
"dataset:dihard",
"dataset:voxconverse",
"license:mit",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#pyannote-audio #pyannote #pyannote-audio-pipeline #audio #voice #speech #speaker #voice-activity-detection #automatic-speech-recognition #dataset-ami #dataset-dihard #dataset-voxconverse #license-mit #has_space #region-us
|
I propose (paid) scientific consulting services to companies willing to make the most of their data and open-source speech processing toolkits (and 'pyannote' in particular).
# Voice activity detection
Relies on URL 2.1: see installation instructions.
| [
"# Voice activity detection\n\nRelies on URL 2.1: see installation instructions."
] | [
"TAGS\n#pyannote-audio #pyannote #pyannote-audio-pipeline #audio #voice #speech #speaker #voice-activity-detection #automatic-speech-recognition #dataset-ami #dataset-dihard #dataset-voxconverse #license-mit #has_space #region-us \n",
"# Voice activity detection\n\nRelies on URL 2.1: see installation instruction... |
automatic-speech-recognition | espnet |
## ESPnet2 ASR model
### `pyf98/librispeech_100h_conformer`
This model was trained by Yifan Peng using librispeech_100 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 060fdb8b231b980c67b88a00fb8dd644aebbb1c0
pip install -e .
cd egs2/librispeech... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["librispeech_100"]} | pyf98/librispeech_100h_conformer | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"dataset:librispeech_100",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #automatic-speech-recognition #en #dataset-librispeech_100 #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ESPnet2 ASR model
-----------------
### 'pyf98/librispeech\_100h\_conformer'
This model was trained by Yifan Peng using librispeech\_100 recipe in espnet.
### Demo: How to use in ESPnet2
RESULTS
=======
Environments
------------
* date: 'Mon Feb 7 21:28:00 EST 2022'
* python version: '3.9.7 (default, Sep 16... | [
"### 'pyf98/librispeech\\_100h\\_conformer'\n\n\nThis model was trained by Yifan Peng using librispeech\\_100 recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Mon Feb 7 21:28:00 EST 2022'\n* python version: '3.9.7 (default, Sep 16 2021, 13:09... | [
"TAGS\n#espnet #audio #automatic-speech-recognition #en #dataset-librispeech_100 #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"### 'pyf98/librispeech\\_100h\\_conformer'\n\n\nThis model was trained by Yifan Peng using librispeech\\_100 recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=... |
automatic-speech-recognition | espnet |
## ESPnet2 ASR model
### `pyf98/librispeech_100h_transformer`
This model was trained by Yifan Peng using librispeech_100 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout f6779876103be2116de158a44757f8979eff0ab0
pip install -e .
cd egs2/librispee... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["librispeech_100"]} | pyf98/librispeech_100h_transformer | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"dataset:librispeech_100",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #automatic-speech-recognition #en #dataset-librispeech_100 #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ESPnet2 ASR model
-----------------
### 'pyf98/librispeech\_100h\_transformer'
This model was trained by Yifan Peng using librispeech\_100 recipe in espnet.
### Demo: How to use in ESPnet2
RESULTS
=======
Environments
------------
* date: 'Fri Feb 18 16:00:45 EST 2022'
* python version: '3.9.7 (default, Sep... | [
"### 'pyf98/librispeech\\_100h\\_transformer'\n\n\nThis model was trained by Yifan Peng using librispeech\\_100 recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Fri Feb 18 16:00:45 EST 2022'\n* python version: '3.9.7 (default, Sep 16 2021, 13... | [
"TAGS\n#espnet #audio #automatic-speech-recognition #en #dataset-librispeech_100 #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"### 'pyf98/librispeech\\_100h\\_transformer'\n\n\nThis model was trained by Yifan Peng using librispeech\\_100 recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\... |
automatic-speech-recognition | espnet |
## ESPnet2 ASR model
### `pyf98/speechcommands_12commands_conformer`
This model was trained by Yifan Peng using speechcommands recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout bf523b70cae8300da004b41ec6a0d1b57c7ae8bb
pip install -e .
cd egs2/spe... | {"language": "noinfo", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["speechcommands"]} | pyf98/speechcommands_12commands_conformer | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"dataset:speechcommands",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"noinfo"
] | TAGS
#espnet #audio #automatic-speech-recognition #dataset-speechcommands #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ESPnet2 ASR model
-----------------
### 'pyf98/speechcommands\_12commands\_conformer'
This model was trained by Yifan Peng using speechcommands recipe in espnet.
### Demo: How to use in ESPnet2
RESULTS
=======
Environments
------------
* date: 'Fri Dec 24 21:53:37 EST 2021'
* python version: '3.9.7 (default... | [
"### 'pyf98/speechcommands\\_12commands\\_conformer'\n\n\nThis model was trained by Yifan Peng using speechcommands recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Fri Dec 24 21:53:37 EST 2021'\n* python version: '3.9.7 (default, Sep 16 2021... | [
"TAGS\n#espnet #audio #automatic-speech-recognition #dataset-speechcommands #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"### 'pyf98/speechcommands\\_12commands\\_conformer'\n\n\nThis model was trained by Yifan Peng using speechcommands recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n... |
automatic-speech-recognition | espnet |
## ESPnet2 ASR model
### `pyf98/speechcommands_35commands_conformer`
This model was trained by Yifan Peng using speechcommands recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout bf523b70cae8300da004b41ec6a0d1b57c7ae8bb
pip install -e .
cd egs2/spe... | {"language": "noinfo", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["speechcommands"]} | pyf98/speechcommands_35commands_conformer | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"dataset:speechcommands",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"noinfo"
] | TAGS
#espnet #audio #automatic-speech-recognition #dataset-speechcommands #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ESPnet2 ASR model
-----------------
### 'pyf98/speechcommands\_35commands\_conformer'
This model was trained by Yifan Peng using speechcommands recipe in espnet.
### Demo: How to use in ESPnet2
RESULTS
=======
Environments
------------
* date: 'Tue Dec 28 20:39:29 EST 2021'
* python version: '3.9.7 (default... | [
"### 'pyf98/speechcommands\\_35commands\\_conformer'\n\n\nThis model was trained by Yifan Peng using speechcommands recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Tue Dec 28 20:39:29 EST 2021'\n* python version: '3.9.7 (default, Sep 16 2021... | [
"TAGS\n#espnet #audio #automatic-speech-recognition #dataset-speechcommands #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"### 'pyf98/speechcommands\\_35commands\\_conformer'\n\n\nThis model was trained by Yifan Peng using speechcommands recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n... |
null | pysentimiento | # Hate Speech detection in English
## bertweet-hate-speech
Repository: [https://github.com/pysentimiento/pysentimiento/](https://github.com/finiteautomata/pysentimiento/)
Model trained with SemEval 2019 Task 5: HatEval (SubTask B) corpus for Hate Speech detection in English. Base model is [BERTweet](https://hugging... | {"language": ["en"], "library_name": "pysentimiento", "tags": ["twitter", "hate-speech"]} | pysentimiento/bertweet-hate-speech | null | [
"pysentimiento",
"pytorch",
"roberta",
"twitter",
"hate-speech",
"en",
"arxiv:2106.09462",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.09462"
] | [
"en"
] | TAGS
#pysentimiento #pytorch #roberta #twitter #hate-speech #en #arxiv-2106.09462 #region-us
| # Hate Speech detection in English
## bertweet-hate-speech
Repository: URL
Model trained with SemEval 2019 Task 5: HatEval (SubTask B) corpus for Hate Speech detection in English. Base model is BERTweet, a RoBERTa model trained in English tweets.
It is a multi-classifier model, with the following classes:
- HS: i... | [
"# Hate Speech detection in English",
"## bertweet-hate-speech\n\nRepository: URL\n\n\n\nModel trained with SemEval 2019 Task 5: HatEval (SubTask B) corpus for Hate Speech detection in English. Base model is BERTweet, a RoBERTa model trained in English tweets.\n\nIt is a multi-classifier model, with the following... | [
"TAGS\n#pysentimiento #pytorch #roberta #twitter #hate-speech #en #arxiv-2106.09462 #region-us \n",
"# Hate Speech detection in English",
"## bertweet-hate-speech\n\nRepository: URL\n\n\n\nModel trained with SemEval 2019 Task 5: HatEval (SubTask B) corpus for Hate Speech detection in English. Base model is BERT... |
null | pysentimiento |
# robertuito-base-cased
# RoBERTuito
## A pre-trained language model for social media text in Spanish
[**READ THE FULL PAPER**](https://arxiv.org/abs/2111.09453)
[Github Repository](https://github.com/pysentimiento/robertuito)
[](https://co... | {"language": ["es"], "library_name": "pysentimiento", "tags": ["twitter", "RoBERTa"]} | pysentimiento/robertuito-base-cased | null | [
"pysentimiento",
"pytorch",
"roberta",
"twitter",
"RoBERTa",
"es",
"arxiv:2111.09453",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2111.09453"
] | [
"es"
] | TAGS
#pysentimiento #pytorch #roberta #twitter #RoBERTa #es #arxiv-2111.09453 #region-us
| robertuito-base-cased
=====================
RoBERTuito
==========
A pre-trained language model for social media text in Spanish
-------------------------------------------------------------
READ THE FULL PAPER
Github Repository
![Test it in Colab](URL
*RoBERTuito* is a pre-trained language model for user-genera... | [] | [
"TAGS\n#pysentimiento #pytorch #roberta #twitter #RoBERTa #es #arxiv-2111.09453 #region-us \n"
] |
fill-mask | transformers | # robertuito-base-deacc
# RoBERTuito
## A pre-trained language model for social media text in Spanish
[**READ THE FULL PAPER**](https://arxiv.org/abs/2111.09453)
[Github Repository](https://github.com/pysentimiento/robertuito)
[](https://co... | {} | pysentimiento/robertuito-base-deacc | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"arxiv:2111.09453",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2111.09453"
] | [] | TAGS
#transformers #pytorch #roberta #fill-mask #arxiv-2111.09453 #autotrain_compatible #endpoints_compatible #region-us
| robertuito-base-deacc
=====================
RoBERTuito
==========
A pre-trained language model for social media text in Spanish
-------------------------------------------------------------
READ THE FULL PAPER
Github Repository
![Test it in Colab](URL
*RoBERTuito* is a pre-trained language model for user-genera... | [] | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #arxiv-2111.09453 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers |
# robertuito-base-uncased
# RoBERTuito
## A pre-trained language model for social media text in Spanish
[**PAPER**](https://arxiv.org/abs/2111.09453)
[Github Repository](https://github.com/pysentimiento/robertuito)
[](https://colab.researc... | {"language": ["es"], "tags": ["twitter", "masked-lm"]} | pysentimiento/robertuito-base-uncased | null | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"fill-mask",
"twitter",
"masked-lm",
"es",
"arxiv:2111.09453",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2111.09453"
] | [
"es"
] | TAGS
#transformers #pytorch #safetensors #roberta #fill-mask #twitter #masked-lm #es #arxiv-2111.09453 #autotrain_compatible #endpoints_compatible #region-us
| robertuito-base-uncased
=======================
RoBERTuito
==========
A pre-trained language model for social media text in Spanish
-------------------------------------------------------------
PAPER
Github Repository
![Test it in Colab](URL
*RoBERTuito* is a pre-trained language model for user-generated conten... | [] | [
"TAGS\n#transformers #pytorch #safetensors #roberta #fill-mask #twitter #masked-lm #es #arxiv-2111.09453 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
null | pysentimiento |
# Emotion Analysis in Spanish
## robertuito-emotion-analysis
Repository: [https://github.com/pysentimiento/pysentimiento/](https://github.com/finiteautomata/pysentimiento/)
Model trained with TASS 2020 Task 2 corpus for Emotion detection in Spanish. Base model is [RoBERTuito](https://github.com/pysentimiento/rober... | {"language": ["es"], "library_name": "pysentimiento", "tags": ["emotion-analysis", "twitter"]} | pysentimiento/robertuito-emotion-analysis | null | [
"pysentimiento",
"pytorch",
"roberta",
"emotion-analysis",
"twitter",
"es",
"arxiv:2106.09462",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.09462"
] | [
"es"
] | TAGS
#pysentimiento #pytorch #roberta #emotion-analysis #twitter #es #arxiv-2106.09462 #has_space #region-us
| Emotion Analysis in Spanish
===========================
robertuito-emotion-analysis
---------------------------
Repository: URL
Model trained with TASS 2020 Task 2 corpus for Emotion detection in Spanish. Base model is RoBERTuito, a RoBERTa model trained in Spanish tweets.
Contains the six Ekman emotions plus a... | [] | [
"TAGS\n#pysentimiento #pytorch #roberta #emotion-analysis #twitter #es #arxiv-2106.09462 #has_space #region-us \n"
] |
null | pysentimiento | # Hate Speech detection in Spanish
## robertuito-hate-speech
Repository: [https://github.com/pysentimiento/pysentimiento/](https://github.com/finiteautomata/pysentimiento/)
Model trained with SemEval 2019 Task 5: HatEval (SubTask B) corpus for Hate Speech detection in Spanish. Base model is [RoBERTuito](https://git... | {"language": ["es"], "library_name": "pysentimiento", "tags": ["twitter", "hate-speech"]} | pysentimiento/robertuito-hate-speech | null | [
"pysentimiento",
"pytorch",
"roberta",
"twitter",
"hate-speech",
"es",
"arxiv:2106.09462",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.09462"
] | [
"es"
] | TAGS
#pysentimiento #pytorch #roberta #twitter #hate-speech #es #arxiv-2106.09462 #has_space #region-us
| Hate Speech detection in Spanish
================================
robertuito-hate-speech
----------------------
Repository: URL
Model trained with SemEval 2019 Task 5: HatEval (SubTask B) corpus for Hate Speech detection in Spanish. Base model is RoBERTuito, a RoBERTa model trained in Spanish tweets.
It is a mu... | [] | [
"TAGS\n#pysentimiento #pytorch #roberta #twitter #hate-speech #es #arxiv-2106.09462 #has_space #region-us \n"
] |
null | pysentimiento |
# Irony detection in Spanish
## robertuito-irony
Repository: [https://github.com/pysentimiento/pysentimiento/](https://github.com/finiteautomata/pysentimiento/)
Model trained with IRosVA 2019 dataset for irony detection. Base model is [RoBERTuito](https://github.com/pysentimiento/robertuito), a RoBERTa model trai... | {"language": ["es"], "library_name": "pysentimiento", "tags": ["twitter", "irony"]} | pysentimiento/robertuito-irony | null | [
"pysentimiento",
"pytorch",
"roberta",
"twitter",
"irony",
"es",
"arxiv:2106.09462",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.09462"
] | [
"es"
] | TAGS
#pysentimiento #pytorch #roberta #twitter #irony #es #arxiv-2106.09462 #region-us
| Irony detection in Spanish
==========================
robertuito-irony
----------------
Repository: URL
Model trained with IRosVA 2019 dataset for irony detection. Base model is RoBERTuito, a RoBERTa model trained in Spanish tweets.
The positive class marks irony, the negative class marks not irony.
Results
-... | [] | [
"TAGS\n#pysentimiento #pytorch #roberta #twitter #irony #es #arxiv-2106.09462 #region-us \n"
] |
null | pysentimiento | # Sentiment Analysis in Spanish
## robertuito-sentiment-analysis
Repository: [https://github.com/pysentimiento/pysentimiento/](https://github.com/finiteautomata/pysentimiento/)
Model trained with TASS 2020 corpus (around ~5k tweets) of several dialects of Spanish. Base model is [RoBERTuito](https://github.com/pysent... | {"language": ["es"], "library_name": "pysentimiento", "tags": ["twitter", "sentiment-analysis"]} | pysentimiento/robertuito-sentiment-analysis | null | [
"pysentimiento",
"pytorch",
"tf",
"roberta",
"twitter",
"sentiment-analysis",
"es",
"arxiv:2106.09462",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.09462"
] | [
"es"
] | TAGS
#pysentimiento #pytorch #tf #roberta #twitter #sentiment-analysis #es #arxiv-2106.09462 #has_space #region-us
| Sentiment Analysis in Spanish
=============================
robertuito-sentiment-analysis
-----------------------------
Repository: URL
Model trained with TASS 2020 corpus (around ~5k tweets) of several dialects of Spanish. Base model is RoBERTuito, a RoBERTa model trained in Spanish tweets.
Uses 'POS', 'NEG', ... | [] | [
"TAGS\n#pysentimiento #pytorch #tf #roberta #twitter #sentiment-analysis #es #arxiv-2106.09462 #has_space #region-us \n"
] |
token-classification | flair |
# POET: A French Extended Part-of-Speech Tagger
- Corpora: [ANTILLES](https://github.com/qanastek/ANTILLES)
- Embeddings: [Flair](https://aclanthology.org/C18-1139.pdf) & [CamemBERT](https://arxiv.org/abs/1911.03894)
- Sequence Labelling: [Bi-LSTM-CRF](https://arxiv.org/abs/1011.4088)
- Number of Epochs: 50
**People... | {"language": "fr", "tags": ["flair", "token-classification", "sequence-tagger-model"], "datasets": ["qanastek/ANTILLES"], "widget": [{"text": "George Washington est all\u00e9 \u00e0 Washington"}]} | qanastek/pos-french-camembert-flair | null | [
"flair",
"pytorch",
"token-classification",
"sequence-tagger-model",
"fr",
"dataset:qanastek/ANTILLES",
"arxiv:1911.03894",
"arxiv:1011.4088",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1911.03894",
"1011.4088"
] | [
"fr"
] | TAGS
#flair #pytorch #token-classification #sequence-tagger-model #fr #dataset-qanastek/ANTILLES #arxiv-1911.03894 #arxiv-1011.4088 #has_space #region-us
| POET: A French Extended Part-of-Speech Tagger
=============================================
* Corpora: ANTILLES
* Embeddings: Flair & CamemBERT
* Sequence Labelling: Bi-LSTM-CRF
* Number of Epochs: 50
People Involved
* LABRAK Yanis (1)
* DUFOUR Richard (2)
Affiliations
1. LIA, NLP team, Avignon University, Av... | [] | [
"TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #fr #dataset-qanastek/ANTILLES #arxiv-1911.03894 #arxiv-1011.4088 #has_space #region-us \n"
] |
token-classification | transformers |
# POET: A French Extended Part-of-Speech Tagger
- Corpora: [ANTILLES](https://github.com/qanastek/ANTILLES)
- Embeddings & Sequence Labelling: [CamemBERT](https://arxiv.org/abs/1911.03894)
- Number of Epochs: 115
**People Involved**
* [LABRAK Yanis](https://www.linkedin.com/in/yanis-labrak-8a7412145/) (1)
* [DUFOUR... | {"language": "fr", "tags": ["Transformers", "token-classification", "sequence-tagger-model"], "datasets": ["qanastek/ANTILLES"], "widget": [{"text": "George Washington est all\u00e9 \u00e0 Washington"}]} | qanastek/pos-french-camembert | null | [
"transformers",
"pytorch",
"camembert",
"token-classification",
"Transformers",
"sequence-tagger-model",
"fr",
"dataset:qanastek/ANTILLES",
"arxiv:1911.03894",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1911.03894"
] | [
"fr"
] | TAGS
#transformers #pytorch #camembert #token-classification #Transformers #sequence-tagger-model #fr #dataset-qanastek/ANTILLES #arxiv-1911.03894 #autotrain_compatible #endpoints_compatible #region-us
| POET: A French Extended Part-of-Speech Tagger
=============================================
* Corpora: ANTILLES
* Embeddings & Sequence Labelling: CamemBERT
* Number of Epochs: 115
People Involved
* LABRAK Yanis (1)
* DUFOUR Richard (2)
Affiliations
1. LIA, NLP team, Avignon University, Avignon, France.
2. LS... | [] | [
"TAGS\n#transformers #pytorch #camembert #token-classification #Transformers #sequence-tagger-model #fr #dataset-qanastek/ANTILLES #arxiv-1911.03894 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
token-classification | flair |
# POET: A French Extended Part-of-Speech Tagger
- Corpora: [ANTILLES](https://github.com/qanastek/ANTILLES)
- Embeddings: [FastText](https://fasttext.cc/)
- Sequence Labelling: [Bi-LSTM-CRF](https://arxiv.org/abs/1011.4088)
- Number of Epochs: 115
**People Involved**
* [LABRAK Yanis](https://www.linkedin.com/in/yan... | {"language": "fr", "tags": ["flair", "token-classification", "sequence-tagger-model"], "datasets": ["qanastek/ANTILLES"], "widget": [{"text": "George Washington est all\u00e9 \u00e0 Washington"}]} | qanastek/pos-french | null | [
"flair",
"pytorch",
"token-classification",
"sequence-tagger-model",
"fr",
"dataset:qanastek/ANTILLES",
"arxiv:1011.4088",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1011.4088"
] | [
"fr"
] | TAGS
#flair #pytorch #token-classification #sequence-tagger-model #fr #dataset-qanastek/ANTILLES #arxiv-1011.4088 #has_space #region-us
| POET: A French Extended Part-of-Speech Tagger
=============================================
* Corpora: ANTILLES
* Embeddings: FastText
* Sequence Labelling: Bi-LSTM-CRF
* Number of Epochs: 115
People Involved
* LABRAK Yanis (1)
* DUFOUR Richard (2)
Affiliations
1. LIA, NLP team, Avignon University, Avignon, F... | [] | [
"TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #fr #dataset-qanastek/ANTILLES #arxiv-1011.4088 #has_space #region-us \n"
] |
fill-mask | transformers | # QARiB: QCRI Arabic and Dialectal BERT
## About QARiB
QCRI Arabic and Dialectal BERT (QARiB) model, was trained on a collection of ~ 420 Million tweets and ~ 180 Million sentences of text.
For the tweets, the data was collected using twitter API and using language filter. `lang:ar`. For the text data, it was a combi... | {"language": "ar", "tags": ["pytorch", "tf", "QARiB", "qarib"], "datasets": ["arabic_billion_words", "open_subtitles", "twitter"], "metrics": ["f1"], "widget": [{"text": " \u0634\u0648 \u0639\u0646\u062f\u0643\u0645 \u064a\u0627 [MASK] ."}]} | qarib/bert-base-qarib | null | [
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| QARiB: QCRI Arabic and Dialectal BERT
=====================================
About QARiB
-----------
QCRI Arabic and Dialectal BERT (QARiB) model, was trained on a collection of ~ 420 Million tweets and ~ 180 Million sentences of text.
For the tweets, the data was collected using twitter API and using language filte... | [
"### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\n\n\nEvaluations:\n------------\n\n\n\nModel Weights and Vocab Download\n--------------------------------\n\n\nFrom Huggingface site: URL\n\n\nContacts\n--------\n\n\nAhmed Abdelali, Sabit Hassan, Hamdy Mubarak, Karee... | [
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"### How to use\n\n\nYou can use this model directly with a pipeline for masked language mo... |
fill-mask | transformers |
# QARiB: QCRI Arabic and Dialectal BERT
## About QARiB
QCRI Arabic and Dialectal BERT (QARiB) model, was trained on a collection of ~ 420 Million tweets and ~ 180 Million sentences of text.
For Tweets, the data was collected using twitter API and using language filter. `lang:ar`. For Text data, it was a combination ... | {"language": "ar", "tags": ["pytorch", "tf", "qarib", "qarib60_1790k"], "datasets": ["arabic_billion_words", "open_subtitles", "twitter"], "metrics": ["f1"], "widget": [{"text": " \u0634\u0648 \u0639\u0646\u062f\u0643\u0645 \u064a\u0627 [MASK] ."}]} | qarib/bert-base-qarib60_1790k | null | [
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|
# QARiB: QCRI Arabic and Dialectal BERT
## About QARiB
QCRI Arabic and Dialectal BERT (QARiB) model, was trained on a collection of ~ 420 Million tweets and ~ 180 Million sentences of text.
For Tweets, the data was collected using twitter API and using language filter. 'lang:ar'. For Text data, it was a combination ... | [
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"# QARiB: QCRI Arabic and Dialectal BERT",
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fill-mask | transformers |
# QARiB: QCRI Arabic and Dialectal BERT
## About QARiB
QCRI Arabic and Dialectal BERT (QARiB) model, was trained on a collection of ~ 420 Million tweets and ~ 180 Million sentences of text.
For Tweets, the data was collected using twitter API and using language filter. `lang:ar`. For Text data, it was a combination ... | {"language": "ar", "tags": ["pytorch", "tf", "qarib", "qarib60_1790k"], "datasets": ["arabic_billion_words", "open_subtitles", "twitter"], "metrics": ["f1"], "widget": [{"text": " \u0634\u0648 \u0639\u0646\u062f\u0643\u0645 \u064a\u0627 [MASK] ."}]} | qarib/bert-base-qarib60_1970k | null | [
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|
# QARiB: QCRI Arabic and Dialectal BERT
## About QARiB
QCRI Arabic and Dialectal BERT (QARiB) model, was trained on a collection of ~ 420 Million tweets and ~ 180 Million sentences of text.
For Tweets, the data was collected using twitter API and using language filter. 'lang:ar'. For Text data, it was a combination ... | [
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fill-mask | transformers |
# QARiB: QCRI Arabic and Dialectal BERT
## About QARiB
QCRI Arabic and Dialectal BERT (QARiB) model, was trained on a collection of ~ 420 Million tweets and ~ 180 Million sentences of text.
For tweets, the data was collected using twitter API and using language filter. `lang:ar`. For text data, it was a combination ... | {"language": "ar", "tags": ["pytorch", "tf", "bert-base-qarib60_860k", "qarib"], "datasets": ["arabic_billion_words", "open_subtitles", "twitter"], "metrics": ["f1"], "widget": [{"text": " \u0634\u0648 \u0639\u0646\u062f\u0643\u0645 \u064a\u0627 [MASK] ."}]} | qarib/bert-base-qarib60_860k | null | [
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|
# QARiB: QCRI Arabic and Dialectal BERT
## About QARiB
QCRI Arabic and Dialectal BERT (QARiB) model, was trained on a collection of ~ 420 Million tweets and ~ 180 Million sentences of text.
For tweets, the data was collected using twitter API and using language filter. 'lang:ar'. For text data, it was a combination ... | [
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"## About QARiB\nQCRI Arabic and Dialectal BERT (QARiB) model, was trained on a collection of ~ 420 Million tweets and ~ 180 Million sentences of text.\nFor tweets, the data was collected using twitter API and using language filter. 'lang:ar'. For text data, it was a co... | [
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"# QARiB: QCRI Arabic and Dialectal BERT",
"## About QARiB\nQCRI Arabic ... |
null | transformers | # QARiB: QCRI Arabic and Dialectal BERT
## About QARiB Farasa
QCRI Arabic and Dialectal BERT (QARiB) model, was trained on a collection of ~ 420 Million tweets and ~ 180 Million sentences of text.
For the tweets, the data was collected using twitter API and using language filter. `lang:ar`. For the text data, it was a... | {"language": "ar", "tags": ["pytorch", "tf", "QARiB", "qarib"], "datasets": ["arabic_billion_words", "open_subtitles", "twitter", "Farasa"], "metrics": ["f1"], "widget": [{"text": "\u0648+\u0642\u0627\u0645 \u0627\u0644+\u0645\u062f\u064a\u0631 [MASK]"}]} | qarib/bert-base-qarib_far | null | [
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"dataset:Farasa",
"arxiv:2102.10684",
"endpoints_compatible",
"region:us"
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"2102.10684"
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| # QARiB: QCRI Arabic and Dialectal BERT
## About QARiB Farasa
QCRI Arabic and Dialectal BERT (QARiB) model, was trained on a collection of ~ 420 Million tweets and ~ 180 Million sentences of text.
For the tweets, the data was collected using twitter API and using language filter. 'lang:ar'. For the text data, it was a... | [
"# QARiB: QCRI Arabic and Dialectal BERT",
"## About QARiB Farasa\nQCRI Arabic and Dialectal BERT (QARiB) model, was trained on a collection of ~ 420 Million tweets and ~ 180 Million sentences of text.\nFor the tweets, the data was collected using twitter API and using language filter. 'lang:ar'. For the text da... | [
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"# QARiB: QCRI Arabic and Dialectal BERT",
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null | transformers | # QARiB: QCRI Arabic and Dialectal BERT
## About QARiB Farasa
QCRI Arabic and Dialectal BERT (QARiB) model, was trained on a collection of ~ 420 Million tweets and ~ 180 Million sentences of text.
For the tweets, the data was collected using twitter API and using language filter. `lang:ar`. For the text data, it was a... | {"language": "ar", "tags": ["pytorch", "tf", "QARiB", "qarib"], "datasets": ["arabic_billion_words", "open_subtitles", "twitter", "Farasa"], "metrics": ["f1"], "widget": [{"text": "\u0648+\u0642\u0627\u0645 \u0627\u0644+\u0645\u062f\u064a\u0631 [MASK]"}]} | qarib/bert-base-qarib_far_6500k | null | [
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"arxiv:2102.10684",
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| QARiB: QCRI Arabic and Dialectal BERT
=====================================
About QARiB Farasa
------------------
QCRI Arabic and Dialectal BERT (QARiB) model, was trained on a collection of ~ 420 Million tweets and ~ 180 Million sentences of text.
For the tweets, the data was collected using twitter API and using ... | [
"### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\n\n\nEvaluations:\n------------\n\n\n\nModel Weights and Vocab Download\n--------------------------------\n\n\nFrom Huggingface site: URL\n\n\nContacts\n--------\n\n\nAhmed Abdelali, Sabit Hassan, Hamdy Mubarak, Karee... | [
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"### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\n\n\nEvaluations:\n-... |
null | transformers | # QARiB: QCRI Arabic and Dialectal BERT
## About QARiB Farasa
QCRI Arabic and Dialectal BERT (QARiB) model, was trained on a collection of ~ 420 Million tweets and ~ 180 Million sentences of text.
For the tweets, the data was collected using twitter API and using language filter. `lang:ar`. For the text data, it was a... | {"language": "ar", "tags": ["pytorch", "tf", "QARiB", "qarib"], "datasets": ["arabic_billion_words", "open_subtitles", "twitter", "Farasa"], "metrics": ["f1"], "widget": [{"text": "\u0648+\u0642\u0627\u0645 \u0627\u0644+\u0645\u062f\u064a\u0631 [MASK]"}]} | qarib/bert-base-qarib_far_8280k | null | [
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"region:us"
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"2102.10684"
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| QARiB: QCRI Arabic and Dialectal BERT
=====================================
About QARiB Farasa
------------------
QCRI Arabic and Dialectal BERT (QARiB) model, was trained on a collection of ~ 420 Million tweets and ~ 180 Million sentences of text.
For the tweets, the data was collected using twitter API and using ... | [
"### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\n\n\nEvaluations:\n------------\n\n\n\nModel Weights and Vocab Download\n--------------------------------\n\n\nFrom Huggingface site: URL\n\n\nContacts\n--------\n\n\nAhmed Abdelali, Sabit Hassan, Hamdy Mubarak, Karee... | [
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"### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\n\n\nEvaluations:\n-... |
null | transformers | # QARiB: QCRI Arabic and Dialectal BERT
## About QARiB Farasa
QCRI Arabic and Dialectal BERT (QARiB) model, was trained on a collection of ~ 420 Million tweets and ~ 180 Million sentences of text.
For the tweets, the data was collected using twitter API and using language filter. `lang:ar`. For the text data, it was a... | {"language": "ar", "tags": ["pytorch", "tf", "QARiB", "qarib"], "datasets": ["arabic_billion_words", "open_subtitles", "twitter", "Farasa"], "metrics": ["f1"], "widget": [{"text": "\u0648+\u0642\u0627\u0645 \u0627\u0644+\u0645\u062f\u064a\u0631 [MASK]"}]} | qarib/bert-base-qarib_far_9920k | null | [
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"arxiv:2102.10684",
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"2102.10684"
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| QARiB: QCRI Arabic and Dialectal BERT
=====================================
About QARiB Farasa
------------------
QCRI Arabic and Dialectal BERT (QARiB) model, was trained on a collection of ~ 420 Million tweets and ~ 180 Million sentences of text.
For the tweets, the data was collected using twitter API and using ... | [
"### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\n\n\nEvaluations:\n------------\n\n\n\nModel Weights and Vocab Download\n--------------------------------\n\n\nFrom Huggingface site: URL\n\n\nContacts\n--------\n\n\nAhmed Abdelali, Sabit Hassan, Hamdy Mubarak, Karee... | [
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"### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\n\n\nEvaluations:\n-... |
null | null | # Image embedding | {} | qfortier/image-retrieval-ny | null | [
"tensorboard",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#tensorboard #region-us
| # Image embedding | [
"# Image embedding"
] | [
"TAGS\n#tensorboard #region-us \n",
"# Image embedding"
] |
fill-mask | transformers |
## Word based BERT model
原模型及说明见：https://github.com/ZhuiyiTechnology/WoBERT
pytorch 模型见: https://github.com/JunnYu/WoBERT_pytorch
## 安装 WoBertTokenizer
```bash
pip install git+https://github.com/JunnYu/WoBERT_pytorch.git
```
## TF Example
```python
from transformers import TFBertForMaskedLM as WoBertForMaskedLM
f... | {"language": "zh", "tags": ["wobert"], "inference": true} | qinluo/wobert-chinese-plus | null | [
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"bert",
"fill-mask",
"wobert",
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"autotrain_compatible",
"endpoints_compatible",
"region:us"
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"zh"
] | TAGS
#transformers #pytorch #tf #bert #fill-mask #wobert #zh #autotrain_compatible #endpoints_compatible #region-us
|
## Word based BERT model
原模型及说明见：URL
pytorch 模型见: URL
## 安装 WoBertTokenizer
## TF Example
## Pytorch Example
## 引用
Bibtex：
| [
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"## 安装 WoBertTokenizer",
"## TF Example",
"## Pytorch Example",
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] |
null | null | # Word2vec
测试文件大小、上传、下载速度 | {} | qlh/word2vec | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| # Word2vec
测试文件大小、上传、下载速度 | [
"# Word2vec\n\n测试文件大小、上传、下载速度"
] | [
"TAGS\n#region-us \n",
"# Word2vec\n\n测试文件大小、上传、下载速度"
] |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Japanese
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Japanese using the [Common Voice](https://huggingface.co/datasets/common_voice), and JSUT dataset{s}.
When using this model, make sure that your speech input is sampled at 16kHz.
... | {"language": "ja", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice", "jsut"], "metrics": ["wer", "cer"], "model-index": [{"name": "Japanese XLSR Wav2Vec2 Large 53", "results": [{"task": {"type": "automatic-speech-recognition", "na... | qqpann/w2v_hf_jsut_xlsr53 | null | [
"transformers",
"pytorch",
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"automatic-speech-recognition",
"audio",
"speech",
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"dataset:common_voice",
"dataset:jsut",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #ja #dataset-common_voice #dataset-jsut #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-Japanese
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Japanese using the Common Voice, and JSUT dataset{s}.
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 mode... | [
"# Wav2Vec2-Large-XLSR-53-Japanese\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Japanese using the Common Voice, and JSUT dataset{s}.\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:",
"## Evalu... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #ja #dataset-common_voice #dataset-jsut #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Japanese\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Japanese us... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-{language} #TODO: replace language with your {language}, _e.g._ French
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on {language} using the [Common Voice](https://huggingface.co/datasets/common_voice), ... and ... dataset{s}. #TODO: repl... | {"language": "ja", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer", "cer"], "model-index": [{"name": "Japanese XLSR Wav2Vec2 Large 53", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Sp... | qqpann/wav2vec2-large-xlsr-japanese-0325-1200 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"ja",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #ja #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-{language} #TODO: replace language with your {language}, _e.g._ French
Fine-tuned facebook/wav2vec2-large-xlsr-53 on {language} using the Common Voice, ... and ... dataset{s}. #TODO: replace {language} with your language, _e.g._ French and eventually add more datasets that were used and event... | [
"# Wav2Vec2-Large-XLSR-53-{language} #TODO: replace language with your {language}, _e.g._ French\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on {language} using the Common Voice, ... and ... dataset{s}. #TODO: replace {language} with your language, _e.g._ French and eventually add more datasets that were used and... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #ja #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-{language} #TODO: replace language with your {language}, _e.g._ French\n\nFine-tune... |
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. -->
# layoutlmv2_e
This model is a fine-tuned version of [microsoft/layoutlmv2-base-uncased](https://huggingface.co/microsoft/layoutlm... | {"license": "cc-by-sa-4.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "layoutlmv2_e", "results": []}]} | quangtran199hust/layoutlmv2_e | null | [
"transformers",
"pytorch",
"tensorboard",
"layoutlmv2",
"token-classification",
"generated_from_trainer",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #layoutlmv2 #token-classification #generated_from_trainer #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# layoutlmv2_e
This model is a fine-tuned version of microsoft/layoutlmv2-base-uncased on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperpa... | [
"# layoutlmv2_e\n\nThis model is a fine-tuned version of microsoft/layoutlmv2-base-uncased on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training proce... | [
"TAGS\n#transformers #pytorch #tensorboard #layoutlmv2 #token-classification #generated_from_trainer #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# layoutlmv2_e\n\nThis model is a fine-tuned version of microsoft/layoutlmv2-base-uncased on an unknown dataset.",
"## Model des... |
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. -->
# layoutlmv2_roige
This model is a fine-tuned version of [microsoft/layoutlmv2-base-uncased](https://huggingface.co/microsoft/layo... | {"license": "cc-by-sa-4.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "layoutlmv2_roige", "results": []}]} | quangtran199hust/layoutlmv2_roige | null | [
"transformers",
"pytorch",
"tensorboard",
"layoutlmv2",
"token-classification",
"generated_from_trainer",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #layoutlmv2 #token-classification #generated_from_trainer #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# layoutlmv2_roige
This model is a fine-tuned version of microsoft/layoutlmv2-base-uncased on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyp... | [
"# layoutlmv2_roige\n\nThis model is a fine-tuned version of microsoft/layoutlmv2-base-uncased on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training p... | [
"TAGS\n#transformers #pytorch #tensorboard #layoutlmv2 #token-classification #generated_from_trainer #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# layoutlmv2_roige\n\nThis model is a fine-tuned version of microsoft/layoutlmv2-base-uncased on an unknown dataset.",
"## Model... |
text-classification | transformers | labeled by "YES" : 1, "NO" : 0, "No Answer" : 2
fine tuned by klue/roberta-large | {} | quarter100/ko-boolq-model | 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
| labeled by "YES" : 1, "NO" : 0, "No Answer" : 2
fine tuned by klue/roberta-large | [] | [
"TAGS\n#transformers #pytorch #roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
null | null | afad | {} | qunwang6/test | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| afad | [] | [
"TAGS\n#region-us \n"
] |
fill-mask | transformers |
# FrALBERT Base
Pretrained model on French language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1909.11942) and first released in
[this repository](https://github.com/google-research/albert). This model, as all ALBERT models, is uncased: it does not make a... | {"language": "fr", "license": "apache-2.0", "datasets": ["wikipedia"]} | qwant/fralbert-base | null | [
"transformers",
"pytorch",
"safetensors",
"albert",
"fill-mask",
"fr",
"dataset:wikipedia",
"arxiv:1909.11942",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1909.11942"
] | [
"fr"
] | TAGS
#transformers #pytorch #safetensors #albert #fill-mask #fr #dataset-wikipedia #arxiv-1909.11942 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| FrALBERT Base
=============
Pretrained model on French language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This model, as all ALBERT models, is uncased: it does not make a difference
between french and French.
Model description
----------... | [
"### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\n\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\n\nand in TensorFlow:\n\n\nTraining data\n-------------\n\n\nThe FrALBERT model was pretrained on 4go of French Wikipedia (excluding... | [
"TAGS\n#transformers #pytorch #safetensors #albert #fill-mask #fr #dataset-wikipedia #arxiv-1909.11942 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\n\n\nHere is how to use this mod... |
text-generation | transformers |
# DialoGPT Small Rick
| {"tags": ["conversational"]} | qwerty/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
|
# DialoGPT Small Rick
| [
"# DialoGPT Small Rick"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# DialoGPT Small Rick"
] |
null | null | AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA | {} | qwgqq/test | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA | [] | [
"TAGS\n#region-us \n"
] |
null | null | なぜカジノボーナスがあるのか
ここでは、日本のギャンブラー向けのカジノ無料ボーナスの主な利点を紹介いたします。オンラインでギャンブルする時に楽しんでいただける利点から始めましょう。プレイ中に、ボーナスではバンクロールを容易に増やすことが出来ます。オンラインギャンブルの初心者の場合、無料ボーナスでのゲームの試用は良い機会です。
カジノサイトが提供する特別なプロモーションには、自動車のリワード、イベント、大会や番組のチケットなどがあります。掛け金の要件が、ゲームのプロセスから楽しい気持ちを消してしまう事が、ボーナスの一つのデメリットとなります。
日本のギャンブラーは、オンラインカジノでプレーするときに、常にさまざまな製品を要求しま... | {} | qytocompany/bonus | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| なぜカジノボーナスがあるのか
ここでは、日本のギャンブラー向けのカジノ無料ボーナスの主な利点を紹介いたします。オンラインでギャンブルする時に楽しんでいただける利点から始めましょう。プレイ中に、ボーナスではバンクロールを容易に増やすことが出来ます。オンラインギャンブルの初心者の場合、無料ボーナスでのゲームの試用は良い機会です。
カジノサイトが提供する特別なプロモーションには、自動車のリワード、イベント、大会や番組のチケットなどがあります。掛け金の要件が、ゲームのプロセスから楽しい気持ちを消してしまう事が、ボーナスの一つのデメリットとなります。
日本のギャンブラーは、オンラインカジノでプレーするときに、常にさまざまな製品を要求しま... | [] | [
"TAGS\n#region-us \n"
] |
sentence-similarity | sentence-transformers |
# r2d2/stsb-bertweet-base-v0
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)
Using this model beco... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | r2d2/stsb-bertweet-base-v0 | null | [
"sentence-transformers",
"pytorch",
"roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #roberta #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# r2d2/stsb-bertweet-base-v0
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-transformers installed:
... | [
"# r2d2/stsb-bertweet-base-v0\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-transformers inst... | [
"TAGS\n#sentence-transformers #pytorch #roberta #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# r2d2/stsb-bertweet-base-v0\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... |
text-generation | transformers |
# DialoGPT Trained on the Speech of a Game Character
This is an instance of [microsoft/DialoGPT-medium](https://huggingface.co/microsoft/DialoGPT-medium) trained on a game character, Joshua from [The World Ends With You](https://en.wikipedia.org/wiki/The_World_Ends_with_You). The data comes from [a Kaggle game script... | {"license": "mit", "tags": ["conversational"], "thumbnail": "https://raw.githubusercontent.com/RuolinZheng08/twewy-discord-chatbot/main/gif-demo/icon.png"} | r3cdhummingbird/DialoGPT-medium-joshua | 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
|
# DialoGPT Trained on the Speech of a Game Character
This is an instance of microsoft/DialoGPT-medium trained on a game character, Joshua from The World Ends With You. The data comes from a Kaggle game script dataset.
I built a Discord AI chatbot based on this model. Check out my GitHub repo.
Chat with the model:
| [
"# DialoGPT Trained on the Speech of a Game Character\n\nThis is an instance of microsoft/DialoGPT-medium trained on a game character, Joshua from The World Ends With You. The data comes from a Kaggle game script dataset.\n\nI built a Discord AI chatbot based on this model. Check out my GitHub repo.\n\nChat with th... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# DialoGPT Trained on the Speech of a Game Character\n\nThis is an instance of microsoft/DialoGPT-medium trained on a game character, Joshua fro... |
text-generation | transformers |
# DialoGPT Trained on the Speech of a Game Character
This is an instance of [microsoft/DialoGPT-medium](https://huggingface.co/microsoft/DialoGPT-medium) trained on a game character, Joshua from [The World Ends With You](https://en.wikipedia.org/wiki/The_World_Ends_with_You). The data comes from [a Kaggle game script... | {"license": "mit", "tags": ["conversational"], "thumbnail": "https://raw.githubusercontent.com/RuolinZheng08/twewy-discord-chatbot/main/gif-demo/icon.png"} | r3dhummingbird/DialoGPT-medium-joshua | null | [
"transformers",
"pytorch",
"safetensors",
"gpt2",
"text-generation",
"conversational",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #gpt2 #text-generation #conversational #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# DialoGPT Trained on the Speech of a Game Character
This is an instance of microsoft/DialoGPT-medium trained on a game character, Joshua from The World Ends With You. The data comes from a Kaggle game script dataset.
I built a Discord AI chatbot based on this model. Check out my GitHub repo.
Chat with the model:
| [
"# DialoGPT Trained on the Speech of a Game Character\n\nThis is an instance of microsoft/DialoGPT-medium trained on a game character, Joshua from The World Ends With You. The data comes from a Kaggle game script dataset.\n\nI built a Discord AI chatbot based on this model. Check out my GitHub repo.\n\nChat with th... | [
"TAGS\n#transformers #pytorch #safetensors #gpt2 #text-generation #conversational #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# DialoGPT Trained on the Speech of a Game Character\n\nThis is an instance of microsoft/DialoGPT-medium trained on a ga... |
text-generation | transformers |
# DialoGPT Trained on the Speech of a Game Character
This is an instance of [microsoft/DialoGPT-medium](https://huggingface.co/microsoft/DialoGPT-medium) trained on a game character, Neku Sakuraba from [The World Ends With You](https://en.wikipedia.org/wiki/The_World_Ends_with_You). The data comes from [a Kaggle game... | {"license": "mit", "tags": ["conversational"], "thumbnail": "https://huggingface.co/front/thumbnails/dialogpt.png"} | r3dhummingbird/DialoGPT-medium-neku | null | [
"transformers",
"pytorch",
"safetensors",
"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 #safetensors #gpt2 #text-generation #conversational #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# DialoGPT Trained on the Speech of a Game Character
This is an instance of microsoft/DialoGPT-medium trained on a game character, Neku Sakuraba from The World Ends With You. The data comes from a Kaggle game script dataset.
Chat with the model:
| [
"# DialoGPT Trained on the Speech of a Game Character\n\nThis is an instance of microsoft/DialoGPT-medium trained on a game character, Neku Sakuraba from The World Ends With You. The data comes from a Kaggle game script dataset.\n\nChat with the model:"
] | [
"TAGS\n#transformers #pytorch #safetensors #gpt2 #text-generation #conversational #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# DialoGPT Trained on the Speech of a Game Character\n\nThis is an instance of microsoft/DialoGPT-medium trained on a game characte... |
text-generation | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | r3dhummingbird/DialoGPT-small-harrypotter | null | [
"transformers",
"pytorch",
"safetensors",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #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 #safetensors #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Harry Potter DialoGPT Model"
] |
text-generation | transformers |
# DialoGPT Trained on the Speech of a Game Character
This is an instance of [microsoft/DialoGPT-small](https://huggingface.co/microsoft/DialoGPT-small) trained on a game character, Neku Sakuraba from [The World Ends With You](https://en.wikipedia.org/wiki/The_World_Ends_with_You). The data comes from [a Kaggle game s... | {"license": "mit", "tags": ["conversational"], "thumbnail": "https://huggingface.co/front/thumbnails/dialogpt.png"} | r3dhummingbird/DialoGPT-small-neku | null | [
"transformers",
"pytorch",
"safetensors",
"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 #safetensors #gpt2 #text-generation #conversational #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# DialoGPT Trained on the Speech of a Game Character
This is an instance of microsoft/DialoGPT-small trained on a game character, Neku Sakuraba from The World Ends With You. The data comes from a Kaggle game script dataset.
Chat with the model:
| [
"# DialoGPT Trained on the Speech of a Game Character\n\nThis is an instance of microsoft/DialoGPT-small trained on a game character, Neku Sakuraba from The World Ends With You. The data comes from a Kaggle game script dataset.\n\nChat with the model:"
] | [
"TAGS\n#transformers #pytorch #safetensors #gpt2 #text-generation #conversational #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# DialoGPT Trained on the Speech of a Game Character\n\nThis is an instance of microsoft/DialoGPT-small trained on a game character... |
text-classification | transformers | # Sentiment Analysis of English Tweets with BERTsent
**BERTsent**: A finetuned **BERT** based **sent**iment classifier for English language tweets.
BERTsent is trained with SemEval 2017 corpus (39k plus tweets) and is based on [bertweet-base](https://github.com/VinAIResearch/BERTweet) that was trained on 850M English... | {} | rabindralamsal/BERTsent | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"roberta",
"text-classification",
"arxiv:2206.10471",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2206.10471"
] | [] | TAGS
#transformers #pytorch #tf #safetensors #roberta #text-classification #arxiv-2206.10471 #autotrain_compatible #endpoints_compatible #region-us
| # Sentiment Analysis of English Tweets with BERTsent
BERTsent: A finetuned BERT based sentiment classifier for English language tweets.
BERTsent is trained with SemEval 2017 corpus (39k plus tweets) and is based on bertweet-base that was trained on 850M English Tweets (cased) and additional 23M COVID-19 English Tweet... | [
"# Sentiment Analysis of English Tweets with BERTsent\n\nBERTsent: A finetuned BERT based sentiment classifier for English language tweets.\n\nBERTsent is trained with SemEval 2017 corpus (39k plus tweets) and is based on bertweet-base that was trained on 850M English Tweets (cased) and additional 23M COVID-19 Engl... | [
"TAGS\n#transformers #pytorch #tf #safetensors #roberta #text-classification #arxiv-2206.10471 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Sentiment Analysis of English Tweets with BERTsent\n\nBERTsent: A finetuned BERT based sentiment classifier for English language tweets.\n\nBERTsent is trai... |
null | transformers |
# Romanian DistilBERT
This repository contains the uncased Romanian DistilBERT (named Distil-BERT-base-ro in the paper). The teacher model used for distillation is: [dumitrescustefan/bert-base-romanian-cased-v1](https://huggingface.co/dumitrescustefan/bert-base-romanian-cased-v1).
The model was introduced in [this p... | {"language": "ro", "license": "mit", "datasets": ["oscar", "wikipedia"]} | racai/distilbert-base-romanian-cased | null | [
"transformers",
"pytorch",
"tf",
"jax",
"distilbert",
"ro",
"dataset:oscar",
"dataset:wikipedia",
"arxiv:2112.12650",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2112.12650"
] | [
"ro"
] | TAGS
#transformers #pytorch #tf #jax #distilbert #ro #dataset-oscar #dataset-wikipedia #arxiv-2112.12650 #license-mit #endpoints_compatible #region-us
| Romanian DistilBERT
===================
This repository contains the uncased Romanian DistilBERT (named Distil-BERT-base-ro in the paper). The teacher model used for distillation is: dumitrescustefan/bert-base-romanian-cased-v1.
The model was introduced in this paper. The adjacent code can be found
here.
Usage
--... | [
"### BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #tf #jax #distilbert #ro #dataset-oscar #dataset-wikipedia #arxiv-2112.12650 #license-mit #endpoints_compatible #region-us \n",
"### BibTeX entry and citation info"
] |
null | transformers |
# Romanian DistilBERT
This repository contains the uncased Romanian DistilBERT (named Distil-RoBERT-base in the paper). The teacher model used for distillation is: [readerbench/RoBERT-base](https://huggingface.co/readerbench/RoBERT-base).
The model was introduced in [this paper](https://arxiv.org/abs/2112.12650). T... | {"language": "ro", "license": "mit", "datasets": ["oscar", "wikipedia"]} | racai/distilbert-base-romanian-uncased | null | [
"transformers",
"pytorch",
"tf",
"jax",
"distilbert",
"ro",
"dataset:oscar",
"dataset:wikipedia",
"arxiv:2112.12650",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2112.12650"
] | [
"ro"
] | TAGS
#transformers #pytorch #tf #jax #distilbert #ro #dataset-oscar #dataset-wikipedia #arxiv-2112.12650 #license-mit #endpoints_compatible #region-us
| Romanian DistilBERT
===================
This repository contains the uncased Romanian DistilBERT (named Distil-RoBERT-base in the paper). The teacher model used for distillation is: readerbench/RoBERT-base.
The model was introduced in this paper. The adjacent code can be found
here.
Usage
-----
Model Size
-----... | [
"### BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #tf #jax #distilbert #ro #dataset-oscar #dataset-wikipedia #arxiv-2112.12650 #license-mit #endpoints_compatible #region-us \n",
"### BibTeX entry and citation info"
] |
null | transformers |
# Romanian DistilBERT
This repository contains the a Romanian cased version of DistilBERT (named DistilMulti-BERT-base-ro in the paper) that was obtained by distilling an ensemble of two teacher models: [dumitrescustefan/bert-base-romanian-cased-v1](https://huggingface.co/dumitrescustefan/bert-base-romanian-cased-v1)... | {"language": "ro", "license": "mit", "datasets": ["oscar", "wikipedia"]} | racai/distilbert-multi-base-romanian-cased | null | [
"transformers",
"pytorch",
"tf",
"jax",
"distilbert",
"ro",
"dataset:oscar",
"dataset:wikipedia",
"arxiv:2112.12650",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2112.12650"
] | [
"ro"
] | TAGS
#transformers #pytorch #tf #jax #distilbert #ro #dataset-oscar #dataset-wikipedia #arxiv-2112.12650 #license-mit #endpoints_compatible #region-us
| Romanian DistilBERT
===================
This repository contains the a Romanian cased version of DistilBERT (named DistilMulti-BERT-base-ro in the paper) that was obtained by distilling an ensemble of two teacher models: dumitrescustefan/bert-base-romanian-cased-v1 and readerbench/RoBERT-base.
The model was introdu... | [
"### BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #tf #jax #distilbert #ro #dataset-oscar #dataset-wikipedia #arxiv-2112.12650 #license-mit #endpoints_compatible #region-us \n",
"### BibTeX entry and citation info"
] |
text-generation | transformers |
# a chatbot based on Cosmo Kramer | {"tags": ["conversational"]} | rachelcorey/DialoGPT-medium-kramer | 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
|
# a chatbot based on Cosmo Kramer | [
"# a chatbot based on Cosmo Kramer"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# a chatbot based on Cosmo Kramer"
] |
text-generation | transformers |
# a chatbot based on Niles Crane | {"tags": ["conversational"]} | rachelcorey/DialoGPT-medium-niles | 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
|
# a chatbot based on Niles Crane | [
"# a chatbot based on Niles Crane"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# a chatbot based on Niles Crane"
] |
token-classification | spacy |
# NoCy transformer model
NoCy is a Norwegian transformer model for SpaCy, based on `ltgoslo/norbert` and trained on the NorNE named entity corpus (`NbAiLab/norne`).
The model is made by and for SpaCy, based on the DaCy blueprint (https://github.com/centre-for-humanities-computing/DaCy). Code for the project can be ... | {"language": ["nb"], "tags": ["spacy", "token-classification"]} | radbrt/nb_nocy_trf | null | [
"spacy",
"token-classification",
"nb",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"nb"
] | TAGS
#spacy #token-classification #nb #region-us
| NoCy transformer model
======================
NoCy is a Norwegian transformer model for SpaCy, based on 'ltgoslo/norbert' and trained on the NorNE named entity corpus ('NbAiLab/norne').
The model is made by and for SpaCy, based on the DaCy blueprint (URL Code for the project can be found on github: URL
The model ... | [
"### Label Scheme\n\n\n\nView label scheme (265 labels for 4 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #nb #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (265 labels for 4 components)",
"### Accuracy"
] |
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": []}]} | radhakri119/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: 0.4780
* Wer: 0.3403
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... |
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