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sentence-similarity | sentence-transformers |
# sentence-transformers/nli-roberta-base-v2
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.
## Usage (Sentence-Transformers)
Using this model becomes easy when you h... | {"license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/nli-roberta-base-v2 | null | [
"sentence-transformers",
"pytorch",
"tf",
"jax",
"roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.10084"
] | [] | TAGS
#sentence-transformers #pytorch #tf #jax #roberta #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us
|
# sentence-transformers/nli-roberta-base-v2
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... | [
"# sentence-transformers/nli-roberta-base-v2\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-tr... | [
"TAGS\n#sentence-transformers #pytorch #tf #jax #roberta #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# sentence-transformers/nli-roberta-base-v2\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768... |
sentence-similarity | sentence-transformers |
**⚠️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: [SBERT.net - Pretrained Models](https://www.sbert.net/docs/pretrained_models.html)**
# sentence-transformers/nli-roberta-base
This is a [sentence-transformer... | {"license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/nli-roberta-base | null | [
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"pytorch",
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"roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.10084"
] | [] | TAGS
#sentence-transformers #pytorch #tf #roberta #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us
|
️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: URL - Pretrained Models
# sentence-transformers/nli-roberta-base
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dens... | [
"# sentence-transformers/nli-roberta-base\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-trans... | [
"TAGS\n#sentence-transformers #pytorch #tf #roberta #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# sentence-transformers/nli-roberta-base\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensi... |
sentence-similarity | sentence-transformers |
**⚠️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: [SBERT.net - Pretrained Models](https://www.sbert.net/docs/pretrained_models.html)**
# sentence-transformers/nli-roberta-large
This is a [sentence-transforme... | {"license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/nli-roberta-large | null | [
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"pytorch",
"jax",
"roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.10084"
] | [] | TAGS
#sentence-transformers #pytorch #jax #roberta #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us
|
️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: URL - Pretrained Models
# sentence-transformers/nli-roberta-large
This is a sentence-transformers model: It maps sentences & paragraphs to a 1024 dimensional de... | [
"# sentence-transformers/nli-roberta-large\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 1024 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-tra... | [
"TAGS\n#sentence-transformers #pytorch #jax #roberta #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# sentence-transformers/nli-roberta-large\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 1024 dime... |
sentence-similarity | sentence-transformers |
# sentence-transformers/nq-distilbert-base-v1
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.
## Usage (Sentence-Transformers)
Using this model becomes easy when you... | {"license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/nq-distilbert-base-v1 | null | [
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"pytorch",
"tf",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.10084"
] | [] | TAGS
#sentence-transformers #pytorch #tf #distilbert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us
|
# sentence-transformers/nq-distilbert-base-v1
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transforme... | [
"# sentence-transformers/nq-distilbert-base-v1\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-... | [
"TAGS\n#sentence-transformers #pytorch #tf #distilbert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# sentence-transformers/nq-distilbert-base-v1\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768... |
sentence-similarity | sentence-transformers |
# sentence-transformers/paraphrase-MiniLM-L12-v2
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/paraphrase-MiniLM-L12-v2 | null | [
"sentence-transformers",
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"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.10084"
] | [] | TAGS
#sentence-transformers #pytorch #tf #bert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# sentence-transformers/paraphrase-MiniLM-L12-v2
This is a sentence-transformers model: It maps sentences & paragraphs to a 384 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-transfo... | [
"# sentence-transformers/paraphrase-MiniLM-L12-v2\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 384 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 senten... | [
"TAGS\n#sentence-transformers #pytorch #tf #bert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# sentence-transformers/paraphrase-MiniLM-L12-v2\n\nThis is a sentence-transformers model: It maps sentences & paragraphs ... |
sentence-similarity | sentence-transformers |
# sentence-transformers/paraphrase-MiniLM-L3-v2
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when y... | {"license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "datasets": ["flax-sentence-embeddings/stackexchange_xml", "s2orc", "ms_marco", "wiki_atomic_edits", "snli", "multi_nli", "embedding-data/altlex", "embedding... | sentence-transformers/paraphrase-MiniLM-L3-v2 | null | [
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"dataset:s2orc",
"dataset:ms_marco",
"dataset:wiki_atomic_edits",
"dataset:snli",
"dataset:multi_nli",
"dataset:embedding-dat... | null | 2022-03-02T23:29:05+00:00 | [
"1908.10084"
] | [] | TAGS
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# sentence-transformers/paraphrase-MiniLM-L3-v2
This is a sentence-transformers model: It maps sentences & paragraphs to a 384 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-transfor... | [
"# sentence-transformers/paraphrase-MiniLM-L3-v2\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 384 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 sentenc... | [
"TAGS\n#sentence-transformers #pytorch #tf #bert #feature-extraction #sentence-similarity #transformers #dataset-flax-sentence-embeddings/stackexchange_xml #dataset-s2orc #dataset-ms_marco #dataset-wiki_atomic_edits #dataset-snli #dataset-multi_nli #dataset-embedding-data/altlex #dataset-embedding-data/simple-wiki ... |
sentence-similarity | sentence-transformers |
# sentence-transformers/paraphrase-MiniLM-L6-v2
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when y... | {"license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/paraphrase-MiniLM-L6-v2 | null | [
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"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.10084"
] | [] | TAGS
#sentence-transformers #pytorch #tf #bert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# sentence-transformers/paraphrase-MiniLM-L6-v2
This is a sentence-transformers model: It maps sentences & paragraphs to a 384 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-transfor... | [
"# sentence-transformers/paraphrase-MiniLM-L6-v2\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 384 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 sentenc... | [
"TAGS\n#sentence-transformers #pytorch #tf #bert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# sentence-transformers/paraphrase-MiniLM-L6-v2\n\nThis is a sentence-transformers model: It maps sentences & paragraphs t... |
sentence-similarity | sentence-transformers |
# sentence-transformers/paraphrase-TinyBERT-L6-v2
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.
## Usage (Sentence-Transformers)
Using this model becomes easy when... | {"license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/paraphrase-TinyBERT-L6-v2 | null | [
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"sentence-similarity",
"transformers",
"arxiv:1908.10084",
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"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.10084"
] | [] | TAGS
#sentence-transformers #pytorch #tf #bert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# sentence-transformers/paraphrase-TinyBERT-L6-v2
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-transf... | [
"# sentence-transformers/paraphrase-TinyBERT-L6-v2\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 sente... | [
"TAGS\n#sentence-transformers #pytorch #tf #bert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# sentence-transformers/paraphrase-TinyBERT-L6-v2\n\nThis is a sentence-transformers model: It maps sentences & paragraphs... |
sentence-similarity | sentence-transformers |
# sentence-transformers/paraphrase-albert-base-v2
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.
## Usage (Sentence-Transformers)
Using this model becomes easy when... | {"license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/paraphrase-albert-base-v2 | null | [
"sentence-transformers",
"pytorch",
"tf",
"albert",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.10084"
] | [] | TAGS
#sentence-transformers #pytorch #tf #albert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# sentence-transformers/paraphrase-albert-base-v2
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-transf... | [
"# sentence-transformers/paraphrase-albert-base-v2\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 sente... | [
"TAGS\n#sentence-transformers #pytorch #tf #albert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# sentence-transformers/paraphrase-albert-base-v2\n\nThis is a sentence-transformers model: It maps sentences & paragrap... |
sentence-similarity | sentence-transformers |
# sentence-transformers/paraphrase-albert-small-v2
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.
## Usage (Sentence-Transformers)
Using this model becomes easy whe... | {"license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "datasets": ["flax-sentence-embeddings/stackexchange_xml", "s2orc", "ms_marco", "wiki_atomic_edits", "snli", "multi_nli", "embedding-data/altlex", "embedding... | sentence-transformers/paraphrase-albert-small-v2 | null | [
"sentence-transformers",
"pytorch",
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"albert",
"feature-extraction",
"sentence-similarity",
"transformers",
"dataset:flax-sentence-embeddings/stackexchange_xml",
"dataset:s2orc",
"dataset:ms_marco",
"dataset:wiki_atomic_edits",
"dataset:snli",
"dataset:multi_nli",
"dataset:e... | null | 2022-03-02T23:29:05+00:00 | [
"1908.10084"
] | [] | TAGS
#sentence-transformers #pytorch #tf #rust #albert #feature-extraction #sentence-similarity #transformers #dataset-flax-sentence-embeddings/stackexchange_xml #dataset-s2orc #dataset-ms_marco #dataset-wiki_atomic_edits #dataset-snli #dataset-multi_nli #dataset-embedding-data/altlex #dataset-embedding-data/simple-wik... |
# sentence-transformers/paraphrase-albert-small-v2
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-trans... | [
"# sentence-transformers/paraphrase-albert-small-v2\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 sent... | [
"TAGS\n#sentence-transformers #pytorch #tf #rust #albert #feature-extraction #sentence-similarity #transformers #dataset-flax-sentence-embeddings/stackexchange_xml #dataset-s2orc #dataset-ms_marco #dataset-wiki_atomic_edits #dataset-snli #dataset-multi_nli #dataset-embedding-data/altlex #dataset-embedding-data/simp... |
sentence-similarity | sentence-transformers |
# sentence-transformers/paraphrase-distilroberta-base-v1
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.
## Usage (Sentence-Transformers)
Using this model becomes ea... | {"license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/paraphrase-distilroberta-base-v1 | null | [
"sentence-transformers",
"pytorch",
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"sentence-similarity",
"transformers",
"arxiv:1908.10084",
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"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.10084"
] | [] | TAGS
#sentence-transformers #pytorch #tf #jax #roberta #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# sentence-transformers/paraphrase-distilroberta-base-v1
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... | [
"# sentence-transformers/paraphrase-distilroberta-base-v1\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 hav... | [
"TAGS\n#sentence-transformers #pytorch #tf #jax #roberta #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# sentence-transformers/paraphrase-distilroberta-base-v1\n\nThis is a sentence-transformers model: It maps sentenc... |
sentence-similarity | sentence-transformers |
# sentence-transformers/paraphrase-distilroberta-base-v2
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.
## Usage (Sentence-Transformers)
Using this model becomes ea... | {"license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/paraphrase-distilroberta-base-v2 | null | [
"sentence-transformers",
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"tf",
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"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.10084"
] | [] | TAGS
#sentence-transformers #pytorch #tf #jax #roberta #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us
|
# sentence-transformers/paraphrase-distilroberta-base-v2
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... | [
"# sentence-transformers/paraphrase-distilroberta-base-v2\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 hav... | [
"TAGS\n#sentence-transformers #pytorch #tf #jax #roberta #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# sentence-transformers/paraphrase-distilroberta-base-v2\n\nThis is a sentence-transformers model: It maps sentences & paragr... |
sentence-similarity | sentence-transformers |
# sentence-transformers/paraphrase-mpnet-base-v2
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.
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/paraphrase-mpnet-base-v2 | null | [
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"1908.10084"
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|
# sentence-transformers/paraphrase-mpnet-base-v2
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-transfo... | [
"# sentence-transformers/paraphrase-mpnet-base-v2\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 senten... | [
"TAGS\n#sentence-transformers #pytorch #tf #mpnet #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# sentence-transformers/paraphrase-mpnet-base-v2\n\nThis is a sentence-transformers model: It maps sentences & paragraphs... |
sentence-similarity | sentence-transformers |
# sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becom... | {"language": ["multilingual", "ar", "bg", "ca", "cs", "da", "de", "el", "en", "es", "et", "fa", "fi", "fr", "gl", "gu", "he", "hi", "hr", "hu", "hy", "id", "it", "ja", "ka", "ko", "ku", "lt", "lv", "mk", "mn", "mr", "ms", "my", "nb", "nl", "pl", "pt", "ro", "ru", "sk", "sl", "sq", "sr", "sv", "th", "tr", "uk", "ur", "v... | sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 | null | [
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# sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
This is a sentence-transformers model: It maps sentences & paragraphs to a 384 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 sen... | [
"# sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 384 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 yo... | [
"TAGS\n#sentence-transformers #pytorch #tf #bert #feature-extraction #sentence-similarity #transformers #multilingual #ar #bg #ca #cs #da #de #el #en #es #et #fa #fi #fr #gl #gu #he #hi #hr #hu #hy #id #it #ja #ka #ko #ku #lt #lv #mk #mn #mr #ms #my #nb #nl #pl #pt #ro #ru #sk #sl #sq #sr #sv #th #tr #uk #ur #vi #a... |
sentence-similarity | sentence-transformers |
# sentence-transformers/paraphrase-multilingual-mpnet-base-v2
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.
## Usage (Sentence-Transformers)
Using this model becom... | {"language": ["multilingual", "ar", "bg", "ca", "cs", "da", "de", "el", "en", "es", "et", "fa", "fi", "fr", "gl", "gu", "he", "hi", "hr", "hu", "hy", "id", "it", "ja", "ka", "ko", "ku", "lt", "lv", "mk", "mn", "mr", "ms", "my", "nb", "nl", "pl", "pt", "ro", "ru", "sk", "sl", "sq", "sr", "sv", "th", "tr", "uk", "ur", "v... | sentence-transformers/paraphrase-multilingual-mpnet-base-v2 | null | [
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... | null | 2022-03-02T23:29:05+00:00 | [
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# sentence-transformers/paraphrase-multilingual-mpnet-base-v2
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 sen... | [
"# sentence-transformers/paraphrase-multilingual-mpnet-base-v2\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 yo... | [
"TAGS\n#sentence-transformers #pytorch #tf #xlm-roberta #feature-extraction #sentence-similarity #transformers #multilingual #ar #bg #ca #cs #da #de #el #en #es #et #fa #fi #fr #gl #gu #he #hi #hr #hu #hy #id #it #ja #ka #ko #ku #lt #lv #mk #mn #mr #ms #my #nb #nl #pl #pt #ro #ru #sk #sl #sq #sr #sv #th #tr #uk #ur... |
sentence-similarity | sentence-transformers |
# sentence-transformers/paraphrase-xlm-r-multilingual-v1
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.
## Usage (Sentence-Transformers)
Using this model becomes ea... | {"license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/paraphrase-xlm-r-multilingual-v1 | null | [
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"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.10084"
] | [] | TAGS
#sentence-transformers #pytorch #tf #xlm-roberta #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# sentence-transformers/paraphrase-xlm-r-multilingual-v1
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... | [
"# sentence-transformers/paraphrase-xlm-r-multilingual-v1\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 hav... | [
"TAGS\n#sentence-transformers #pytorch #tf #xlm-roberta #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# sentence-transformers/paraphrase-xlm-r-multilingual-v1\n\nThis is a sentence-transformers model: It maps sentence... |
sentence-similarity | sentence-transformers |
# sentence-transformers/quora-distilbert-base
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.
## Usage (Sentence-Transformers)
Using this model becomes easy when you... | {"license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/quora-distilbert-base | null | [
"sentence-transformers",
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"tf",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.10084"
] | [] | TAGS
#sentence-transformers #pytorch #tf #distilbert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us
|
# sentence-transformers/quora-distilbert-base
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transforme... | [
"# sentence-transformers/quora-distilbert-base\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-... | [
"TAGS\n#sentence-transformers #pytorch #tf #distilbert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# sentence-transformers/quora-distilbert-base\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768... |
sentence-similarity | sentence-transformers |
# sentence-transformers/quora-distilbert-multilingual
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.
## Usage (Sentence-Transformers)
Using this model becomes easy ... | {"license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/quora-distilbert-multilingual | null | [
"sentence-transformers",
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"tf",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.10084"
] | [] | TAGS
#sentence-transformers #pytorch #tf #distilbert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us
|
# sentence-transformers/quora-distilbert-multilingual
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-tr... | [
"# sentence-transformers/quora-distilbert-multilingual\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 s... | [
"TAGS\n#sentence-transformers #pytorch #tf #distilbert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# sentence-transformers/quora-distilbert-multilingual\n\nThis is a sentence-transformers model: It maps sentences & paragraphs ... |
sentence-similarity | sentence-transformers |
**⚠️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: [SBERT.net - Pretrained Models](https://www.sbert.net/docs/pretrained_models.html)**
# sentence-transformers/roberta-base-nli-mean-tokens
This is a [sentence... | {"license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/roberta-base-nli-mean-tokens | null | [
"sentence-transformers",
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"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.10084"
] | [] | TAGS
#sentence-transformers #pytorch #tf #roberta #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us
|
️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: URL - Pretrained Models
# sentence-transformers/roberta-base-nli-mean-tokens
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dime... | [
"# sentence-transformers/roberta-base-nli-mean-tokens\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 se... | [
"TAGS\n#sentence-transformers #pytorch #tf #roberta #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# sentence-transformers/roberta-base-nli-mean-tokens\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a... |
sentence-similarity | sentence-transformers |
**⚠️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: [SBERT.net - Pretrained Models](https://www.sbert.net/docs/pretrained_models.html)**
# sentence-transformers/roberta-base-nli-stsb-mean-tokens
This is a [sen... | {"license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/roberta-base-nli-stsb-mean-tokens | null | [
"sentence-transformers",
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"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.10084"
] | [] | TAGS
#sentence-transformers #pytorch #tf #jax #roberta #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us
|
️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: URL - Pretrained Models
# sentence-transformers/roberta-base-nli-stsb-mean-tokens
This is a sentence-transformers model: It maps sentences & paragraphs to a 768... | [
"# sentence-transformers/roberta-base-nli-stsb-mean-tokens\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 ha... | [
"TAGS\n#sentence-transformers #pytorch #tf #jax #roberta #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# sentence-transformers/roberta-base-nli-stsb-mean-tokens\n\nThis is a sentence-transformers model: It maps sentences & parag... |
sentence-similarity | sentence-transformers |
**⚠️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: [SBERT.net - Pretrained Models](https://www.sbert.net/docs/pretrained_models.html)**
# sentence-transformers/roberta-large-nli-mean-tokens
This is a [sentenc... | {"license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/roberta-large-nli-mean-tokens | null | [
"sentence-transformers",
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"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.10084"
] | [] | TAGS
#sentence-transformers #pytorch #roberta #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us
|
️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: URL - Pretrained Models
# sentence-transformers/roberta-large-nli-mean-tokens
This is a sentence-transformers model: It maps sentences & paragraphs to a 1024 di... | [
"# sentence-transformers/roberta-large-nli-mean-tokens\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 1024 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 ... | [
"TAGS\n#sentence-transformers #pytorch #roberta #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# sentence-transformers/roberta-large-nli-mean-tokens\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 10... |
sentence-similarity | sentence-transformers |
**⚠️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: [SBERT.net - Pretrained Models](https://www.sbert.net/docs/pretrained_models.html)**
# sentence-transformers/roberta-large-nli-stsb-mean-tokens
This is a [se... | {"license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/roberta-large-nli-stsb-mean-tokens | null | [
"sentence-transformers",
"pytorch",
"tf",
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"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.10084"
] | [] | TAGS
#sentence-transformers #pytorch #tf #jax #roberta #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us
|
️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: URL - Pretrained Models
# sentence-transformers/roberta-large-nli-stsb-mean-tokens
This is a sentence-transformers model: It maps sentences & paragraphs to a 10... | [
"# sentence-transformers/roberta-large-nli-stsb-mean-tokens\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 1024 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 ... | [
"TAGS\n#sentence-transformers #pytorch #tf #jax #roberta #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# sentence-transformers/roberta-large-nli-stsb-mean-tokens\n\nThis is a sentence-transformers model: It maps sentences & para... |
sentence-similarity | sentence-transformers |
# sentence-transformers/sentence-t5-base
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space. The model works well for sentence similarity tasks, but doesn't perform that well for semantic search tasks.
This model was converted from t... | {"language": "en", "license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/sentence-t5-base | null | [
"sentence-transformers",
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"sentence-similarity",
"en",
"arxiv:2108.08877",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2108.08877"
] | [
"en"
] | TAGS
#sentence-transformers #pytorch #rust #t5 #feature-extraction #sentence-similarity #en #arxiv-2108.08877 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# sentence-transformers/sentence-t5-base
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space. The model works well for sentence similarity tasks, but doesn't perform that well for semantic search tasks.
This model was converted from the Tensorflow model st5-b... | [
"# sentence-transformers/sentence-t5-base\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space. The model works well for sentence similarity tasks, but doesn't perform that well for semantic search tasks.\n\nThis model was converted from the Tensorflow mod... | [
"TAGS\n#sentence-transformers #pytorch #rust #t5 #feature-extraction #sentence-similarity #en #arxiv-2108.08877 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# sentence-transformers/sentence-t5-base\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimension... |
sentence-similarity | sentence-transformers |
# sentence-transformers/sentence-t5-large
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space. The model works well for sentence similarity tasks, but doesn't perform that well for semantic search tasks.
This model was converted from ... | {"language": "en", "license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/sentence-t5-large | null | [
"sentence-transformers",
"pytorch",
"t5",
"feature-extraction",
"sentence-similarity",
"en",
"arxiv:2108.08877",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2108.08877"
] | [
"en"
] | TAGS
#sentence-transformers #pytorch #t5 #feature-extraction #sentence-similarity #en #arxiv-2108.08877 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# sentence-transformers/sentence-t5-large
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space. The model works well for sentence similarity tasks, but doesn't perform that well for semantic search tasks.
This model was converted from the Tensorflow model st5-... | [
"# sentence-transformers/sentence-t5-large\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space. The model works well for sentence similarity tasks, but doesn't perform that well for semantic search tasks.\n\nThis model was converted from the Tensorflow mo... | [
"TAGS\n#sentence-transformers #pytorch #t5 #feature-extraction #sentence-similarity #en #arxiv-2108.08877 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# sentence-transformers/sentence-t5-large\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional de... |
sentence-similarity | sentence-transformers |
# sentence-transformers/sentence-t5-xl
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space. The model works well for sentence similarity tasks, but doesn't perform that well for semantic search tasks.
This model was converted from the ... | {"language": "en", "license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/sentence-t5-xl | null | [
"sentence-transformers",
"pytorch",
"t5",
"feature-extraction",
"sentence-similarity",
"en",
"arxiv:2108.08877",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2108.08877"
] | [
"en"
] | TAGS
#sentence-transformers #pytorch #t5 #feature-extraction #sentence-similarity #en #arxiv-2108.08877 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# sentence-transformers/sentence-t5-xl
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space. The model works well for sentence similarity tasks, but doesn't perform that well for semantic search tasks.
This model was converted from the Tensorflow model st5-3b-1... | [
"# sentence-transformers/sentence-t5-xl\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space. The model works well for sentence similarity tasks, but doesn't perform that well for semantic search tasks.\nThis model was converted from the Tensorflow model s... | [
"TAGS\n#sentence-transformers #pytorch #t5 #feature-extraction #sentence-similarity #en #arxiv-2108.08877 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# sentence-transformers/sentence-t5-xl\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense... |
sentence-similarity | sentence-transformers |
# sentence-transformers/sentence-t5-xxl
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space. The model works well for sentence similarity tasks, but doesn't perform that well for semantic search tasks.
This model was converted from th... | {"language": "en", "license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/sentence-t5-xxl | null | [
"sentence-transformers",
"pytorch",
"t5",
"feature-extraction",
"sentence-similarity",
"en",
"arxiv:2108.08877",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2108.08877"
] | [
"en"
] | TAGS
#sentence-transformers #pytorch #t5 #feature-extraction #sentence-similarity #en #arxiv-2108.08877 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# sentence-transformers/sentence-t5-xxl
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space. The model works well for sentence similarity tasks, but doesn't perform that well for semantic search tasks.
This model was converted from the Tensorflow model st5-11... | [
"# sentence-transformers/sentence-t5-xxl\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space. The model works well for sentence similarity tasks, but doesn't perform that well for semantic search tasks.\n\nThis model was converted from the Tensorflow mode... | [
"TAGS\n#sentence-transformers #pytorch #t5 #feature-extraction #sentence-similarity #en #arxiv-2108.08877 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# sentence-transformers/sentence-t5-xxl\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dens... |
sentence-similarity | sentence-transformers |
**⚠️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: [SBERT.net - Pretrained Models](https://www.sbert.net/docs/pretrained_models.html)**
# sentence-transformers/stsb-bert-base
This is a [sentence-transformers]... | {"license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/stsb-bert-base | null | [
"sentence-transformers",
"pytorch",
"tf",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.10084"
] | [] | TAGS
#sentence-transformers #pytorch #tf #bert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us
|
️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: URL - Pretrained Models
# sentence-transformers/stsb-bert-base
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense ... | [
"# sentence-transformers/stsb-bert-base\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-transfo... | [
"TAGS\n#sentence-transformers #pytorch #tf #bert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# sentence-transformers/stsb-bert-base\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional ... |
sentence-similarity | sentence-transformers |
**⚠️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: [SBERT.net - Pretrained Models](https://www.sbert.net/docs/pretrained_models.html)**
# sentence-transformers/stsb-bert-large
This is a [sentence-transformers... | {"license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/stsb-bert-large | null | [
"sentence-transformers",
"pytorch",
"tf",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.10084"
] | [] | TAGS
#sentence-transformers #pytorch #tf #bert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: URL - Pretrained Models
# sentence-transformers/stsb-bert-large
This is a sentence-transformers model: It maps sentences & paragraphs to a 1024 dimensional dens... | [
"# sentence-transformers/stsb-bert-large\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 1024 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-trans... | [
"TAGS\n#sentence-transformers #pytorch #tf #bert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# sentence-transformers/stsb-bert-large\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 1024... |
sentence-similarity | sentence-transformers |
**⚠️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: [SBERT.net - Pretrained Models](https://www.sbert.net/docs/pretrained_models.html)**
# sentence-transformers/stsb-distilbert-base
This is a [sentence-transfo... | {"license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/stsb-distilbert-base | null | [
"sentence-transformers",
"pytorch",
"tf",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.10084"
] | [] | TAGS
#sentence-transformers #pytorch #tf #distilbert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: URL - Pretrained Models
# sentence-transformers/stsb-distilbert-base
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional ... | [
"# sentence-transformers/stsb-distilbert-base\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-t... | [
"TAGS\n#sentence-transformers #pytorch #tf #distilbert #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# sentence-transformers/stsb-distilbert-base\n\nThis is a sentence-transformers model: It maps sentences & paragraph... |
sentence-similarity | sentence-transformers |
# sentence-transformers/stsb-distilroberta-base-v2
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.
## Usage (Sentence-Transformers)
Using this model becomes easy whe... | {"license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/stsb-distilroberta-base-v2 | null | [
"sentence-transformers",
"pytorch",
"tf",
"jax",
"roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.10084"
] | [] | TAGS
#sentence-transformers #pytorch #tf #jax #roberta #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# sentence-transformers/stsb-distilroberta-base-v2
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-trans... | [
"# sentence-transformers/stsb-distilroberta-base-v2\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 sent... | [
"TAGS\n#sentence-transformers #pytorch #tf #jax #roberta #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# sentence-transformers/stsb-distilroberta-base-v2\n\nThis is a sentence-transformers model: It maps sentences & p... |
sentence-similarity | sentence-transformers |
# sentence-transformers/stsb-mpnet-base-v2
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.
## Usage (Sentence-Transformers)
Using this model becomes easy when you ha... | {"license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/stsb-mpnet-base-v2 | null | [
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.10084"
] | [] | TAGS
#sentence-transformers #pytorch #mpnet #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# sentence-transformers/stsb-mpnet-base-v2
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 ... | [
"# sentence-transformers/stsb-mpnet-base-v2\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-tra... | [
"TAGS\n#sentence-transformers #pytorch #mpnet #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# sentence-transformers/stsb-mpnet-base-v2\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 ... |
sentence-similarity | sentence-transformers |
# sentence-transformers/stsb-roberta-base-v2
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.
## Usage (Sentence-Transformers)
Using this model becomes easy when you ... | {"license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/stsb-roberta-base-v2 | null | [
"sentence-transformers",
"pytorch",
"tf",
"jax",
"roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.10084"
] | [] | TAGS
#sentence-transformers #pytorch #tf #jax #roberta #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# sentence-transformers/stsb-roberta-base-v2
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-transformer... | [
"# sentence-transformers/stsb-roberta-base-v2\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-t... | [
"TAGS\n#sentence-transformers #pytorch #tf #jax #roberta #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# sentence-transformers/stsb-roberta-base-v2\n\nThis is a sentence-transformers model: It maps sentences & paragra... |
sentence-similarity | sentence-transformers |
**⚠️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: [SBERT.net - Pretrained Models](https://www.sbert.net/docs/pretrained_models.html)**
# sentence-transformers/stsb-roberta-base
This is a [sentence-transforme... | {"license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/stsb-roberta-base | null | [
"sentence-transformers",
"pytorch",
"tf",
"roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.10084"
] | [] | TAGS
#sentence-transformers #pytorch #tf #roberta #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us
|
️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: URL - Pretrained Models
# sentence-transformers/stsb-roberta-base
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional den... | [
"# sentence-transformers/stsb-roberta-base\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-tran... | [
"TAGS\n#sentence-transformers #pytorch #tf #roberta #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# sentence-transformers/stsb-roberta-base\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimens... |
sentence-similarity | sentence-transformers |
**⚠️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: [SBERT.net - Pretrained Models](https://www.sbert.net/docs/pretrained_models.html)**
# sentence-transformers/stsb-roberta-large
This is a [sentence-transform... | {"license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/stsb-roberta-large | null | [
"sentence-transformers",
"pytorch",
"tf",
"jax",
"roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.10084"
] | [] | TAGS
#sentence-transformers #pytorch #tf #jax #roberta #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: URL - Pretrained Models
# sentence-transformers/stsb-roberta-large
This is a sentence-transformers model: It maps sentences & paragraphs to a 1024 dimensional d... | [
"# sentence-transformers/stsb-roberta-large\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 1024 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-tr... | [
"TAGS\n#sentence-transformers #pytorch #tf #jax #roberta #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# sentence-transformers/stsb-roberta-large\n\nThis is a sentence-transformers model: It maps sentences & paragraph... |
sentence-similarity | sentence-transformers |
# sentence-transformers/stsb-xlm-r-multilingual
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.
## Usage (Sentence-Transformers)
Using this model becomes easy when y... | {"license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/stsb-xlm-r-multilingual | null | [
"sentence-transformers",
"pytorch",
"tf",
"xlm-roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.10084"
] | [] | TAGS
#sentence-transformers #pytorch #tf #xlm-roberta #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# sentence-transformers/stsb-xlm-r-multilingual
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-transfor... | [
"# sentence-transformers/stsb-xlm-r-multilingual\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 sentenc... | [
"TAGS\n#sentence-transformers #pytorch #tf #xlm-roberta #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# sentence-transformers/stsb-xlm-r-multilingual\n\nThis is a sentence-transformers model: It maps sentences & parag... |
sentence-similarity | sentence-transformers |
**⚠️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: [SBERT.net - Pretrained Models](https://www.sbert.net/docs/pretrained_models.html)**
# sentence-transformers/xlm-r-100langs-bert-base-nli-mean-tokens
This is... | {"license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/xlm-r-100langs-bert-base-nli-mean-tokens | null | [
"sentence-transformers",
"pytorch",
"tf",
"xlm-roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.10084"
] | [] | TAGS
#sentence-transformers #pytorch #tf #xlm-roberta #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us
|
️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: URL - Pretrained Models
# sentence-transformers/xlm-r-100langs-bert-base-nli-mean-tokens
This is a sentence-transformers model: It maps sentences & paragraphs t... | [
"# sentence-transformers/xlm-r-100langs-bert-base-nli-mean-tokens\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... | [
"TAGS\n#sentence-transformers #pytorch #tf #xlm-roberta #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# sentence-transformers/xlm-r-100langs-bert-base-nli-mean-tokens\n\nThis is a sentence-transformers model: It maps sentences &... |
sentence-similarity | sentence-transformers |
**⚠️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: [SBERT.net - Pretrained Models](https://www.sbert.net/docs/pretrained_models.html)**
# sentence-transformers/xlm-r-100langs-bert-base-nli-stsb-mean-tokens
Th... | {"license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/xlm-r-100langs-bert-base-nli-stsb-mean-tokens | null | [
"sentence-transformers",
"pytorch",
"tf",
"xlm-roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.10084"
] | [] | TAGS
#sentence-transformers #pytorch #tf #xlm-roberta #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: URL - Pretrained Models
# sentence-transformers/xlm-r-100langs-bert-base-nli-stsb-mean-tokens
This is a sentence-transformers model: It maps sentences & paragra... | [
"# sentence-transformers/xlm-r-100langs-bert-base-nli-stsb-mean-tokens\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... | [
"TAGS\n#sentence-transformers #pytorch #tf #xlm-roberta #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# sentence-transformers/xlm-r-100langs-bert-base-nli-stsb-mean-tokens\n\nThis is a sentence-transformers model: It ... |
sentence-similarity | sentence-transformers |
**⚠️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: [SBERT.net - Pretrained Models](https://www.sbert.net/docs/pretrained_models.html)**
# sentence-transformers/xlm-r-base-en-ko-nli-ststb
This is a [sentence-t... | {"license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/xlm-r-base-en-ko-nli-ststb | null | [
"sentence-transformers",
"pytorch",
"tf",
"xlm-roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.10084"
] | [] | TAGS
#sentence-transformers #pytorch #tf #xlm-roberta #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us
|
️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: URL - Pretrained Models
# sentence-transformers/xlm-r-base-en-ko-nli-ststb
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimens... | [
"# sentence-transformers/xlm-r-base-en-ko-nli-ststb\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 sent... | [
"TAGS\n#sentence-transformers #pytorch #tf #xlm-roberta #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# sentence-transformers/xlm-r-base-en-ko-nli-ststb\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to... |
sentence-similarity | sentence-transformers |
**⚠️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: [SBERT.net - Pretrained Models](https://www.sbert.net/docs/pretrained_models.html)**
# sentence-transformers/xlm-r-bert-base-nli-mean-tokens
This is a [sente... | {"license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/xlm-r-bert-base-nli-mean-tokens | null | [
"sentence-transformers",
"pytorch",
"tf",
"xlm-roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.10084"
] | [] | TAGS
#sentence-transformers #pytorch #tf #xlm-roberta #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us
|
️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: URL - Pretrained Models
# sentence-transformers/xlm-r-bert-base-nli-mean-tokens
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 d... | [
"# sentence-transformers/xlm-r-bert-base-nli-mean-tokens\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... | [
"TAGS\n#sentence-transformers #pytorch #tf #xlm-roberta #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# sentence-transformers/xlm-r-bert-base-nli-mean-tokens\n\nThis is a sentence-transformers model: It maps sentences & paragrap... |
sentence-similarity | sentence-transformers |
**⚠️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: [SBERT.net - Pretrained Models](https://www.sbert.net/docs/pretrained_models.html)**
# sentence-transformers/xlm-r-bert-base-nli-stsb-mean-tokens
This is a [... | {"license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/xlm-r-bert-base-nli-stsb-mean-tokens | null | [
"sentence-transformers",
"pytorch",
"tf",
"xlm-roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.10084"
] | [] | TAGS
#sentence-transformers #pytorch #tf #xlm-roberta #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us
|
️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: URL - Pretrained Models
# sentence-transformers/xlm-r-bert-base-nli-stsb-mean-tokens
This is a sentence-transformers model: It maps sentences & paragraphs to a ... | [
"# sentence-transformers/xlm-r-bert-base-nli-stsb-mean-tokens\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... | [
"TAGS\n#sentence-transformers #pytorch #tf #xlm-roberta #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# sentence-transformers/xlm-r-bert-base-nli-stsb-mean-tokens\n\nThis is a sentence-transformers model: It maps sentences & par... |
sentence-similarity | sentence-transformers |
# sentence-transformers/xlm-r-distilroberta-base-paraphrase-v1
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.
## Usage (Sentence-Transformers)
Using this model beco... | {"license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/xlm-r-distilroberta-base-paraphrase-v1 | null | [
"sentence-transformers",
"pytorch",
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"xlm-roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.10084"
] | [] | TAGS
#sentence-transformers #pytorch #tf #xlm-roberta #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# sentence-transformers/xlm-r-distilroberta-base-paraphrase-v1
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 se... | [
"# sentence-transformers/xlm-r-distilroberta-base-paraphrase-v1\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 y... | [
"TAGS\n#sentence-transformers #pytorch #tf #xlm-roberta #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# sentence-transformers/xlm-r-distilroberta-base-paraphrase-v1\n\nThis is a sentence-transformers model: It maps se... |
sentence-similarity | sentence-transformers |
**⚠️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: [SBERT.net - Pretrained Models](https://www.sbert.net/docs/pretrained_models.html)**
# sentence-transformers/xlm-r-large-en-ko-nli-ststb
This is a [sentence-... | {"license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-transformers/xlm-r-large-en-ko-nli-ststb | null | [
"sentence-transformers",
"pytorch",
"tf",
"xlm-roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1908.10084"
] | [] | TAGS
#sentence-transformers #pytorch #tf #xlm-roberta #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us
|
️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: URL - Pretrained Models
# sentence-transformers/xlm-r-large-en-ko-nli-ststb
This is a sentence-transformers model: It maps sentences & paragraphs to a 1024 dime... | [
"# sentence-transformers/xlm-r-large-en-ko-nli-ststb\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 1024 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 se... | [
"TAGS\n#sentence-transformers #pytorch #tf #xlm-roberta #feature-extraction #sentence-similarity #transformers #arxiv-1908.10084 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# sentence-transformers/xlm-r-large-en-ko-nli-ststb\n\nThis is a sentence-transformers model: It maps sentences & paragraphs t... |
text-generation | transformers | dataset: Emotion Detection from Text | {} | seokho/gpt2-emotion | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| dataset: Emotion Detection from Text | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-classification | transformers | ### Model information
* language : Korean
* fine tuning data : [klue-tc (a.k.a. YNAT) ](https://klue-benchmark.com/tasks/66/overview/description)
* License : CC-BY-SA 4.0
* Base model : [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased)
* input : news headline
* output : top... | {} | seongju/klue-tc-bert-base-multilingual-cased | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| ### Model information
* language : Korean
* fine tuning data : klue-tc (a.k.a. YNAT)
* License : CC-BY-SA 4.0
* Base model : bert-base-multilingual-cased
* input : news headline
* output : topic
----
### Train information
* train_runtime: 1477.3876
* train_steps_per_second: 2.416
* train_loss: 0.3722... | [
"### Model information\n * language : Korean\n * fine tuning data : klue-tc (a.k.a. YNAT) \n * License : CC-BY-SA 4.0\n * Base model : bert-base-multilingual-cased\n * input : news headline\n * output : topic\n\n----",
"### Train information\n * train_runtime: 1477.3876 \n * train_steps_per_second: 2.416 \n... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"### Model information\n * language : Korean\n * fine tuning data : klue-tc (a.k.a. YNAT) \n * License : CC-BY-SA 4.0\n * Base model : bert-base-multilingual-cased\n * input : news headline\n ... |
text-classification | transformers | ### Model information
* language : Korean
* fine tuning data : [kor_3i4k](https://huggingface.co/datasets/kor_3i4k)
* License : CC-BY-SA 4.0
* Base model : [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased)
* input : sentence
* output : intent
----
### Train information
* ... | {} | seongju/kor-3i4k-bert-base-cased | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| ### Model information
* language : Korean
* fine tuning data : kor_3i4k
* License : CC-BY-SA 4.0
* Base model : bert-base-multilingual-cased
* input : sentence
* output : intent
----
### Train information
* train_runtime: 2376.638
* train_steps_per_second: 2.175
* train_loss: 0.356829648599977
* epoch:... | [
"### Model information\n * language : Korean\n * fine tuning data : kor_3i4k\n * License : CC-BY-SA 4.0\n * Base model : bert-base-multilingual-cased\n * input : sentence\n * output : intent\n\n----",
"### Train information\n * train_runtime: 2376.638\n * train_steps_per_second: 2.175\n * train_loss: 0.3568... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"### Model information\n * language : Korean\n * fine tuning data : kor_3i4k\n * License : CC-BY-SA 4.0\n * Base model : bert-base-multilingual-cased\n * input : sentence\n * output : intent\... |
question-answering | transformers | ### Model information
* language : English
* fine tuning data : [squad 2.0](https://rajpurkar.github.io/SQuAD-explorer/)
* License : CC-BY-SA 4.0
* Base model : [xlm-roberta-base](https://huggingface.co/xlm-roberta-base)
* input : question, context
* output : answer
----
### Train information
* train_runt... | {} | seongju/squadv2-xlm-roberta-base | null | [
"transformers",
"pytorch",
"xlm-roberta",
"question-answering",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #question-answering #endpoints_compatible #region-us
| ### Model information
* language : English
* fine tuning data : squad 2.0
* License : CC-BY-SA 4.0
* Base model : xlm-roberta-base
* input : question, context
* output : answer
----
### Train information
* train_runtime : 7562.859
* train_steps_per_second : 1.077
* training_loss : 0.9661213896603117
*... | [
"### Model information\n * language : English\n * fine tuning data : squad 2.0\n * License : CC-BY-SA 4.0\n * Base model : xlm-roberta-base\n * input : question, context\n * output : answer\n\n----",
"### Train information\n * train_runtime : 7562.859 \n * train_steps_per_second : 1.077\n * training_loss : ... | [
"TAGS\n#transformers #pytorch #xlm-roberta #question-answering #endpoints_compatible #region-us \n",
"### Model information\n * language : English\n * fine tuning data : squad 2.0\n * License : CC-BY-SA 4.0\n * Base model : xlm-roberta-base\n * input : question, context\n * output : answer\n\n----",
"### ... |
text-classification | transformers | # INTERPRESS NEWS CLASSIFICATION
## Dataset
The dataset downloaded from interpress. This dataset is real world data. Actually there are 273K data but I filtered them and used 108K data for this model. For more information about dataset please visit this [link](https://huggingface.co/datasets/interpress_news_category_tr... | {"language": "tr", "Dataset": "interpress_news_category_tr"} | serdarakyol/interpress-turkish-news-classification | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"text-classification",
"tr",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #tf #jax #bert #text-classification #tr #autotrain_compatible #endpoints_compatible #has_space #region-us
| # INTERPRESS NEWS CLASSIFICATION
## Dataset
The dataset downloaded from interpress. This dataset is real world data. Actually there are 273K data but I filtered them and used 108K data for this model. For more information about dataset please visit this link
## Model
Model accuracy on train data and validation data is... | [
"# INTERPRESS NEWS CLASSIFICATION",
"## Dataset\nThe dataset downloaded from interpress. This dataset is real world data. Actually there are 273K data but I filtered them and used 108K data for this model. For more information about dataset please visit this link",
"## Model\nModel accuracy on train data and va... | [
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"# INTERPRESS NEWS CLASSIFICATION",
"## Dataset\nThe dataset downloaded from interpress. This dataset is real world data. Actually there are 273K data but I filtered them ... |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 14722565
## Validation Metrics
- Loss: 0.6077525615692139
- Accuracy: 0.7745398773006135
- Macro F1: 0.7287152925396537
- Micro F1: 0.7745398773006135
- Weighted F1: 0.7754701717098939
- Macro Precision: 0.7282186282186283
- Micro ... | {"language": "en", "tags": "autonlp", "datasets": ["serenay/autonlp-data-Emotion"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}]} | serenay/autonlp-Emotion-14722565 | null | [
"transformers",
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"text-classification",
"autonlp",
"en",
"dataset:serenay/autonlp-data-Emotion",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #autonlp #en #dataset-serenay/autonlp-data-Emotion #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 14722565
## Validation Metrics
- Loss: 0.6077525615692139
- Accuracy: 0.7745398773006135
- Macro F1: 0.7287152925396537
- Micro F1: 0.7745398773006135
- Weighted F1: 0.7754701717098939
- Macro Precision: 0.7282186282186283
- Micro ... | [
"# Model Trained Using AutoNLP\n\n- Problem type: Multi-class Classification\n- Model ID: 14722565",
"## Validation Metrics\n\n- Loss: 0.6077525615692139\n- Accuracy: 0.7745398773006135\n- Macro F1: 0.7287152925396537\n- Micro F1: 0.7745398773006135\n- Weighted F1: 0.7754701717098939\n- Macro Precision: 0.7282186... | [
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"# Model Trained Using AutoNLP\n\n- Problem type: Multi-class Classification\n- Model ID: 14722565",
"## Validation Metrics\n\n- Loss: 0.6077525... |
text-generation | transformers |
## Model description
Fine-tuning facebook/blenderbot-400M-distill on subtitles rick and morty | {"language": ["en"], "license": "apache-2.0", "tags": ["conversational"], "datasets": ["rick_and_morty"], "metrics": ["perplexity"]} | sergunow/movie-chat | null | [
"transformers",
"pytorch",
"blenderbot",
"text2text-generation",
"conversational",
"en",
"dataset:rick_and_morty",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #blenderbot #text2text-generation #conversational #en #dataset-rick_and_morty #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
## Model description
Fine-tuning facebook/blenderbot-400M-distill on subtitles rick and morty | [
"## Model description\nFine-tuning facebook/blenderbot-400M-distill on subtitles rick and morty"
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"## Model description\nFine-tuning facebook/blenderbot-400M-distill on subtitles rick and morty"
] |
text-generation | transformers |
# Harry Potter DialogGPT Model | {"tags": ["conversational"]} | setiadia/DialogGPT-small-HPBot | null | [
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"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
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|
# Harry Potter DialogGPT Model | [
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] |
sentence-similarity | transformers |
# LaBSE
## Model description
Language-agnostic BERT Sentence Encoder (LaBSE) is a BERT-based model trained for sentence embedding for 109 languages. The pre-training process combines masked language modeling with translation language modeling. The model is useful for getting multilingual sentence embeddings and for ... | {"language": ["af", "am", "ar", "as", "az", "be", "bg", "bn", "bo", "bs", "ca", "ceb", "co", "cs", "cy", "da", "de", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "haw", "he", "hi", "hmn", "hr", "ht", "hu", "hy", "id", "ig", "is", "it", "ja", "jv", "ka", "kk", "km", "kn", "ko... | setu4993/LaBSE | null | [
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"... | null | 2022-03-02T23:29:05+00:00 | [
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"... | TAGS
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# LaBSE
## Model description
Language-agnostic BERT Sentence Encoder (LaBSE) is a BERT-based model trained for sentence embedding for 109 languages. The pre-training process combines masked language modeling with translation language modeling. The model is useful for getting multilingual sentence embeddings and for ... | [
"# LaBSE",
"## Model description\n\nLanguage-agnostic BERT Sentence Encoder (LaBSE) is a BERT-based model trained for sentence embedding for 109 languages. The pre-training process combines masked language modeling with translation language modeling. The model is useful for getting multilingual sentence embedding... | [
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sentence-similarity | transformers |
# LaBSE
## Model description
Smaller Language-agnostic BERT Sentence Encoder (LaBSE) is a BERT-based model distilled from the [original LaBSE model](https://huggingface.co/setu4993/LaBSE) to 15 languages (from the original 109 languages) using the techniques described in the paper ['Load What You Need: Smaller Versi... | {"language": ["ar", "de", "en", "es", "fr", "it", "ja", "ko", "nl", "pl", "pt", "ru", "th", "tr", "zh"], "license": "apache-2.0", "tags": ["bert", "sentence_embedding", "multilingual", "google", "sentence-similarity", "labse"], "datasets": ["CommonCrawl", "Wikipedia"], "pipeline_tag": "sentence-similarity"} | setu4993/smaller-LaBSE | null | [
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# LaBSE
## Model description
Smaller Language-agnostic BERT Sentence Encoder (LaBSE) is a BERT-based model distilled from the original LaBSE model to 15 languages (from the original 109 languages) using the techniques described in the paper 'Load What You Need: Smaller Versions of Multilingual BERT' by Ukjae Jeong.
... | [
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"## Model description\n\nSmaller Language-agnostic BERT Sentence Encoder (LaBSE) is a BERT-based model distilled from the original LaBSE model to 15 languages (from the original 109 languages) using the techniques described in the paper 'Load What You Need: Smaller Versions of Multilingual BERT' by Ukj... | [
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null | null | https://maccaboard.paulmccartney.com/users/watch-shang-chi-2021-full-movie-watch-online-download-hdrip
https://maccaboard.paulmccartney.com/users/watch-shang-chi-2021-online-full-free-download
https://maccaboard.paulmccartney.com/users/watch-shang-chi-2021-full-movie-download-hd
https://maccaboard.paulmccartney.com/use... | {} | sevbqewre/hyou | null | [
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token-classification | transformers |
# German BERT for literary texts
This German BERT is based on `bert-base-german-dbmdz-cased`, and has been adapted to the domain of literary texts by fine-tuning the language modeling task on the [Corpus of German-Language Fiction](https://figshare.com/articles/Corpus_of_German-Language_Fiction_txt_/4524680/1). After... | {"language": "de", "thumbnail": "https://huggingface.co/severinsimmler/literary-german-bert/raw/main/kfold.png"} | severinsimmler/literary-german-bert | null | [
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"token-classification",
"de",
"autotrain_compatible",
"endpoints_compatible",
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] | null | 2022-03-02T23:29:05+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #jax #safetensors #bert #token-classification #de #autotrain_compatible #endpoints_compatible #region-us
| German BERT for literary texts
==============================
This German BERT is based on 'bert-base-german-dbmdz-cased', and has been adapted to the domain of literary texts by fine-tuning the language modeling task on the Corpus of German-Language Fiction. Afterwards the model was fine-tuned for named entity recog... | [
"### Results\n\n\nAfter one epoch:\n\n\n\nNamed entity recognition\n------------------------\n\n\nThe provided model was also fine-tuned for two epochs on 10,799 sentences for training, validated on 547 and tested on 1,845 with three labels: 'B-PER', 'I-PER' and 'O'.\n\n\nResults\n-------\n\n\n\nThe model has also ... | [
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text-classification | transformers |
# Model Trained Using AutoNLP
_debug - I want to update this model_
- Problem type: Binary Classification
- Model ID: 1781580
## Validation Metrics
- Loss: 0.16026505827903748
- Accuracy: 0.9426
- Precision: 0.9305057745917961
- Recall: 0.95406288280931
- AUC: 0.9861051024994563
- F1: 0.9421370967741935
## Usage
... | {"language": "en", "tags": "autonlp", "datasets": ["severo/autonlp-data-sentiment_detection-3c8bcd36"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}]} | severo/autonlp-sentiment_detection-1781580 | null | [
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|
# Model Trained Using AutoNLP
_debug - I want to update this model_
- Problem type: Binary Classification
- Model ID: 1781580
## Validation Metrics
- Loss: 0.16026505827903748
- Accuracy: 0.9426
- Precision: 0.9305057745917961
- Recall: 0.95406288280931
- AUC: 0.9861051024994563
- F1: 0.9421370967741935
## Usage
... | [
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text2text-generation | transformers | Dummy T5 Test | {} | severo/dummy-t5-test | null | [
"transformers",
"tensorboard",
"t5",
"text2text-generation",
"autotrain_compatible",
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"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #tensorboard #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Dummy T5 Test | [] | [
"TAGS\n#transformers #tensorboard #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
null | null | I'm just testing this hugging face thing out, wish me luck! | {} | seyia92coding/recommender-demo | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| I'm just testing this hugging face thing out, wish me luck! | [] | [
"TAGS\n#region-us \n"
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fill-mask | transformers |
# ChemBERTa: Training a BERT-like transformer model for masked language modelling of chemical SMILES strings.
Deep learning for chemistry and materials science remains a novel field with lots of potiential. However, the popularity of transfer learning based methods in areas such as NLP and computer vision have not ye... | {"tags": ["chemistry"]} | seyonec/ChemBERTa-zinc-base-v1 | null | [
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"jax",
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"fill-mask",
"chemistry",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
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] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #roberta #fill-mask #chemistry #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# ChemBERTa: Training a BERT-like transformer model for masked language modelling of chemical SMILES strings.
Deep learning for chemistry and materials science remains a novel field with lots of potiential. However, the popularity of transfer learning based methods in areas such as NLP and computer vision have not ye... | [
"# ChemBERTa: Training a BERT-like transformer model for masked language modelling of chemical SMILES strings.\n\nDeep learning for chemistry and materials science remains a novel field with lots of potiential. However, the popularity of transfer learning based methods in areas such as NLP and computer vision have ... | [
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null | null | # HateSpeechDetection
---
pipeline_tag: text-classification
---
The model is used for classifying a text as Hatespeech or Normal. The model is trained using data from Twitter, specifically Kenyan related tweets. To maximize on the limited dataset, text augmentation was done.
The dataset is available here: https://git... | {} | sgich/bert_case_uncased_KenyaHateSpeech | null | [
"pytorch",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#pytorch #region-us
| # HateSpeechDetection
---
pipeline_tag: text-classification
---
The model is used for classifying a text as Hatespeech or Normal. The model is trained using data from Twitter, specifically Kenyan related tweets. To maximize on the limited dataset, text augmentation was done.
The dataset is available here: URL
Using ... | [
"# HateSpeechDetection\n---\npipeline_tag: text-classification\n---\n\nThe model is used for classifying a text as Hatespeech or Normal. The model is trained using data from Twitter, specifically Kenyan related tweets. To maximize on the limited dataset, text augmentation was done.\n\nThe dataset is available here:... | [
"TAGS\n#pytorch #region-us \n",
"# HateSpeechDetection\n---\npipeline_tag: text-classification\n---\n\nThe model is used for classifying a text as Hatespeech or Normal. The model is trained using data from Twitter, specifically Kenyan related tweets. To maximize on the limited dataset, text augmentation was done.... |
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. -->
# bert-fine-tuned-cola
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the glue... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "bert-fine-tuned-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "cola"}, "met... | sgugger/bert-fine-tuned-cola | null | [
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| bert-fine-tuned-cola
====================
This model is a fine-tuned version of bert-base-cased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8068
* Matthews Correlation: 0.5959
Model description
-----------------
More information needed
Intended uses & limitations
--... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rat... |
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. -->
# bert-finetuned-mrpc
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the GLUE ... | {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "bert-finetuned-mrpc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE MRPC", "type": "glue", "ar... | sgugger/bert-finetuned-mrpc | null | [
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| bert-finetuned-mrpc
===================
This model is a fine-tuned version of bert-base-cased on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5152
* Accuracy: 0.8603
* F1: 0.9032
* Combined Score: 0.8818
Model description
-----------------
More information needed
I... | [
"### 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* distributed\\_type: multi-GPU\n* num\\_devices: 2\n* total\\_train\\_batch\\_size: 16\n* total\\_eval\\_batch\\_size: 16\n* op... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during tra... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "distilbert-base-uncased-finetuned-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "ar... | sgugger/distilbert-base-uncased-finetuned-cola | null | [
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] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-cola
======================================
This model is a fine-tuned version of distilbert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7572
* Matthews Correlation: 0.5159
Model description
-----------------
More informa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
fill-mask | 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. -->
# esberto-small
This model is a fine-tuned version of [](https://huggingface.co/) on the oscar dataset.
## Model description
Mor... | {"tags": ["generated_from_trainer"], "datasets": ["oscar"], "model_index": [{"name": "esberto-small", "results": [{"task": {"name": "Masked Language Modeling", "type": "fill-mask"}, "dataset": {"name": "oscar", "type": "oscar", "args": "unshuffled_original_eo"}}]}]} | sgugger/esberto-small | null | [
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"fill-mask",
"generated_from_trainer",
"dataset:oscar",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #dataset-oscar #autotrain_compatible #endpoints_compatible #region-us
|
# esberto-small
This model is a fine-tuned version of [](URL on the oscar dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyp... | [
"# esberto-small\n\nThis model is a fine-tuned version of [](URL on the oscar dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hype... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #dataset-oscar #autotrain_compatible #endpoints_compatible #region-us \n",
"# esberto-small\n\nThis model is a fine-tuned version of [](URL on the oscar dataset.",
"## Model description\n\nMore information needed",
"## Inte... |
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. -->
# finetuned-bert-mrpc
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the glue ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy", "f1"], "model_index": [{"name": "finetuned-bert-mrpc", "results": [{"task": {"name": "Text Classification", "type": "text-classification"}, "dataset": {"name": "glue", "type": "glue", "args": "mrpc"}, "metric": {... | sgugger/finetuned-bert-mrpc | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| finetuned-bert-mrpc
===================
This model is a fine-tuned version of bert-base-cased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4917
* Accuracy: 0.8235
* F1: 0.8792
Model description
-----------------
More information needed
Intended uses & limitations
---... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* t... |
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. -->
# finetuned-bert
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the glue datas... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy", "f1"], "model_index": [{"name": "finetuned-bert", "results": [{"task": {"name": "Text Classification", "type": "text-classification"}, "dataset": {"name": "glue", "type": "glue", "args": "mrpc"}, "metric": {"name... | sgugger/finetuned-bert | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| finetuned-bert
==============
This model is a fine-tuned version of bert-base-cased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3916
* Accuracy: 0.875
* F1: 0.9125
Model description
-----------------
More information needed
Intended uses & limitations
--------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Trai... | [
"TAGS\n#transformers #pytorch #safetensors #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* t... |
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. -->
# glue-mrpc
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the GLUE MRPC datas... | {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "glue-mrpc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE MRPC", "type": "glue", "args": "mrpc... | sgugger/glue-mrpc | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# glue-mrpc
This model is a fine-tuned version of bert-base-cased on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6566
- Accuracy: 0.8554
- F1: 0.8974
- Combined Score: 0.8764
## Model description
More information needed
## Intended uses & limitations
More informatio... | [
"# glue-mrpc\n\nThis model is a fine-tuned version of bert-base-cased on the GLUE MRPC dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.6566\n- Accuracy: 0.8554\n- F1: 0.8974\n- Combined Score: 0.8764",
"## Model description\n\nMore information needed",
"## Intended uses & limitatio... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# glue-mrpc\n\nThis model is a fine-tuned version of bert-base-cased on the GLUE MRPC dataset.\nIt achieves ... |
translation | 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. -->
# marian-finetuned-kde4-en-to-fr
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsink... | {"license": "apache-2.0", "tags": ["translation", "generated_from_trainer"], "datasets": ["kde4"], "metrics": ["bleu"], "model-index": [{"name": "marian-finetuned-kde4-en-to-fr", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "kde4", "type": ... | sgugger/marian-finetuned-kde4-en-to-fr | null | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"dataset:kde4",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #marian #text2text-generation #translation #generated_from_trainer #dataset-kde4 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# marian-finetuned-kde4-en-to-fr
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-fr on the kde4 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8666
- Bleu: 53.2503
- Gen Len: 14.7005
## Model description
More information needed
## Intended uses & limitations
More infor... | [
"# marian-finetuned-kde4-en-to-fr\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-en-fr on the kde4 dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.8666\n- Bleu: 53.2503\n- Gen Len: 14.7005",
"## Model description\n\nMore information needed",
"## Intended uses & limit... | [
"TAGS\n#transformers #pytorch #tensorboard #marian #text2text-generation #translation #generated_from_trainer #dataset-kde4 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# marian-finetuned-kde4-en-to-fr\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-e... |
image-classification | timm |
# ResNet-50d
Pretrained model on [ImageNet](http://www.image-net.org/). The ResNet architecture was introduced in
[this paper](https://arxiv.org/abs/1512.03385) and is adapted with the ResNet-D trick from
[this paper](https://arxiv.org/abs/1812.01187)
## Model description
ResNet are deep convolutional neural netwo... | {"license": "apache-2.0", "tags": ["image-classification", "timm", "resnet"], "datasets": ["imagenet"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "ex... | sgugger/resnet50d | null | [
"timm",
"pytorch",
"image-classification",
"resnet",
"dataset:imagenet",
"arxiv:1512.03385",
"arxiv:1812.01187",
"arxiv:1906.02659",
"arxiv:2010.15052",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1512.03385",
"1812.01187",
"1906.02659",
"2010.15052"
] | [] | TAGS
#timm #pytorch #image-classification #resnet #dataset-imagenet #arxiv-1512.03385 #arxiv-1812.01187 #arxiv-1906.02659 #arxiv-2010.15052 #license-apache-2.0 #region-us
|
# ResNet-50d
Pretrained model on ImageNet. The ResNet architecture was introduced in
this paper and is adapted with the ResNet-D trick from
this paper
## Model description
ResNet are deep convolutional neural networks using residual connections. Each layer is composed of two convolutions
with a ReLU in the middle,... | [
"# ResNet-50d\n\nPretrained model on ImageNet. The ResNet architecture was introduced in\nthis paper and is adapted with the ResNet-D trick from\nthis paper",
"## Model description\n\nResNet are deep convolutional neural networks using residual connections. Each layer is composed of two convolutions\nwith a ReLU ... | [
"TAGS\n#timm #pytorch #image-classification #resnet #dataset-imagenet #arxiv-1512.03385 #arxiv-1812.01187 #arxiv-1906.02659 #arxiv-2010.15052 #license-apache-2.0 #region-us \n",
"# ResNet-50d\n\nPretrained model on ImageNet. The ResNet architecture was introduced in\nthis paper and is adapted with the ResNet-D tr... |
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. -->
# xlm-roberta-base-finetuned-marc-en-test-run
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["amazon_reviews_multi"], "model-index": [{"name": "xlm-roberta-base-finetuned-marc-en-test-run", "results": []}]} | shaer/xlm-roberta-base-finetuned-marc-en-test-run | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"dataset:amazon_reviews_multi",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #license-mit #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-marc-en-test-run
===========================================
This model is a fine-tuned version of xlm-roberta-base on the amazon\_reviews\_multi dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8957
* Mae: 0.4390
Model description
-----------------
More inf... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_... |
text-generation | transformers | # GPT2-Horoscopes
[](https://share.streamlit.io/shahp7575/gpt2-horoscopes-app/generate.py)
## Model Description
GPT2 fine-tuned on Horoscopes dataset scraped from [Horoscopes.com](https://www.horoscope.com/us/index.aspx). This mode... | {} | shahp7575/gpt2-horoscopes | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| # GPT2-Horoscopes
![Open in Streamlit](URL
## Model Description
GPT2 fine-tuned on Horoscopes dataset scraped from URL. This model generates horoscopes given a horoscope *category*.
## Uses & Limitations
### How to use
The model can be used directly with the HuggingFace 'pipeline' API.
### Generation
Input Text F... | [
"# GPT2-Horoscopes\n![Open in Streamlit](URL",
"## Model Description\nGPT2 fine-tuned on Horoscopes dataset scraped from URL. This model generates horoscopes given a horoscope *category*.",
"## Uses & Limitations",
"### How to use\nThe model can be used directly with the HuggingFace 'pipeline' API.",
"### G... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# GPT2-Horoscopes\n![Open in Streamlit](URL",
"## Model Description\nGPT2 fine-tuned on Horoscopes dataset scraped from URL. This model generates horoscopes giv... |
text-classification | transformers | # KEYWORD QUERY VS STATEMENT/QUESTION CLASSIFIER FOR NEURAL SEARCH
| Train Loss | Validation Acc.| Test Acc.|
| ------------- |:-------------: | -----: |
| 0.000806 | 0.99 | 0.997 |
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pret... | {"language": "en", "tags": ["neural-search-query-classification", "neural-search"], "widget": [{"text": "keyword query."}]} | shahrukhx01/bert-mini-finetune-question-detection | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"neural-search-query-classification",
"neural-search",
"en",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #bert #text-classification #neural-search-query-classification #neural-search #en #autotrain_compatible #endpoints_compatible #has_space #region-us
| KEYWORD QUERY VS STATEMENT/QUESTION CLASSIFIER FOR NEURAL SEARCH
================================================================
Trained to add feature for classifying queries between Keyword Query or Question + Statement Query using classification in Haystack
Problem Statement:
One common challenge that we saw i... | [] | [
"TAGS\n#transformers #pytorch #safetensors #bert #text-classification #neural-search-query-classification #neural-search #en #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
text-classification | transformers | # A Multi-task learning model with two prediction heads
* One prediction head classifies between keyword sentences vs statements/questions
* Other prediction head corresponds to classifier for statements vs questions
## Scores
##### Spaadia SQuaD Test acc: **0.9891**
##### Quora Keyword Pairs Test acc: **0.98048**
##... | {} | shahrukhx01/bert-multitask-query-classifiers | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #bert #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us
| # A Multi-task learning model with two prediction heads
* One prediction head classifies between keyword sentences vs statements/questions
* Other prediction head corresponds to classifier for statements vs questions
## Scores
##### Spaadia SQuaD Test acc: 0.9891
##### Quora Keyword Pairs Test acc: 0.98048
## Dataset... | [
"# A Multi-task learning model with two prediction heads\n* One prediction head classifies between keyword sentences vs statements/questions\n* Other prediction head corresponds to classifier for statements vs questions",
"## Scores",
"##### Spaadia SQuaD Test acc: 0.9891",
"##### Quora Keyword Pairs Test acc... | [
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text2text-generation | transformers | The distilbart-cnn-12-6-text2sql is fine-tuned on WIKISQL dataset.
```python
from transformers import BartTokenizer, BartForConditionalGeneration, BartConfig
model = BartForConditionalGeneration.from_pretrained('shahrukhx01/distilbart-cnn-12-6-text2sql')
tokenizer = BartTokenizer.from_pretrained('shahrukhx01/distilbar... | {} | shahrukhx01/distilbart-cnn-12-6-text2sql | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| The distilbart-cnn-12-6-text2sql is fine-tuned on WIKISQL dataset.
| [] | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
feature-extraction | transformers | A Siamese BERT architecture trained at character levels tokens for embedding based Fuzzy matching.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
`... | {"tags": ["fuzzy-matching", "fuzzy-search", "entity-resolution", "record-linking", "structured-data-search"]} | shahrukhx01/paraphrase-mpnet-base-v2-fuzzy-matcher | null | [
"transformers",
"pytorch",
"safetensors",
"mpnet",
"feature-extraction",
"fuzzy-matching",
"fuzzy-search",
"entity-resolution",
"record-linking",
"structured-data-search",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #mpnet #feature-extraction #fuzzy-matching #fuzzy-search #entity-resolution #record-linking #structured-data-search #endpoints_compatible #region-us
| A Siamese BERT architecture trained at character levels tokens for embedding based Fuzzy matching.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can use the model like this:
## Usage (HuggingFace Transformers)
## ACKNOWLEDGEMENT
A big thank ... | [
"## Usage (Sentence-Transformers)\nUsing this model becomes easy when you have sentence-transformers installed:\n\nThen you can use the model like this:",
"## Usage (HuggingFace Transformers)",
"## ACKNOWLEDGEMENT\nA big thank you to Sentence Transformers as their implementation really expedited the implementat... | [
"TAGS\n#transformers #pytorch #safetensors #mpnet #feature-extraction #fuzzy-matching #fuzzy-search #entity-resolution #record-linking #structured-data-search #endpoints_compatible #region-us \n",
"## Usage (Sentence-Transformers)\nUsing this model becomes easy when you have sentence-transformers installed:\n\nTh... |
text-classification | transformers | # KEYWORD STATEMENT VS QUESTION CLASSIFIER FOR NEURAL SEARCH
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("shahrukhx01/question-vs-statement-classifier")
model = AutoModelForSequenceClassification.from_pretrained("shahrukhx01/questi... | {"language": "en", "tags": ["neural-search-query-classification", "neural-search"], "widget": [{"text": "what did you eat in lunch?"}]} | shahrukhx01/question-vs-statement-classifier | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"neural-search-query-classification",
"neural-search",
"en",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #bert #text-classification #neural-search-query-classification #neural-search #en #autotrain_compatible #endpoints_compatible #has_space #region-us
| # KEYWORD STATEMENT VS QUESTION CLASSIFIER FOR NEURAL SEARCH
Trained to add the feature for classifying queries between Question Query vs Statement Query using classification in Haystack
| [
"# KEYWORD STATEMENT VS QUESTION CLASSIFIER FOR NEURAL SEARCH\n\n\n\nTrained to add the feature for classifying queries between Question Query vs Statement Query using classification in Haystack"
] | [
"TAGS\n#transformers #pytorch #safetensors #bert #text-classification #neural-search-query-classification #neural-search #en #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# KEYWORD STATEMENT VS QUESTION CLASSIFIER FOR NEURAL SEARCH\n\n\n\nTrained to add the feature for classifying querie... |
text-classification | transformers | # Labels Map
LABEL_0 => **"NO"** <br/>
LABEL_1 => **"YES"**
```python
from transformers import (
AutoModelForSequenceClassification,
AutoTokenizer,
)
model = AutoModelForSequenceClassification.from_pretrained("shahrukhx01/roberta-base-boolq")
model.to(device)
#model.push_to_hub("roberta-base-boolq")
tokeni... | {"language": "en", "tags": ["boolean-qa"], "widget": [{"text": "Is Berlin the smallest city of Germany? <s> Berlin is the capital and largest city of Germany by both area and population. Its 3.8 million inhabitants make it the European Union's most populous city, according to the population within city limits "}]} | shahrukhx01/roberta-base-boolq | null | [
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"safetensors",
"roberta",
"text-classification",
"boolean-qa",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #roberta #text-classification #boolean-qa #en #autotrain_compatible #endpoints_compatible #region-us
| # Labels Map
LABEL_0 => "NO" <br/>
LABEL_1 => "YES"
| [
"# Labels Map\nLABEL_0 => \"NO\" <br/>\nLABEL_1 => \"YES\""
] | [
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"# Labels Map\nLABEL_0 => \"NO\" <br/>\nLABEL_1 => \"YES\""
] |
question-answering | transformers | ## Multiple Prediction Heads
* ExtractiveQA Head
* Three Class Classification Head, classes => (yes, no, extra_qa) to answer binary questions or direct to ExtractiveQA Head
## BoolQ Validation dataset Evaluation: <br/>
support => 3270 <br/>
accuracy => 0.73 <br/>
macro f1 => 0.71
## SQuAD Validation dataset Evaluati... | {} | shahrukhx01/roberta-base-squad2-boolq-baseline | null | [
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] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #question-answering #endpoints_compatible #region-us
| ## Multiple Prediction Heads
* ExtractiveQA Head
* Three Class Classification Head, classes => (yes, no, extra_qa) to answer binary questions or direct to ExtractiveQA Head
## BoolQ Validation dataset Evaluation: <br/>
support => 3270 <br/>
accuracy => 0.73 <br/>
macro f1 => 0.71
## SQuAD Validation dataset Evaluati... | [
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"## SQuAD Validatio... | [
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"## Multiple Prediction Heads\n* ExtractiveQA Head \n* Three Class Classification Head, classes => (yes, no, extra_qa) to answer binary questions or direct to ExtractiveQA Head",
"## BoolQ Validation dataset Evaluat... |
fill-mask | transformers | # Dhivehi Roberta Base - Oscar
## Description
RoBERTA pretrained from scratch using Jax/Flax backend and with the Dhivehi Oscar Corpus only.
| {"language": "dv", "tags": ["dv", "roberta"], "widget": [{"text": "<mask> \u0789\u07a7\u078d\u07ac \u0787\u07a6\u0786\u07a9 \u078b\u07a8\u0788\u07ac\u0780\u07a8\u0783\u07a7\u0787\u07b0\u0796\u07ad\u078e\u07ac"}]} | shahukareem/dhivehi-roberta-base | null | [
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"dv"
] | TAGS
#transformers #pytorch #jax #tensorboard #roberta #fill-mask #dv #autotrain_compatible #endpoints_compatible #region-us
| # Dhivehi Roberta Base - Oscar
## Description
RoBERTA pretrained from scratch using Jax/Flax backend and with the Dhivehi Oscar Corpus only.
| [
"# Dhivehi Roberta Base - Oscar",
"## Description\nRoBERTA pretrained from scratch using Jax/Flax backend and with the Dhivehi Oscar Corpus only."
] | [
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"# Dhivehi Roberta Base - Oscar",
"## Description\nRoBERTA pretrained from scratch using Jax/Flax backend and with the Dhivehi Oscar Corpus only."
] |
automatic-speech-recognition | transformers | # Wav2Vec2-Large-XLSR-53-Dhivehi
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Dhivehi using the [Common Voice](https://huggingface.co/datasets/common_voice).
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be ... | {"language": "dv", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech"], "datasets": ["common_voice"], "metrics": ["wer"]} | shahukareem/wav2vec2-large-xlsr-53-dhivehi-v2 | null | [
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"automatic-speech-recognition",
"audio",
"speech",
"dv",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"dv"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #dv #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| # Wav2Vec2-Large-XLSR-53-Dhivehi
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Dhivehi using the Common Voice.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
## Evaluation
The model can be evaluated as follows ... | [
"# Wav2Vec2-Large-XLSR-53-Dhivehi\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Dhivehi using the Common Voice.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\nThe model can be eva... | [
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"# Wav2Vec2-Large-XLSR-53-Dhivehi\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Dhivehi using the Common Voice.\nWhen using this model, make sur... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Dhivehi
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Dhivehi using the [Common Voice](https://huggingface.co/datasets/common_voice).
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can... | {"language": "dv", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "Shahu Kareem XLSR Wav2Vec2 Large 53 Dhivehi", "results": [{"task": {"type": "automatic-speech-recognition", "name"... | shahukareem/wav2vec2-large-xlsr-53-dhivehi | null | [
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|
# Wav2Vec2-Large-XLSR-53-Dhivehi
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Dhivehi using the Common Voice.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
## Evaluation
The model can be evaluated as ... | [
"# Wav2Vec2-Large-XLSR-53-Dhivehi\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Dhivehi using the Common Voice.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model can ... | [
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"# Wav2Vec2-Large-XLSR-53-Dhivehi\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Dhivehi using t... |
automatic-speech-recognition | transformers | # wav2vec2-xls-r-1b-dv-with-lm
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the common_voice dataset. | {} | shahukareem/wav2vec2-xls-r-1b-dv-with-lm | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us
| # wav2vec2-xls-r-1b-dv-with-lm
This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the common_voice dataset. | [
"# wav2vec2-xls-r-1b-dv-with-lm\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the common_voice dataset."
] | [
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"# wav2vec2-xls-r-1b-dv-with-lm\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the common_voice dataset."
] |
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-xls-r-1b-dv
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2... | {"license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "dv", "robust-speech-event", "model_for_talk"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "wav2vec2-xls-r-1b-dv", "results": [{"task": {"type": "automat... | shahukareem/wav2vec2-xls-r-1b-dv | null | [
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"license:apache-2.0",
"model-index",
"endpoints_compatible",
... | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #dv #robust-speech-event #model_for_talk #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us
| wav2vec2-xls-r-1b-dv
====================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1702
* Wer: 0.2123
Model description
-----------------
More information needed
Intended uses & limitations... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 4.5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
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"### Training hyperparamet... |
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. -->
# xls-r-300m-dv
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-... | {"language": ["dv"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "dv", "generated_from_trainer", "hf-asr-leaderboard", "model_for_talk", "mozilla-foundation/common_voice_8_0", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "XLS-R-300M - Dhiveh... | shahukareem/xls-r-300m-dv | null | [
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"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"... | null | 2022-03-02T23:29:05+00:00 | [] | [
"dv"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #dv #generated_from_trainer #hf-asr-leaderboard #model_for_talk #mozilla-foundation/common_voice_8_0 #robust-speech-event #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us
| xls-r-300m-dv
=============
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2855
* Wer: 0.2665
Model description
-----------------
More information needed
Intended uses & limitations
-----------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #dv #generated_from_trainer #hf-asr-leaderboard #model_for_talk #mozilla-foundation/common_voice_8_0 #robust-speech-event #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### T... |
null | null |
# CoQUAD_MPNet : MPNet model for COVID-19
## Introduction
It is a state-of-the-art language model for MPNet for Covid-19 dataset with focus on post-covid.
## How to use for Deepset Haystack
```python
# Load data
from datasets import load_dataset
dataset = load_dataset("shaina/covid19")
# Haystack pipeline
!sudo ap... | {"language": "en", "license": "apache-2.0", "tags": ["MPNet"], "dataset": ["covid-19"]} | shaina/CoQUAD_MPNet | null | [
"MPNet",
"en",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#MPNet #en #license-apache-2.0 #region-us
|
# CoQUAD_MPNet : MPNet model for COVID-19
## Introduction
It is a state-of-the-art language model for MPNet for Covid-19 dataset with focus on post-covid.
## How to use for Deepset Haystack
---
## Authors
Shaina Raza
--- | [
"# CoQUAD_MPNet : MPNet model for COVID-19",
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"## How to use for Deepset Haystack\n\n\n---",
"## Authors \nShaina Raza\n\n ---"
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"## How to use for Deepset Haystack\n\n\n---",
"## Authors \nShaina Raza\n\n ---"
] |
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. -->
# covid_qa_distillBert
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-unca... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["covid_qa_deepset"], "widget": [{"text": "What is COVID-19?", "context": "Coronavirus disease 2019 (COVID-19) is a contagious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The first known case was identified in ... | shaina/covid_qa_distillBert | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:covid_qa_deepset",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-covid_qa_deepset #license-apache-2.0 #endpoints_compatible #region-us
| covid\_qa\_distillBert
======================
This model is a fine-tuned version of distilbert-base-uncased on the covid\_qa\_deepset dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0971
Model description
-----------------
More information needed
Intended uses & limitations
-------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-covid_qa_deepset #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\... |
question-answering | transformers | # covid_qa_mpnet
This model is a fine-tuned version of [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base) on our COVID-19 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1352
## Model description
More information needed
## Intended uses & limitations
More information ne... | {"tags": ["generated_from_trainer"], "widget": [{"text": "What is COVID-19?", "context": "Coronavirus disease 2019 (COVID-19) is a contagious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The first known case was identified in Wuhan, China, in December 2019.[7] The disease has since sp... | shaina/covid_qa_mpnet | null | [
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"pytorch",
"tensorboard",
"mpnet",
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] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #mpnet #question-answering #generated_from_trainer #endpoints_compatible #has_space #region-us
| covid\_qa\_mpnet
================
This model is a fine-tuned version of microsoft/mpnet-base on our COVID-19 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1352
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
Mo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #mpnet #question-answering #generated_from_trainer #endpoints_compatible #has_space #region-us \n",
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question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["covid_qa_deepset"], "metrics": ["squad_v2"], "widget": [{"text": "What is COVID-19?", "context": "Coronavirus disease 2019 (COVID-19) is a contagious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The first know... | shainahub/covid_qa_distillbert | null | [
"transformers",
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"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:covid_qa_deepset",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-covid_qa_deepset #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-squad
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the covid\_qa\_deepset dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0976
Model description
-----------------
More information needed
I... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-covid_qa_deepset #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\... |
summarization | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-small-finetuned-arxiv-cs-finetuned-arxiv-cs-full
This model is a fine-tuned version of [shamikbose89/mt5-small-finetuned-arx... | {"license": "apache-2.0", "tags": ["generated_from_trainer", "summarization"], "metrics": ["rouge"], "base_model": "shamikbose89/mt5-small-finetuned-arxiv-cs", "model-index": [{"name": "mt5-small-finetuned-arxiv-cs-finetuned-arxiv-cs-full", "results": []}]} | shamikbose89/mt5-small-finetuned-arxiv-cs-finetuned-arxiv-cs-full | null | [
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"tensorboard",
"safetensors",
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"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inf... | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #mt5 #text2text-generation #generated_from_trainer #summarization #base_model-shamikbose89/mt5-small-finetuned-arxiv-cs #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| mt5-small-finetuned-arxiv-cs-finetuned-arxiv-cs-full
====================================================
This model is a fine-tuned version of shamikbose89/mt5-small-finetuned-arxiv-cs on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.4037
* Rouge1: 39.8923
* Rouge2: 20.9831
*... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5.6e-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: 10",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #mt5 #text2text-generation #generated_from_trainer #summarization #base_model-shamikbose89/mt5-small-finetuned-arxiv-cs #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"### Training hyperp... |
summarization | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-small-finetuned-arxiv-cs
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) o... | {"license": "apache-2.0", "tags": ["generated_from_trainer", "summarization"], "metrics": ["rouge"], "model-index": [{"name": "mt5-small-finetuned-arxiv-cs", "results": []}]} | shamikbose89/mt5-small-finetuned-arxiv-cs | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"mt5",
"text2text-generation",
"generated_from_trainer",
"summarization",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #mt5 #text2text-generation #generated_from_trainer #summarization #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| mt5-small-finetuned-arxiv-cs
============================
This model is a fine-tuned version of google/mt5-small on a subset of the arxiv dataset.
It achieves the following results on the evaluation set:
* Loss: 1.6922
* Rouge1: 0.7734
* Rouge2: 0.2865
* Rougel: 0.6665
* Rougelsum: 0.6743
Model description
------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5.6e-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: 4",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #mt5 #text2text-generation #generated_from_trainer #summarization #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during tra... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-base-fine-tuned-for-Punctuation-Restoration
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) o... | {"license": "apache-2.0", "tags": ["generated_from_trainer"]} | shashank2123/t5-base-fine-tuned-for-Punctuation-Restoration | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-base-fine-tuned-for-Punctuation-Restoration
==============================================
This model is a fine-tuned version of t5-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1097
Model description
-----------------
More information needed
Intended uses &... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-finetuned-for-GEC
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on an unkown dataset.
It ac... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["bleu"], "model_index": [{"name": "t5-finetuned-for-GEC", "results": [{"task": {"name": "Sequence-to-sequence Language Modeling", "type": "text2text-generation"}, "metric": {"name": "Bleu", "type": "bleu", "value": 0.3571}}]}]} | shashank2123/t5-finetuned-for-GEC | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-finetuned-for-GEC
====================
This model is a fine-tuned version of t5-base on an unkown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3949
* Bleu: 0.3571
* Gen Len: 19.0
Model description
-----------------
More information needed
Intended uses & limitations
---------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* tr... |
text-generation | transformers |
# Ash DialoGPT Model | {"tags": ["conversational"]} | shelb-doc/DialoGPT-medium-ash | 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
|
# Ash DialoGPT Model | [
"# Ash DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Ash DialoGPT Model"
] |
null | null | Test for First Model | {} | shelly/bedrooms | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| Test for First Model | [] | [
"TAGS\n#region-us \n"
] |
text-generation | transformers |
# GPT2 for Code AutoComplete Model
code-autocomplete, a code completion plugin for Python.
**code-autocomplete** can automatically complete the code of lines and blocks with GPT2.
## Usage
Open source repo：[code-autocomplete](https://github.com/shibing624/code-autocomplete)，support GPT2 model, usage：
```... | {"language": ["en"], "license": "apache-2.0", "tags": ["code", "autocomplete", "pytorch", "en"]} | shibing624/code-autocomplete-distilgpt2-python | null | [
"transformers",
"pytorch",
"safetensors",
"gpt2",
"text-generation",
"code",
"autocomplete",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #gpt2 #text-generation #code #autocomplete #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# GPT2 for Code AutoComplete Model
code-autocomplete, a code completion plugin for Python.
code-autocomplete can automatically complete the code of lines and blocks with GPT2.
## Usage
Open source repo：code-autocomplete，support GPT2 model, usage：
Also, use huggingface/transformers：
*Please use 'GP... | [
"# GPT2 for Code AutoComplete Model\r\ncode-autocomplete, a code completion plugin for Python.\r\n\r\ncode-autocomplete can automatically complete the code of lines and blocks with GPT2.",
"## Usage\r\n\r\nOpen source repo：code-autocomplete，support GPT2 model, usage：\r\n\r\n\r\n\r\nAlso, use huggingface/transform... | [
"TAGS\n#transformers #pytorch #safetensors #gpt2 #text-generation #code #autocomplete #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# GPT2 for Code AutoComplete Model\r\ncode-autocomplete, a code completion plugin for Python.\r\n\r\ncode... |
text-generation | transformers |
# GPT2 for Code AutoComplete Model
code-autocomplete, a code completion plugin for Python.
**code-autocomplete** can automatically complete the code of lines and blocks with GPT2.
## Usage
Open source repo：[code-autocomplete](https://github.com/shibing624/code-autocomplete)，support GPT2 model, usage：
```... | {"language": ["en"], "license": "apache-2.0", "tags": ["code", "autocomplete", "pytorch", "en"]} | shibing624/code-autocomplete-gpt2-base | null | [
"transformers",
"pytorch",
"safetensors",
"gpt2",
"text-generation",
"code",
"autocomplete",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #gpt2 #text-generation #code #autocomplete #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# GPT2 for Code AutoComplete Model
code-autocomplete, a code completion plugin for Python.
code-autocomplete can automatically complete the code of lines and blocks with GPT2.
## Usage
Open source repo：code-autocomplete，support GPT2 model, usage：
Also, use huggingface/transformers：
*Please use 'GP... | [
"# GPT2 for Code AutoComplete Model\r\ncode-autocomplete, a code completion plugin for Python.\r\n\r\ncode-autocomplete can automatically complete the code of lines and blocks with GPT2.",
"## Usage\r\n\r\nOpen source repo：code-autocomplete，support GPT2 model, usage：\r\n\r\n\r\n\r\nAlso, use huggingface/transform... | [
"TAGS\n#transformers #pytorch #safetensors #gpt2 #text-generation #code #autocomplete #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# GPT2 for Code AutoComplete Model\r\ncode-autocomplete, a code completion plugin for Python.\r\n\r\ncode... |
fill-mask | transformers |
# MacBERT for Chinese Spelling Correction(macbert4csc) Model
中文拼写纠错模型
`macbert4csc-base-chinese` evaluate SIGHAN2015 test data：
- Char Level: precision:0.9372, recall:0.8640, f1:0.8991
- Sentence Level: precision:0.8264, recall:0.7366, f1:0.7789
由于训练使用的数据使用了SIGHAN2015的训练集（复现paper），在SIGHAN2015的测试集上达到SOTA水平。
模型结... | {"language": ["zh"], "license": "apache-2.0", "tags": ["bert", "pytorch", "zh"]} | shibing624/macbert4csc-base-chinese | null | [
"transformers",
"pytorch",
"onnx",
"safetensors",
"bert",
"fill-mask",
"zh",
"arxiv:2004.13922",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.13922"
] | [
"zh"
] | TAGS
#transformers #pytorch #onnx #safetensors #bert #fill-mask #zh #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| MacBERT for Chinese Spelling Correction(macbert4csc) Model
==========================================================
中文拼写纠错模型
'macbert4csc-base-chinese' evaluate SIGHAN2015 test data：
* Char Level: precision:0.9372, recall:0.8640, f1:0.8991
* Sentence Level: precision:0.8264, recall:0.7366, f1:0.7789
由于训练使用的数据... | [
"### 训练数据集",
"#### SIGHAN+Wang271K中文纠错数据集\n\n\n\nSIGHAN+Wang271K中文纠错数据集，数据格式：\n\n\n如果需要训练macbert4csc，请参考https://URL",
"### About MacBERT\n\n\nMacBERT is an improved BERT with novel MLM as correction pre-training task, which mitigates the discrepancy of pre-training and fine-tuning.\n\n\nHere is an example of ou... | [
"TAGS\n#transformers #pytorch #onnx #safetensors #bert #fill-mask #zh #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### 训练数据集",
"#### SIGHAN+Wang271K中文纠错数据集\n\n\n\nSIGHAN+Wang271K中文纠错数据集，数据格式：\n\n\n如果需要训练macbert4csc，请参考https://URL",
"### About Ma... |
sentence-similarity | transformers | # shibing624/text2vec-base-chinese
This is a CoSENT(Cosine Sentence) model: shibing624/text2vec-base-chinese.
It maps sentences to a 768 dimensional dense vector space and can be used for tasks
like sentence embeddings, text matching or semantic search.
## Evaluation
For an automated evaluation of this model, see t... | {"language": ["zh"], "license": "apache-2.0", "library_name": "transformers", "tags": ["text2vec", "feature-extraction", "sentence-similarity", "transformers"], "datasets": ["shibing624/nli_zh"], "metrics": ["spearmanr"], "pipeline_tag": "sentence-similarity"} | shibing624/text2vec-base-chinese | null | [
"transformers",
"pytorch",
"onnx",
"bert",
"feature-extraction",
"text2vec",
"sentence-similarity",
"zh",
"dataset:shibing624/nli_zh",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #onnx #bert #feature-extraction #text2vec #sentence-similarity #zh #dataset-shibing624/nli_zh #license-apache-2.0 #endpoints_compatible #has_space #region-us
| shibing624/text2vec-base-chinese
================================
This is a CoSENT(Cosine Sentence) model: shibing624/text2vec-base-chinese.
It maps sentences to a 768 dimensional dense vector space and can be used for tasks
like sentence embeddings, text matching or semantic search.
Evaluation
----------
For a... | [
"### Pre-training\n\n\nWe use the pretrained 'hfl/chinese-macbert-base' model.\nPlease refer to the model card for more detailed information about the pre-training procedure.",
"### Fine-tuning\n\n\nWe fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each\npossibl... | [
"TAGS\n#transformers #pytorch #onnx #bert #feature-extraction #text2vec #sentence-similarity #zh #dataset-shibing624/nli_zh #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"### Pre-training\n\n\nWe use the pretrained 'hfl/chinese-macbert-base' model.\nPlease refer to the model card for more d... |
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-large-xls-r-300m-pun-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-pun-colab", "results": []}]} | shibli/wav2vec2-large-xls-r-300m-pun-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"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 #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-large-xls-r-300m-pun-colab
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedu... | [
"# wav2vec2-large-xls-r-300m-pun-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information neede... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-large-xls-r-300m-pun-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voic... |
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-20sec-timit-and-dementiabank
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-20sec-timit-and-dementiabank", "results": []}]} | shields/wav2vec2-base-20sec-timit-and-dementiabank | 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-20sec-timit-and-dementiabank
==========================================
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: 0.4338
* Wer: 0.2313
Model description
-----------------
More information neede... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 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: 4... |
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-dementiabank
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2ve... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-dementiabank", "results": []}]} | shields/wav2vec2-base-dementiabank | 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-dementiabank
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 11.0473
- eval_wer: 1.0
- eval_runtime: 3.3353
- eval_samples_per_second: 2.399
- eval_steps_per_second: 0.3
- epoch: 3.12
- step: 200
... | [
"# wav2vec2-base-dementiabank\n\nThis model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 11.0473\n- eval_wer: 1.0\n- eval_runtime: 3.3353\n- eval_samples_per_second: 2.399\n- eval_steps_per_second: 0.3\n- epoch: 3.12\n... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-base-dementiabank\n\nThis model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.\nIt achieves the following results... |
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