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sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | GPL/nq-msmarco-distilbert-gpl | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-04-19T14:14:57+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can u... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\n... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\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... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | GPL/quora-msmarco-distilbert-gpl | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-04-19T14:15:16+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can u... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\n... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\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... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | GPL/signal1m-msmarco-distilbert-gpl | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-04-19T14:15:34+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can u... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\n... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\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... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | GPL/trec-news-msmarco-distilbert-gpl | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-04-19T14:15:52+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can u... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\n... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\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... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | GPL/webis-touche2020-msmarco-distilbert-gpl | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-04-19T14:16:11+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can u... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\n... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\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... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | GPL/scidocs-msmarco-distilbert-gpl | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-04-19T14:16:31+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can u... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\n... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\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... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | GPL/trec-covid-msmarco-distilbert-gpl | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-04-19T14:16:49+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can u... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\n... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\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... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | GPL/arguana-tsdae-msmarco-distilbert-gpl | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-04-19T14:20:03+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can u... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\n... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\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... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | GPL/climate-fever-tsdae-msmarco-distilbert-gpl | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-04-19T14:20:21+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can u... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\n... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\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... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | GPL/dbpedia-entity-tsdae-msmarco-distilbert-gpl | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-04-19T14:22:12+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can u... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\n... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\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... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | GPL/fever-tsdae-msmarco-distilbert-gpl | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-04-19T14:22:46+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can u... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\n... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\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... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | GPL/hotpotqa-tsdae-msmarco-distilbert-gpl | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-04-19T14:24:10+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can u... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\n... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\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... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | GPL/nfcorpus-tsdae-msmarco-distilbert-gpl | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-04-19T14:25:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can u... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\n... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\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... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | GPL/quora-tsdae-msmarco-distilbert-gpl | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-04-19T14:25:52+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can u... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\n... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\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... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | GPL/trec-news-tsdae-msmarco-distilbert-gpl | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-04-19T14:26:42+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can u... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\n... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\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... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | GPL/scidocs-tsdae-msmarco-distilbert-gpl | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-04-19T14:27:29+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can u... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\n... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\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... |
image-segmentation | 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. -->
# segformer-trainer-test
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the segmen... | {"license": "apache-2.0", "tags": ["image-segmentation", "vision", "generated_from_trainer"], "widget": [{"src": "https://segmentsai-prod.s3.eu-west-2.amazonaws.com/assets/admin-tobias/439f6843-80c5-47ce-9b17-0b2a1d54dbeb.jpg", "example_title": "Brugge"}], "base_model": "nvidia/mit-b0", "model-index": [{"name": "segfor... | nielsr/segformer-trainer-test | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"segformer",
"image-segmentation",
"vision",
"generated_from_trainer",
"base_model:nvidia/mit-b0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-19T14:35:14+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #segformer #image-segmentation #vision #generated_from_trainer #base_model-nvidia/mit-b0 #license-apache-2.0 #endpoints_compatible #region-us
|
# segformer-trainer-test
This model is a fine-tuned version of nvidia/mit-b0 on the segments/sidewalk-semantic dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3886
- Mean Iou: 0.1391
- Mean Accuracy: 0.1905
- Overall Accuracy: 0.7192
## Model description
More information needed
## Int... | [
"# segformer-trainer-test\n\nThis model is a fine-tuned version of nvidia/mit-b0 on the segments/sidewalk-semantic dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 1.3886\n- Mean Iou: 0.1391\n- Mean Accuracy: 0.1905\n- Overall Accuracy: 0.7192",
"## Model description\n\nMore information... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #segformer #image-segmentation #vision #generated_from_trainer #base_model-nvidia/mit-b0 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# segformer-trainer-test\n\nThis model is a fine-tuned version of nvidia/mit-b0 on the segments/sidewalk-seman... |
text-classification | transformers |
# ROBERTA BASE (cased) trained on private Bulgarian sentiment-analysis dataset
This is a Multilingual Roberta model.
This model is cased: it does make a difference between bulgarian and Bulgarian.
### How to use
Here is how to use this model in PyTorch:
```python
>>> import torch
>>> from transformers import Auto... | {"language": ["bg"], "license": "mit", "tags": ["torch"], "datasets": ["oscar", "chitanka", "wikipedia"], "inference": false} | rmihaylov/roberta-base-sentiment-bg | null | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"torch",
"custom_code",
"bg",
"dataset:oscar",
"dataset:chitanka",
"dataset:wikipedia",
"license:mit",
"autotrain_compatible",
"region:us"
] | null | 2022-04-19T14:46:37+00:00 | [] | [
"bg"
] | TAGS
#transformers #pytorch #xlm-roberta #text-classification #torch #custom_code #bg #dataset-oscar #dataset-chitanka #dataset-wikipedia #license-mit #autotrain_compatible #region-us
|
# ROBERTA BASE (cased) trained on private Bulgarian sentiment-analysis dataset
This is a Multilingual Roberta model.
This model is cased: it does make a difference between bulgarian and Bulgarian.
### How to use
Here is how to use this model in PyTorch:
| [
"# ROBERTA BASE (cased) trained on private Bulgarian sentiment-analysis dataset\nThis is a Multilingual Roberta model. \n\nThis model is cased: it does make a difference between bulgarian and Bulgarian.",
"### How to use\n\nHere is how to use this model in PyTorch:"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #text-classification #torch #custom_code #bg #dataset-oscar #dataset-chitanka #dataset-wikipedia #license-mit #autotrain_compatible #region-us \n",
"# ROBERTA BASE (cased) trained on private Bulgarian sentiment-analysis dataset\nThis is a Multilingual Roberta model. \n\n... |
text-to-speech | espnet |
## ESPnet2 TTS model
### `espnet/GunnarThor_talromur_b_fastspeech2`
This model was trained by Gunnar Thor using talromur recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 49a284e69308d81c142b89795de255b4ce290c54
pip install -e .
cd egs2/talromur/... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["talromur"]} | espnet/GunnarThor_talromur_b_fastspeech2 | null | [
"espnet",
"audio",
"text-to-speech",
"en",
"dataset:talromur",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-04-19T14:49:08+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #text-to-speech #en #dataset-talromur #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## ESPnet2 TTS model
### 'espnet/GunnarThor_talromur_b_fastspeech2'
This model was trained by Gunnar Thor using talromur recipe in espnet.
### Demo: How to use in ESPnet2
## TTS config
<details><summary>expand</summary>
</details>
### Citing ESPnet
or arXiv:
| [
"## ESPnet2 TTS model",
"### 'espnet/GunnarThor_talromur_b_fastspeech2'\n\nThis model was trained by Gunnar Thor using talromur recipe in espnet.",
"### Demo: How to use in ESPnet2",
"## TTS config\n\n<details><summary>expand</summary>\n\n\n\n</details>",
"### Citing ESPnet\n\n\n\nor arXiv:"
] | [
"TAGS\n#espnet #audio #text-to-speech #en #dataset-talromur #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## ESPnet2 TTS model",
"### 'espnet/GunnarThor_talromur_b_fastspeech2'\n\nThis model was trained by Gunnar Thor using talromur recipe in espnet.",
"### Demo: How to use in ESPnet2",
"## TTS conf... |
text-to-speech | espnet |
## ESPnet2 TTS model
### `espnet/GunnarThor_talromur_b_tacotron2`
This model was trained by Gunnar Thor using talromur recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 49a284e69308d81c142b89795de255b4ce290c54
pip install -e .
cd egs2/talromur/tt... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["talromur"]} | espnet/GunnarThor_talromur_b_tacotron2 | null | [
"espnet",
"audio",
"text-to-speech",
"en",
"dataset:talromur",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-04-19T14:49:18+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #text-to-speech #en #dataset-talromur #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## ESPnet2 TTS model
### 'espnet/GunnarThor_talromur_b_tacotron2'
This model was trained by Gunnar Thor using talromur recipe in espnet.
### Demo: How to use in ESPnet2
## TTS config
<details><summary>expand</summary>
</details>
### Citing ESPnet
or arXiv:
| [
"## ESPnet2 TTS model",
"### 'espnet/GunnarThor_talromur_b_tacotron2'\n\nThis model was trained by Gunnar Thor using talromur recipe in espnet.",
"### Demo: How to use in ESPnet2",
"## TTS config\n\n<details><summary>expand</summary>\n\n\n\n</details>",
"### Citing ESPnet\n\n\n\nor arXiv:"
] | [
"TAGS\n#espnet #audio #text-to-speech #en #dataset-talromur #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## ESPnet2 TTS model",
"### 'espnet/GunnarThor_talromur_b_tacotron2'\n\nThis model was trained by Gunnar Thor using talromur recipe in espnet.",
"### Demo: How to use in ESPnet2",
"## TTS config... |
text-to-speech | espnet |
## ESPnet2 TTS model
### `espnet/GunnarThor_talromur_c_fastspeech2`
This model was trained by Gunnar Thor using talromur recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 49a284e69308d81c142b89795de255b4ce290c54
pip install -e .
cd egs2/talromur/... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["talromur"]} | espnet/GunnarThor_talromur_c_fastspeech2 | null | [
"espnet",
"audio",
"text-to-speech",
"en",
"dataset:talromur",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-04-19T15:10:06+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #text-to-speech #en #dataset-talromur #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## ESPnet2 TTS model
### 'espnet/GunnarThor_talromur_c_fastspeech2'
This model was trained by Gunnar Thor using talromur recipe in espnet.
### Demo: How to use in ESPnet2
## TTS config
<details><summary>expand</summary>
</details>
### Citing ESPnet
or arXiv:
| [
"## ESPnet2 TTS model",
"### 'espnet/GunnarThor_talromur_c_fastspeech2'\n\nThis model was trained by Gunnar Thor using talromur recipe in espnet.",
"### Demo: How to use in ESPnet2",
"## TTS config\n\n<details><summary>expand</summary>\n\n\n\n</details>",
"### Citing ESPnet\n\n\n\nor arXiv:"
] | [
"TAGS\n#espnet #audio #text-to-speech #en #dataset-talromur #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## ESPnet2 TTS model",
"### 'espnet/GunnarThor_talromur_c_fastspeech2'\n\nThis model was trained by Gunnar Thor using talromur recipe in espnet.",
"### Demo: How to use in ESPnet2",
"## TTS conf... |
text-to-speech | espnet |
## ESPnet2 TTS model
### `espnet/GunnarThor_talromur_c_tacotron2`
This model was trained by Gunnar Thor using talromur recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 49a284e69308d81c142b89795de255b4ce290c54
pip install -e .
cd egs2/talromur/tt... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["talromur"]} | espnet/GunnarThor_talromur_c_tacotron2 | null | [
"espnet",
"audio",
"text-to-speech",
"en",
"dataset:talromur",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-04-19T15:10:16+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #text-to-speech #en #dataset-talromur #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## ESPnet2 TTS model
### 'espnet/GunnarThor_talromur_c_tacotron2'
This model was trained by Gunnar Thor using talromur recipe in espnet.
### Demo: How to use in ESPnet2
## TTS config
<details><summary>expand</summary>
</details>
### Citing ESPnet
or arXiv:
| [
"## ESPnet2 TTS model",
"### 'espnet/GunnarThor_talromur_c_tacotron2'\n\nThis model was trained by Gunnar Thor using talromur recipe in espnet.",
"### Demo: How to use in ESPnet2",
"## TTS config\n\n<details><summary>expand</summary>\n\n\n\n</details>",
"### Citing ESPnet\n\n\n\nor arXiv:"
] | [
"TAGS\n#espnet #audio #text-to-speech #en #dataset-talromur #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## ESPnet2 TTS model",
"### 'espnet/GunnarThor_talromur_c_tacotron2'\n\nThis model was trained by Gunnar Thor using talromur recipe in espnet.",
"### Demo: How to use in ESPnet2",
"## TTS config... |
text-classification | sentence-transformers |
# uaritm/lik_neuro_202
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Traini... | {"license": "apache-2.0", "tags": ["setfit", "sentence-transformers", "text-classification"], "pipeline_tag": "text-classification"} | uaritm/lik_neuro_202 | null | [
"sentence-transformers",
"pytorch",
"bert",
"setfit",
"text-classification",
"arxiv:2209.11055",
"license:apache-2.0",
"region:us"
] | null | 2022-04-19T15:38:15+00:00 | [
"2209.11055"
] | [] | TAGS
#sentence-transformers #pytorch #bert #setfit #text-classification #arxiv-2209.11055 #license-apache-2.0 #region-us
|
# uaritm/lik_neuro_202
This is a SetFit model that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a Sentence Transformer with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentenc... | [
"# uaritm/lik_neuro_202\n\nThis is a SetFit model that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:\n\n1. Fine-tuning a Sentence Transformer with contrastive learning.\n2. Training a classification head with features from the fine-tune... | [
"TAGS\n#sentence-transformers #pytorch #bert #setfit #text-classification #arxiv-2209.11055 #license-apache-2.0 #region-us \n",
"# uaritm/lik_neuro_202\n\nThis is a SetFit model that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:\n\n1.... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | GPL/bioasq-msmarco-distilbert-gpl | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-04-19T15:40:09+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can u... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\n... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\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... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | GPL/bioasq-tsdae-msmarco-distilbert-gpl | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-04-19T15:40:13+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can u... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\n... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\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... |
null | null | !pip install transformers--
license: afl-3.0
model = GPT2LMHeadModel.from_pretrained("gpt2-large", pad_token_id=tokenizer.eos_token_id)---
| {} | SVANZ/gen_testo | null | [
"region:us"
] | null | 2022-04-19T16:38:49+00:00 | [] | [] | TAGS
#region-us
| !pip install transformers--
license: afl-3.0
model = GPT2LMHeadModel.from_pretrained("gpt2-large", pad_token_id=tokenizer.eos_token_id)---
| [] | [
"TAGS\n#region-us \n"
] |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | adalbertojunior/db-msm | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-04-19T17:28:01+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can u... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\n... | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\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 se... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion... | gbennett/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-19T17:33:45+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2260
* Accuracy: 0.9185
* F1: 0.9188
Model description
-----------------
Mo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
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. -->
# roberta-base-prop-16-train-set
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an u... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "roberta-base-prop-16-train-set", "results": []}]} | michaellutz/roberta-base-prop-16-train-set | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-19T18:02:57+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# roberta-base-prop-16-train-set
This model is a fine-tuned version of roberta-base on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparam... | [
"# roberta-base-prop-16-train-set\n\nThis model is a fine-tuned version of roberta-base on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedur... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# roberta-base-prop-16-train-set\n\nThis model is a fine-tuned version of roberta-base on an unknown dataset.",
"## Model description\n\nMore... |
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-xlsr-53_toy_train_fast_masked_augment_random_noise_slow_fast
This model is a fine-tuned version of [facebook/wav2... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-large-xlsr-53_toy_train_fast_masked_augment_random_noise_slow_fast", "results": []}]} | scasutt/wav2vec2-large-xlsr-53_toy_train_fast_masked_augment_random_noise_slow_fast | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-19T18:24:06+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xlsr-53\_toy\_train\_fast\_masked\_augment\_random\_noise\_slow\_fast
====================================================================================
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the None dataset.
It achieves the following results on the evaluation set:
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 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 #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: 16\n* eval\\_b... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1256728742292074496/96By... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/billgates-kellytclements-xychelsea/1650398924367/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/billgates-kellytclements-xychelsea | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-19T19:06:34+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
Chelsea E. Manning & Bill Gates & Kelly T. Clements
@billgates-kellytclements-xychelsea
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
image-segmentation | 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. -->
# segformer-trainer-test-bis
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the se... | {"license": "apache-2.0", "tags": ["image-segmentation", "vision", "generated_from_trainer"], "base_model": "nvidia/mit-b0", "model-index": [{"name": "segformer-trainer-test-bis", "results": []}]} | nielsr/segformer-trainer-test-bis | null | [
"transformers",
"pytorch",
"tensorboard",
"segformer",
"image-segmentation",
"vision",
"generated_from_trainer",
"base_model:nvidia/mit-b0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-19T19:38:30+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #segformer #image-segmentation #vision #generated_from_trainer #base_model-nvidia/mit-b0 #license-apache-2.0 #endpoints_compatible #region-us
|
# segformer-trainer-test-bis
This model is a fine-tuned version of nvidia/mit-b0 on the segments/sidewalk-semantic dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3784
- Mean Iou: 0.1424
- Mean Accuracy: 0.1896
- Overall Accuracy: 0.7288
- Accuracy Unlabeled: nan
- Accuracy Flat-road: 0.... | [
"# segformer-trainer-test-bis\n\nThis model is a fine-tuned version of nvidia/mit-b0 on the segments/sidewalk-semantic dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 1.3784\n- Mean Iou: 0.1424\n- Mean Accuracy: 0.1896\n- Overall Accuracy: 0.7288\n- Accuracy Unlabeled: nan\n- Accuracy Fl... | [
"TAGS\n#transformers #pytorch #tensorboard #segformer #image-segmentation #vision #generated_from_trainer #base_model-nvidia/mit-b0 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# segformer-trainer-test-bis\n\nThis model is a fine-tuned version of nvidia/mit-b0 on the segments/sidewalk-semantic datas... |
image-to-text | 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. -->
# ArOCR
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the following results... | {"language": "ar", "tags": ["image-to-text"]} | gagan3012/ArOCR | null | [
"transformers",
"pytorch",
"tensorboard",
"vision-encoder-decoder",
"image-to-text",
"ar",
"model-index",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-19T20:13:24+00:00 | [] | [
"ar"
] | TAGS
#transformers #pytorch #tensorboard #vision-encoder-decoder #image-to-text #ar #model-index #endpoints_compatible #has_space #region-us
| ArOCR
=====
This model is a fine-tuned version of [](URL on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0407
* Cer: 0.0200
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #vision-encoder-decoder #image-to-text #ar #model-index #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\... |
text-classification | transformers | # distilbert-base-turkish-cased-emotion
## Model description:
[Distilbert-base-turkish-cased](https://huggingface.co/dbmdz/distilbert-base-turkish-cased) finetuned on the emotion dataset (Translated to Turkish via Google Translate API) using HuggingFace Trainer with below Hyperparameters
```
learning rate 2e-5,
bat... | {"language": ["tr"], "tags": ["text-classification", "emotion", "pytorch"], "datasets": ["emotion (Translated to Turkish)"], "metrics": ["Accuracy, F1 Score"], "thumbnail": "https://avatars3.githubusercontent.com/u/32437151?s=460&u=4ec59abc8d21d5feea3dab323d23a5860e6996a4&v=4"} | zafercavdar/distilbert-base-turkish-cased-emotion | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"emotion",
"tr",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-19T20:16:33+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #distilbert #text-classification #emotion #tr #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-turkish-cased-emotion
=====================================
Model description:
------------------
Distilbert-base-turkish-cased finetuned on the emotion dataset (Translated to Turkish via Google Translate API) using HuggingFace Trainer with below Hyperparameters
Model Performance Comparision on Em... | [] | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #emotion #tr #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": []}]} | Aldraz/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-19T22:26:01+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2319
* Accuracy: 0.921
* F1: 0.9214
Model description
-----------------
More... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #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* train\\_b... |
text2text-generation | transformers | # eva_ru_forum_headlines
## Model Description
The model was trained on forum topics names and first posts (100 - 150 words). It generates short headlines (3 - 5 words) in the opposite to headlines from models trained on newspaper articles.
"I do not know how to title this post" can be a valid headline.
"What would yo... | {"language": ["ru"], "widget": [{"text": "\u0426\u0435\u043b\u044c \u043e\u0434\u043d\u0430 - \u0438\u0441\u0442\u0440\u0435\u0431\u043b\u0435\u043d\u0438\u0435 \u043a\u0430\u043a \u043c\u043e\u0436\u043d\u043e \u0431\u043e\u043b\u044c\u0448\u0435 \u0441\u043b\u0430\u0432\u044f\u043d\u0441\u043a\u0438\u0445 \u043d\u043... | Kateryna/eva_ru_forum_headlines | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"ru",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-19T23:00:24+00:00 | [] | [
"ru"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #ru #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # eva_ru_forum_headlines
## Model Description
The model was trained on forum topics names and first posts (100 - 150 words). It generates short headlines (3 - 5 words) in the opposite to headlines from models trained on newspaper articles.
"I do not know how to title this post" can be a valid headline.
"What would yo... | [
"# eva_ru_forum_headlines",
"## Model Description\nThe model was trained on forum topics names and first posts (100 - 150 words). It generates short headlines (3 - 5 words) in the opposite to headlines from models trained on newspaper articles.\n\n\"I do not know how to title this post\" can be a valid headline.\... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #ru #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# eva_ru_forum_headlines",
"## Model Description\nThe model was trained on forum topics names and first posts (100 - 150 words). It generates short headlines (3 - 5... |
null | null | Surgery classification | {} | tgordon/surgery_classification | null | [
"region:us"
] | null | 2022-04-19T23:34:49+00:00 | [] | [] | TAGS
#region-us
| Surgery classification | [] | [
"TAGS\n#region-us \n"
] |
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. -->
# ctrlv-wav2vec2-tokenizer
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "ctrlv-wav2vec2-tokenizer", "results": []}]} | proseph/ctrlv-wav2vec2-tokenizer | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-20T00:08:37+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| ctrlv-wav2vec2-tokenizer
========================
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: 0.3967
* Wer: 0.3138
Model description
-----------------
More information needed
Intended uses & limitations
----... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 3... |
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. -->
# bart-finetuned-conala-3
This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge", "bleu"], "model-index": [{"name": "bart-finetuned-conala-3", "results": []}]} | celinelee/bart-finetuned-conala-3 | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-20T01:00:22+00:00 | [] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bart-finetuned-conala-3
=======================
This model is a fine-tuned version of facebook/bart-large on an CoNaLa.
It achieves the following results on the evaluation set:
* Loss: 1.8253
* Rouge1: 47.4345
* Rouge2: 23.8936
* Rougel: 45.317
* Rougelsum: 45.4339
* Bleu: 0.0657
* Gen Len: 58.0
Model description... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-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: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #generated_from_trainer #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: 5e-05\n* train\\_batch\\_size: 4\n* ... |
text-generation | null |
# My Awesome Model
| {"tags": ["conversational"]} | jakewillms17/capcake-model | null | [
"conversational",
"region:us"
] | null | 2022-04-20T01:18:28+00:00 | [] | [] | TAGS
#conversational #region-us
|
# My Awesome Model
| [
"# My Awesome Model"
] | [
"TAGS\n#conversational #region-us \n",
"# My Awesome Model"
] |
multiple-choice | transformers | # INT8 bert-base-uncased-finetuned-swag
### Post-training static quantization
This is an INT8 PyTorch model quantized with [huggingface/optimum-intel](https://github.com/huggingface/optimum-intel) through the usage of [Intel® Neural Compressor](https://github.com/intel/neural-compressor).
The original fp32 model c... | {"language": ["en"], "license": "apache-2.0", "tags": ["multiple-choice", "int8", "Intel\u00ae Neural Compressor", "PostTrainingStatic"], "datasets": ["swag"], "metrics": ["accuracy"], "model-index": [{"name": "bert-base-uncased-finetuned-swag-int8-static", "results": [{"task": {"type": "multiple-choice", "name": "Mult... | Intel/bert-base-uncased-finetuned-swag-int8-static | null | [
"transformers",
"pytorch",
"bert",
"multiple-choice",
"int8",
"Intel® Neural Compressor",
"PostTrainingStatic",
"en",
"dataset:swag",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-04-20T02:20:35+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #multiple-choice #int8 #Intel® Neural Compressor #PostTrainingStatic #en #dataset-swag #license-apache-2.0 #model-index #endpoints_compatible #region-us
| INT8 bert-base-uncased-finetuned-swag
=====================================
### Post-training static quantization
This is an INT8 PyTorch model quantized with huggingface/optimum-intel through the usage of Intel® Neural Compressor.
The original fp32 model comes from the fine-tuned model thyagosme/bert-base-uncased-... | [
"### Post-training static quantization\n\n\nThis is an INT8 PyTorch model quantized with huggingface/optimum-intel through the usage of Intel® Neural Compressor.\nThe original fp32 model comes from the fine-tuned model thyagosme/bert-base-uncased-finetuned-swag.\n\n\nThe calibration dataloader is the train dataload... | [
"TAGS\n#transformers #pytorch #bert #multiple-choice #int8 #Intel® Neural Compressor #PostTrainingStatic #en #dataset-swag #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Post-training static quantization\n\n\nThis is an INT8 PyTorch model quantized with huggingface/optimum-intel throu... |
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-urdu-cv8-200epochs
This model was trained from scratch on the common_voice dataset.
It achieves the fo... | {"tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-urdu-cv8-200epochs", "results": []}]} | omar47/wav2vec2-large-xls-r-300m-urdu-cv8-200epochs | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"endpoints_compatible",
"region:us"
] | null | 2022-04-20T02:29:34+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #endpoints_compatible #region-us
| wav2vec2-large-xls-r-300m-urdu-cv8-200epochs
============================================
This model was trained from scratch on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 1.3200
* Wer: 0.7723
Model description
-----------------
More information needed
Intended... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 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 #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #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:... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1503591435324563456/foUr... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/elonmusk-iamsrk/1650430682800/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/elonmusk-iamsrk | null | [
"transformers",
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"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-20T03:52:51+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
Elon Musk & Shah Rukh Khan
@elonmusk-iamsrk
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
T... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
fill-mask | transformers | 🇹🇷 RoBERTaTurk
## Model description
This is a Turkish RoBERTa base model pretrained on Turkish Wikipedia, Turkish OSCAR, and some news websites.
The final training corpus has a size of 38 GB and 329.720.508 sentences.
Thanks to Turkcell we could train the model on Intel(R) Xeon(R) Gold 6230R CPU @ 2.10GHz 256GB RA... | {"language": "tr", "license": "mit"} | burakaytan/roberta-base-turkish-uncased | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"tr",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-20T05:08:13+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #roberta #fill-mask #tr #license-mit #autotrain_compatible #endpoints_compatible #region-us
| 🇹🇷 RoBERTaTurk
## Model description
This is a Turkish RoBERTa base model pretrained on Turkish Wikipedia, Turkish OSCAR, and some news websites.
The final training corpus has a size of 38 GB and 329.720.508 sentences.
Thanks to Turkcell we could train the model on Intel(R) Xeon(R) Gold 6230R CPU @ 2.10GHz 256GB RA... | [
"## Model description\nThis is a Turkish RoBERTa base model pretrained on Turkish Wikipedia, Turkish OSCAR, and some news websites.\n\nThe final training corpus has a size of 38 GB and 329.720.508 sentences.\n\nThanks to Turkcell we could train the model on Intel(R) Xeon(R) Gold 6230R CPU @ 2.10GHz 256GB RAM 2 x GV... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #tr #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"## Model description\nThis is a Turkish RoBERTa base model pretrained on Turkish Wikipedia, Turkish OSCAR, and some news websites.\n\nThe final training corpus has a size of 38 GB and 32... |
text2text-generation | keras |
# ID G2P LSTM
ID G2P LSTM is a grapheme-to-phoneme model based on the [LSTM](https://doi.org/10.1162/neco.1997.9.8.1735) architecture. This model was trained from scratch on a modified [Malay/Indonesian lexicon](https://huggingface.co/datasets/bookbot/id_word2phoneme).
This model was trained using the [Keras](https:... | {"language": ["id", "ms"], "license": "apache-2.0", "tags": ["g2p", "text2text-generation"], "inference": false} | bookbot/id-g2p-lstm | null | [
"keras",
"tensorboard",
"g2p",
"text2text-generation",
"id",
"ms",
"license:apache-2.0",
"region:us"
] | null | 2022-04-20T05:19:05+00:00 | [] | [
"id",
"ms"
] | TAGS
#keras #tensorboard #g2p #text2text-generation #id #ms #license-apache-2.0 #region-us
| ID G2P LSTM
===========
ID G2P LSTM is a grapheme-to-phoneme model based on the LSTM architecture. This model was trained from scratch on a modified Malay/Indonesian lexicon.
This model was trained using the Keras framework. All training was done on Google Colaboratory. We adapted the LSTM training script provided ... | [] | [
"TAGS\n#keras #tensorboard #g2p #text2text-generation #id #ms #license-apache-2.0 #region-us \n"
] |
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_multilingual_XLSum-finetuned-ar-wikilingua
This model is a fine-tuned version of [csebuetnlp/mT5_multilingual_XLSum](https:/... | {"tags": ["summarization", "generated_from_trainer"], "datasets": ["wiki_lingua"], "model-index": [{"name": "mT5_multilingual_XLSum-finetuned-ar-wikilingua", "results": []}]} | eslamxm/mT5_multilingual_XLSum-finetuned-ar-wikilingua | null | [
"transformers",
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"mt5",
"text2text-generation",
"summarization",
"generated_from_trainer",
"dataset:wiki_lingua",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-20T05:33:58+00:00 | [] | [] | TAGS
#transformers #pytorch #mt5 #text2text-generation #summarization #generated_from_trainer #dataset-wiki_lingua #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| mT5\_multilingual\_XLSum-finetuned-ar-wikilingua
================================================
This model is a fine-tuned version of csebuetnlp/mT5\_multilingual\_XLSum on the wiki\_lingua dataset.
It achieves the following results on the evaluation set:
* Loss: 3.6903
* Rouge-1: 24.47
* Rouge-2: 7.69
* Rouge-l:... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-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* lr\\_scheduler\\_warmup\\_steps: ... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #summarization #generated_from_trainer #dataset-wiki_lingua #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\\_... |
text-classification | transformers | # Erlangshen-Roberta-110M-Sentiment
- Main Page:[Fengshenbang](https://fengshenbang-lm.com/)
- Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM)
## 简介 Brief Introduction
中文的RoBERTa-wwm-ext-base在数个情感分析任务微调后的版本
This is the fine-tuned version of the Chinese RoBERTa-wwm-ext-base model on several s... | {"language": ["zh"], "license": "apache-2.0", "tags": ["roberta", "NLU", "Sentiment", "Chinese"], "inference": true, "widget": [{"text": "\u4eca\u5929\u5fc3\u60c5\u4e0d\u597d"}]} | IDEA-CCNL/Erlangshen-Roberta-110M-Sentiment | null | [
"transformers",
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"bert",
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"NLU",
"Sentiment",
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"zh",
"arxiv:2209.02970",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-20T05:45:09+00:00 | [
"2209.02970"
] | [
"zh"
] | TAGS
#transformers #pytorch #bert #text-classification #roberta #NLU #Sentiment #Chinese #zh #arxiv-2209.02970 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| Erlangshen-Roberta-110M-Sentiment
=================================
* Main Page:Fengshenbang
* Github: Fengshenbang-LM
简介 Brief Introduction
---------------------
中文的RoBERTa-wwm-ext-base在数个情感分析任务微调后的版本
This is the fine-tuned version of the Chinese RoBERTa-wwm-ext-base model on several sentiment analysis dataset... | [
"### 下游效果 Performance\n\n\n\n使用 Usage\n--------\n\n\n引用 Citation\n-----------\n\n\n如果您在您的工作中使用了我们的模型，可以引用我们的论文：\n\n\nIf you are using the resource for your work, please cite the our paper:\n\n\n也可以引用我们的网站:\n\n\nYou can also cite our website:"
] | [
"TAGS\n#transformers #pytorch #bert #text-classification #roberta #NLU #Sentiment #Chinese #zh #arxiv-2209.02970 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### 下游效果 Performance\n\n\n\n使用 Usage\n--------\n\n\n引用 Citation\n-----------\n\n\n如果您在您的工作中使用了我们的模型，可以引用我们的论文... |
text-classification | transformers |
# INT8 BERT base uncased finetuned MRPC
## Post-training static quantization
### PyTorch
This is an INT8 PyTorch model quantized with [huggingface/optimum-intel](https://github.com/huggingface/optimum-intel) through the usage of [Intel® Neural Compressor](https://github.com/intel/neural-compressor).
The original ... | {"language": "en", "license": "apache-2.0", "tags": ["text-classfication", "int8", "Intel\u00ae Neural Compressor", "neural-compressor", "PostTrainingStatic"], "datasets": ["mrpc"], "metrics": ["f1"]} | Intel/bert-base-uncased-mrpc-int8-static | null | [
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"en",
"dataset:mrpc",
"license:apache-2.0",
"autotrain_compatible",
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"region:us"
] | null | 2022-04-20T06:00:53+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #onnx #bert #text-classification #text-classfication #int8 #Intel® Neural Compressor #neural-compressor #PostTrainingStatic #en #dataset-mrpc #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| INT8 BERT base uncased finetuned MRPC
=====================================
Post-training static quantization
---------------------------------
### PyTorch
This is an INT8 PyTorch model quantized with huggingface/optimum-intel through the usage of Intel® Neural Compressor.
The original fp32 model comes from the... | [
"### PyTorch\n\n\nThis is an INT8 PyTorch model quantized with huggingface/optimum-intel through the usage of Intel® Neural Compressor.\n\n\nThe original fp32 model comes from the fine-tuned model Intel/bert-base-uncased-mrpc.\n\n\nThe calibration dataloader is the train dataloader. The calibration sampling size is... | [
"TAGS\n#transformers #pytorch #onnx #bert #text-classification #text-classfication #int8 #Intel® Neural Compressor #neural-compressor #PostTrainingStatic #en #dataset-mrpc #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### PyTorch\n\n\nThis is an INT8 PyTorch model quantized with... |
text-classification | transformers |
# Erlangshen-Roberta-330M-Sentiment
- Main Page:[Fengshenbang](https://fengshenbang-lm.com/)
- Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM)
## 简介 Brief Introduction
中文的RoBERTa-wwm-ext-large在数个情感分析任务微调后的版本
This is the fine-tuned version of the Chinese RoBERTa-wwm-ext-large model on severa... | {"language": ["zh"], "license": "apache-2.0", "tags": ["roberta", "NLU", "Sentiment", "Chinese"], "inference": true, "widget": [{"text": "\u4eca\u5929\u5fc3\u60c5\u4e0d\u597d"}]} | IDEA-CCNL/Erlangshen-Roberta-330M-Sentiment | null | [
"transformers",
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"safetensors",
"bert",
"text-classification",
"roberta",
"NLU",
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"zh",
"arxiv:2209.02970",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-20T06:15:44+00:00 | [
"2209.02970"
] | [
"zh"
] | TAGS
#transformers #pytorch #safetensors #bert #text-classification #roberta #NLU #Sentiment #Chinese #zh #arxiv-2209.02970 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| Erlangshen-Roberta-330M-Sentiment
=================================
* Main Page:Fengshenbang
* Github: Fengshenbang-LM
简介 Brief Introduction
---------------------
中文的RoBERTa-wwm-ext-large在数个情感分析任务微调后的版本
This is the fine-tuned version of the Chinese RoBERTa-wwm-ext-large model on several sentiment analysis datas... | [
"### 下游效果 Performance\n\n\n\n使用 Usage\n--------\n\n\n引用 Citation\n-----------\n\n\n如果您在您的工作中使用了我们的模型，可以引用我们的论文：\n\n\nIf you are using the resource for your work, please cite the our paper:\n\n\n也可以引用我们的网站:\n\n\nYou can also cite our website:"
] | [
"TAGS\n#transformers #pytorch #safetensors #bert #text-classification #roberta #NLU #Sentiment #Chinese #zh #arxiv-2209.02970 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### 下游效果 Performance\n\n\n\n使用 Usage\n--------\n\n\n引用 Citation\n-----------\n\n\n如果您在您的工作中使用了我们... |
image-segmentation | 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. -->
# segformer-finetuned-sidewalk-50-epochs
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b... | {"license": "apache-2.0", "tags": ["image-segmentation", "vision", "generated_from_trainer"], "base_model": "nvidia/mit-b0", "model-index": [{"name": "segformer-finetuned-sidewalk-50-epochs", "results": []}]} | nielsr/segformer-finetuned-sidewalk-10k-steps | null | [
"transformers",
"pytorch",
"tensorboard",
"segformer",
"image-segmentation",
"vision",
"generated_from_trainer",
"base_model:nvidia/mit-b0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-20T06:21:41+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #segformer #image-segmentation #vision #generated_from_trainer #base_model-nvidia/mit-b0 #license-apache-2.0 #endpoints_compatible #region-us
| segformer-finetuned-sidewalk-50-epochs
======================================
This model is a fine-tuned version of nvidia/mit-b0 on the segments/sidewalk-semantic dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6350
* Mean Iou: 0.3022
* Mean Accuracy: 0.3724
* Overall Accuracy: 0.8117
*... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6e-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: polynomial\n* training\\_steps: 10000",
... | [
"TAGS\n#transformers #pytorch #tensorboard #segformer #image-segmentation #vision #generated_from_trainer #base_model-nvidia/mit-b0 #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: 6e-05\n*... |
feature-extraction | transformers | # <a name="introduction"></a> ViHealthBERT: Pre-trained Language Models for Vietnamese in Health Text Mining
ViHealthBERT is the a strong baseline language models for Vietnamese in Healthcare domain.
We empirically investigate our model with different training strategies, achieving state of the art (SOTA) performa... | {} | demdecuong/vihealthbert-base-word | null | [
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | null | 2022-04-20T06:49:34+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #feature-extraction #endpoints_compatible #region-us
| ViHealthBERT: Pre-trained Language Models for Vietnamese in Health Text Mining
===============================================================================
ViHealthBERT is the a strong baseline language models for Vietnamese in Healthcare domain.
We empirically investigate our model with different training stra... | [
"### Installation\n\n\n* Python 3.6+, and PyTorch >= 1.6\n* Install 'transformers': \n\n'pip install transformers==4.2.0'",
"### Pre-trained models",
"### Example usage",
"### Example usage for raw text\n\n\nSince ViHealthBERT used the RDRSegmenter from VnCoreNLP to pre-process the pre-training data.\nWe hig... | [
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"### Pre-trained models",
"### Example usage",
"### Example usage for raw text\n\n\nSi... |
feature-extraction | transformers | # <a name="introduction"></a> ViHealthBERT: Pre-trained Language Models for Vietnamese in Health Text Mining
ViHealthBERT is the a strong baseline language models for Vietnamese in Healthcare domain.
We empirically investigate our model with different training strategies, achieving state of the art (SOTA) performa... | {} | demdecuong/vihealthbert-base-syllable | null | [
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | null | 2022-04-20T06:50:23+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #feature-extraction #endpoints_compatible #region-us
| ViHealthBERT: Pre-trained Language Models for Vietnamese in Health Text Mining
===============================================================================
ViHealthBERT is the a strong baseline language models for Vietnamese in Healthcare domain.
We empirically investigate our model with different training stra... | [
"### Installation\n\n\n* Python 3.6+, and PyTorch >= 1.6\n* Install 'transformers': \n\n'pip install transformers==4.2.0'",
"### Pre-trained models",
"### Example usage",
"### Example usage for raw text\n\n\nSince ViHealthBERT used the RDRSegmenter from VnCoreNLP to pre-process the pre-training data.\nWe hig... | [
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"### Pre-trained models",
"### Example usage",
"### Example usage for raw text\n\n\nSi... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# focus_sum_gpt2
This model is a fine-tuned version of [uer/gpt2-chinese-cluecorpussmall](https://huggingface.co/uer/gpt2-chinese-... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "focus_sum_gpt2", "results": []}]} | eagles/focus_sum_gpt2 | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-20T06:56:28+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| focus\_sum\_gpt2
================
This model is a fine-tuned version of uer/gpt2-chinese-cluecorpussmall on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1917
Model description
-----------------
More information needed
Intended uses & limitations
----------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batc... |
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-common_voice-lithuanian
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/... | {"language": ["lt"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "common_voice", "generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-common_voice-lithuanian", "results": []}]} | birgermoell/wav2vec2-common_voice-lithuanian | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"common_voice",
"generated_from_trainer",
"lt",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-20T07:10:38+00:00 | [] | [
"lt"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #lt #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-common\_voice-lithuanian
=================================
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the COMMON\_VOICE - LT dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5988
* Wer: 0.6546
Model description
-----------------
More information ... | [
"### 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 #common_voice #generated_from_trainer #lt #dataset-common_voice #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: 0.0003... |
automatic-speech-recognition | transformers |
# Hubert-XLarge-ls960-ft + 4-gram
This model is identical to [Facebook's hubert-xlarge-ls960-ft](https://huggingface.co/facebook/hubert-xlarge-ls960-ft), but is
augmented with an English 4-gram. The `4-gram.arpa.gz` of [Librispeech's official ngrams](https://www.openslr.org/11) is used.
## Evaluation
This code... | {"language": "en", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "hf-asr-leaderboard"], "datasets": ["librispeech_asr"], "widget": [{"example_title": "Librispeech sample 1", "src": "https://cdn-media.huggingface.co/speech_samples/sample1.flac"}, {"example_title": "Librispeech sample 2", "sr... | patrickvonplaten/hubert-xlarge-ls960-ft-4-gram | null | [
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"license:apache-2.0",
"model-index",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-20T07:21:44+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #hubert #automatic-speech-recognition #audio #hf-asr-leaderboard #en #dataset-librispeech_asr #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us
| Hubert-XLarge-ls960-ft + 4-gram
===============================
This model is identical to Facebook's hubert-xlarge-ls960-ft, but is
augmented with an English 4-gram. The 'URL' of Librispeech's official ngrams is used.
Evaluation
----------
This code snippet shows how to evaluate patrickvonplaten/hubert-xlarge-ls... | [] | [
"TAGS\n#transformers #pytorch #hubert #automatic-speech-recognition #audio #hf-asr-leaderboard #en #dataset-librispeech_asr #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us \n"
] |
fill-mask | transformers |
# tokenizer - BPE 30_522 vocab size
## model - Roberta
trained using MLM
OSCAR dataset
train data size 5000 lines olly | {"language": ["Tamil"], "license": "apache-2.0", "tags": ["Tamil-Tokenizer", "Tamil-language-model"], "datasets": ["oscar"]} | AswiN037/tamil-Roberta-small | null | [
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"fill-mask",
"Tamil-Tokenizer",
"Tamil-language-model",
"dataset:oscar",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-20T07:29:14+00:00 | [] | [
"Tamil"
] | TAGS
#transformers #pytorch #roberta #fill-mask #Tamil-Tokenizer #Tamil-language-model #dataset-oscar #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# tokenizer - BPE 30_522 vocab size
## model - Roberta
trained using MLM
OSCAR dataset
train data size 5000 lines olly | [
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"# tokenizer - BPE 30_522 vocab size",
"## model - Roberta \n trained using MLM \n OSCAR dataset\n train da... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-colab", "results": []}]} | obokkkk/wav2vec2-base-timit-demo-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-20T07:30:37+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-timit-demo-colab
==============================
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4779
* Wer: 0.3468
Model description
-----------------
More information needed
Intended uses & limi... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
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image-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. -->
# swin-tiny-patch4-window7-224-plant-doctor
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https:/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["image_folder"], "metrics": ["accuracy"], "model-index": [{"name": "swin-tiny-patch4-window7-224-plant-doctor", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "image_folder", "type": "i... | plantdoctor/swin-tiny-patch4-window7-224-plant-doctor | null | [
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"autotrain_compatible",
"endpoints_compatible",
"has_space",
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] | null | 2022-04-20T07:44:27+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #swin #image-classification #generated_from_trainer #dataset-image_folder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| swin-tiny-patch4-window7-224-plant-doctor
=========================================
This model is a fine-tuned version of microsoft/swin-tiny-patch4-window7-224 on the image\_folder dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0043
* Accuracy: 0.9983
Model description
--------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 762623422
- CO2 Emissions (in grams): 4.453029772491864
## Validation Metrics
- Loss: 0.40843138098716736
- Accuracy: 0.8302828618968386
- Macro F1: 0.8302447939743022
- Micro F1: 0.8302828618968385
- Weighted F1: 0.8302151855901... | {"language": "en", "tags": "autotrain", "datasets": ["Souvikcmsa/autotrain-data-sentimentAnalysis_By_Souvik"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 4.453029772491864} | Souvikcmsa/Roberta_Sentiment_Analysis | null | [
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"text-classification",
"autotrain",
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"dataset:Souvikcmsa/autotrain-data-sentimentAnalysis_By_Souvik",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-20T07:50:58+00:00 | [] | [
"en"
] | TAGS
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|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 762623422
- CO2 Emissions (in grams): 4.453029772491864
## Validation Metrics
- Loss: 0.40843138098716736
- Accuracy: 0.8302828618968386
- Macro F1: 0.8302447939743022
- Micro F1: 0.8302828618968385
- Weighted F1: 0.8302151855901... | [
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"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 762623... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification (3-class Sentiment Classification)
## Validation Metrics
If you search sentiment analysis model in huggingface you find a model from finiteautomata. Their model provides micro and macro F1 score around 67%. Check out this model with around 80... | {"language": "en", "tags": "autotrain", "datasets": ["Souvikcmsa/autotrain-data-sentiment_analysis"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}, {"Output": "Positive"}], "co2_eq_emissions": 0.029363397844935534} | Souvikcmsa/BERT_sentiment_analysis | null | [
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"dataset:Souvikcmsa/autotrain-data-sentiment_analysis",
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"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-20T08:03:15+00:00 | [] | [
"en"
] | TAGS
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|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification (3-class Sentiment Classification)
## Validation Metrics
If you search sentiment analysis model in huggingface you find a model from finiteautomata. Their model provides micro and macro F1 score around 67%. Check out this model with around 80... | [
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"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification (3-class Sentiment Classificati... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 762923432
- CO2 Emissions (in grams): 0.015536746909294205
## Validation Metrics
- Loss: 0.49825894832611084
- Accuracy: 0.7962895598399418
- Macro F1: 0.7997458031044901
- Micro F1: 0.7962895598399418
- Weighted F1: 0.7963653258... | {"language": "en", "tags": "autotrain", "datasets": ["Souvikcmsa/autotrain-data-sentiment_analysis"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 0.015536746909294205} | Souvikcmsa/SentimentAnalysisDistillBERT | null | [
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"distilbert",
"text-classification",
"autotrain",
"en",
"dataset:Souvikcmsa/autotrain-data-sentiment_analysis",
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"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-20T08:03:32+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #distilbert #text-classification #autotrain #en #dataset-Souvikcmsa/autotrain-data-sentiment_analysis #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 762923432
- CO2 Emissions (in grams): 0.015536746909294205
## Validation Metrics
- Loss: 0.49825894832611084
- Accuracy: 0.7962895598399418
- Macro F1: 0.7997458031044901
- Micro F1: 0.7962895598399418
- Weighted F1: 0.7963653258... | [
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"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 7... |
token-classification | transformers | # INT8 distilbert-base-uncased-finetuned-conll03-english
### Post-training static quantization
This is an INT8 PyTorch model quantized with [huggingface/optimum-intel](https://github.com/huggingface/optimum-intel) through the usage of [Intel® Neural Compressor](https://github.com/intel/neural-compressor).
The ori... | {"language": ["en"], "license": "apache-2.0", "tags": ["token-classfication", "int8", "Intel\u00ae Neural Compressor", "PostTrainingStatic"], "datasets": ["conll2003"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-conll03-english-int8-static", "results": [{"task": {"type": "token... | Intel/distilbert-base-uncased-finetuned-conll03-english-int8-static | null | [
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"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-20T08:03:49+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #distilbert #token-classification #token-classfication #int8 #Intel® Neural Compressor #PostTrainingStatic #en #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| INT8 distilbert-base-uncased-finetuned-conll03-english
======================================================
### Post-training static quantization
This is an INT8 PyTorch model quantized with huggingface/optimum-intel through the usage of Intel® Neural Compressor.
The original fp32 model comes from the fine-tune... | [
"### Post-training static quantization\n\n\nThis is an INT8 PyTorch model quantized with huggingface/optimum-intel through the usage of Intel® Neural Compressor.\n\n\nThe original fp32 model comes from the fine-tuned model elastic/distilbert-base-uncased-finetuned-conll03-english.\n\n\nThe calibration dataloader is... | [
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"### Post-training static quantization\n\n\nThis is an INT8 P... |
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. -->
# AGT_Roberta
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None da... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "AGT_Roberta", "results": []}]} | James-kc-min/AGT_Roberta | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-20T08:34:51+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# AGT_Roberta
This model is a fine-tuned version of distilroberta-base on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The foll... | [
"# AGT_Roberta\n\nThis model is a fine-tuned version of distilroberta-base on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Trai... | [
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"## Model description\n\nMore information needed",... |
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. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "finetuning-sentiment-model-3000-samples", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "imdb", "type": "imdb", "args": ... | PDM/finetuning-sentiment-model-3000-samples | null | [
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"region:us"
] | null | 2022-04-20T08:35:06+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3061
- Accuracy: 0.8733
- F1: 0.8742
## Model description
More information needed
## Intended uses & limitations
More in... | [
"# finetuning-sentiment-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.3061\n- Accuracy: 0.8733\n- F1: 0.8742",
"## Model description\n\nMore information needed",
"## Intended uses & li... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# finetuning-sentiment-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased on the imdb ... |
text-classification | transformers | Hugging Face's logo
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James-kc-min
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AGT_Roberta Copied
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0
Text Classification
PyTorch
Transformers
apache-2.0
roberta
generated_from_traine... | {} | James-kc-min/AGT_Roberta2 | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-20T09:13:46+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us
| Hugging Face's logo
Hugging Face
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James-kc-min
/
AGT_Roberta Copied
like
0
Text Classification
PyTorch
Transformers
apache-2.0
roberta
generated_from_trainer
... | [
"# AGT_Roberta\n\nThis model is a fine-tuned version of distilroberta-base on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Trai... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# AGT_Roberta\n\nThis model is a fine-tuned version of distilroberta-base on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore i... |
automatic-speech-recognition | transformers |
# Data2Vec-Audio-Base-10m
[Facebook's Data2Vec](https://ai.facebook.com/research/data2vec-a-general-framework-for-self-supervised-learning-in-speech-vision-and-language/)
The base model pretrained and fine-tuned on 10 minutes of Librispeech on 16kHz sampled speech audio. When using the model
make sure that your spee... | {"language": "en", "license": "apache-2.0", "tags": ["speech"], "datasets": ["librispeech_asr"]} | patrickvonplaten/data2vec-audio-base-10m-4-gram | null | [
"transformers",
"pytorch",
"data2vec-audio",
"automatic-speech-recognition",
"speech",
"en",
"dataset:librispeech_asr",
"arxiv:2202.03555",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-20T09:28:23+00:00 | [
"2202.03555"
] | [
"en"
] | TAGS
#transformers #pytorch #data2vec-audio #automatic-speech-recognition #speech #en #dataset-librispeech_asr #arxiv-2202.03555 #license-apache-2.0 #endpoints_compatible #region-us
|
# Data2Vec-Audio-Base-10m
Facebook's Data2Vec
The base model pretrained and fine-tuned on 10 minutes of Librispeech on 16kHz sampled speech audio. When using the model
make sure that your speech input is also sampled at 16Khz.
Paper
Authors: Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu, Michael ... | [
"# Data2Vec-Audio-Base-10m\n\nFacebook's Data2Vec\n\nThe base model pretrained and fine-tuned on 10 minutes of Librispeech on 16kHz sampled speech audio. When using the model\nmake sure that your speech input is also sampled at 16Khz.\n\nPaper\n\nAuthors: Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao... | [
"TAGS\n#transformers #pytorch #data2vec-audio #automatic-speech-recognition #speech #en #dataset-librispeech_asr #arxiv-2202.03555 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Data2Vec-Audio-Base-10m\n\nFacebook's Data2Vec\n\nThe base model pretrained and fine-tuned on 10 minutes of Librispeech on... |
automatic-speech-recognition | transformers |
# Data2Vec-Audio-Base-100h
[Facebook's Data2Vec](https://ai.facebook.com/research/data2vec-a-general-framework-for-self-supervised-learning-in-speech-vision-and-language/)
The base model pretrained and fine-tuned on 100 hours of Librispeech on 16kHz sampled speech audio. When using the model
make sure that your spee... | {"language": "en", "license": "apache-2.0", "tags": ["speech"], "datasets": ["librispeech_asr"]} | patrickvonplaten/data2vec-audio-base-100h-4-gram | null | [
"transformers",
"pytorch",
"data2vec-audio",
"automatic-speech-recognition",
"speech",
"en",
"dataset:librispeech_asr",
"arxiv:2202.03555",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-20T09:28:34+00:00 | [
"2202.03555"
] | [
"en"
] | TAGS
#transformers #pytorch #data2vec-audio #automatic-speech-recognition #speech #en #dataset-librispeech_asr #arxiv-2202.03555 #license-apache-2.0 #endpoints_compatible #region-us
|
# Data2Vec-Audio-Base-100h
Facebook's Data2Vec
The base model pretrained and fine-tuned on 100 hours of Librispeech on 16kHz sampled speech audio. When using the model
make sure that your speech input is also sampled at 16Khz.
Paper
Authors: Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu, Michael ... | [
"# Data2Vec-Audio-Base-100h\n\nFacebook's Data2Vec\n\nThe base model pretrained and fine-tuned on 100 hours of Librispeech on 16kHz sampled speech audio. When using the model\nmake sure that your speech input is also sampled at 16Khz.\n\nPaper\n\nAuthors: Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao... | [
"TAGS\n#transformers #pytorch #data2vec-audio #automatic-speech-recognition #speech #en #dataset-librispeech_asr #arxiv-2202.03555 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Data2Vec-Audio-Base-100h\n\nFacebook's Data2Vec\n\nThe base model pretrained and fine-tuned on 100 hours of Librispeech on... |
automatic-speech-recognition | transformers |
# Data2Vec-Audio-Base-960h
[Facebook's Data2Vec](https://ai.facebook.com/research/data2vec-a-general-framework-for-self-supervised-learning-in-speech-vision-and-language/)
The base model pretrained and fine-tuned on 960 hours of Librispeech on 16kHz sampled speech audio. When using the model
make sure that your spee... | {"language": "en", "license": "apache-2.0", "tags": ["speech", "hf-asr-leaderboard"], "datasets": ["librispeech_asr"], "widget": [{"example_title": "Librispeech sample 1", "src": "https://cdn-media.huggingface.co/speech_samples/sample1.flac"}, {"example_title": "Librispeech sample 2", "src": "https://cdn-media.huggingf... | patrickvonplaten/data2vec-audio-base-960h-4-gram | null | [
"transformers",
"pytorch",
"data2vec-audio",
"automatic-speech-recognition",
"speech",
"hf-asr-leaderboard",
"en",
"dataset:librispeech_asr",
"arxiv:2202.03555",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-04-20T09:28:45+00:00 | [
"2202.03555"
] | [
"en"
] | TAGS
#transformers #pytorch #data2vec-audio #automatic-speech-recognition #speech #hf-asr-leaderboard #en #dataset-librispeech_asr #arxiv-2202.03555 #license-apache-2.0 #model-index #endpoints_compatible #region-us
| Data2Vec-Audio-Base-960h
========================
Facebook's Data2Vec
The base model pretrained and fine-tuned on 960 hours of Librispeech on 16kHz sampled speech audio. When using the model
make sure that your speech input is also sampled at 16Khz.
Paper
Authors: Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun... | [] | [
"TAGS\n#transformers #pytorch #data2vec-audio #automatic-speech-recognition #speech #hf-asr-leaderboard #en #dataset-librispeech_asr #arxiv-2202.03555 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n"
] |
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-53m-gl-jupyter2
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-large-xls-r-53m-gl-jupyter2", "results": []}]} | 4m1g0/wav2vec2-large-xls-r-53m-gl-jupyter2 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-20T09:42:14+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xls-r-53m-gl-jupyter2
====================================
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0941
* Wer: 0.0615
Model description
-----------------
More information needed
... | [
"### 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 #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: 0.0003\n* train\\_batch\\_size: 1... |
text-classification | transformers |
# FeverAlbert
FeverAlbert is a classifier model that predicts if evidence is related to query claim. The model achieved F1 score of 88.33% with test dataset "mwong/fever-claim-related". Using pretrained albert-base-v2 model, the classifier head is trained on Fever dataset. | {"language": "en", "license": "mit", "tags": ["text classification", "fact checking"], "datasets": ["mwong/fever-claim-related"], "metrics": "f1", "widget": [{"text": "Earth\u2019s changing climate is a critical issue and poses the risk of significant environmental, social and economic disruptions around the globe.</s>... | mwong/albert-base-fever-claim-related | null | [
"transformers",
"pytorch",
"albert",
"text-classification",
"text classification",
"fact checking",
"en",
"dataset:mwong/fever-claim-related",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-20T11:49:48+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #albert #text-classification #text classification #fact checking #en #dataset-mwong/fever-claim-related #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# FeverAlbert
FeverAlbert is a classifier model that predicts if evidence is related to query claim. The model achieved F1 score of 88.33% with test dataset "mwong/fever-claim-related". Using pretrained albert-base-v2 model, the classifier head is trained on Fever dataset. | [
"# FeverAlbert\n\nFeverAlbert is a classifier model that predicts if evidence is related to query claim. The model achieved F1 score of 88.33% with test dataset \"mwong/fever-claim-related\". Using pretrained albert-base-v2 model, the classifier head is trained on Fever dataset."
] | [
"TAGS\n#transformers #pytorch #albert #text-classification #text classification #fact checking #en #dataset-mwong/fever-claim-related #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# FeverAlbert\n\nFeverAlbert is a classifier model that predicts if evidence is related to query claim. Th... |
text-classification | transformers |
# FeverRoberta
FeverRoberta is a classifier model that predicts if evidence is related to query claim. The model achieved F1 score of 92.67% with test dataset "mwong/fever-evidence-related". Using pretrained roberta-base model, the classifier head is trained on Fever dataset. | {"language": "en", "license": "mit", "tags": ["text classification", "fact checking"], "datasets": ["mwong/fever-evidence-related"], "metrics": "f1", "widget": [{"text": "Earth\u2019s changing climate is a critical issue and poses the risk of significant environmental, social and economic disruptions around the globe.<... | mwong/roberta-base-fever-evidence-related | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"text classification",
"fact checking",
"en",
"dataset:mwong/fever-evidence-related",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-20T11:50:01+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roberta #text-classification #text classification #fact checking #en #dataset-mwong/fever-evidence-related #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# FeverRoberta
FeverRoberta is a classifier model that predicts if evidence is related to query claim. The model achieved F1 score of 92.67% with test dataset "mwong/fever-evidence-related". Using pretrained roberta-base model, the classifier head is trained on Fever dataset. | [
"# FeverRoberta\n\nFeverRoberta is a classifier model that predicts if evidence is related to query claim. The model achieved F1 score of 92.67% with test dataset \"mwong/fever-evidence-related\". Using pretrained roberta-base model, the classifier head is trained on Fever dataset."
] | [
"TAGS\n#transformers #pytorch #roberta #text-classification #text classification #fact checking #en #dataset-mwong/fever-evidence-related #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# FeverRoberta\n\nFeverRoberta is a classifier model that predicts if evidence is related to query cla... |
text-classification | transformers |
# ClimateRoberta
ClimateRoberta is a classifier model that predicts if climate related evidence is related to query claim. The model achieved F1 score of 80.13% with test dataset "mwong/climate-evidence-related". Using pretrained roberta-base model, the classifier head is trained on Fever dataset and adapted to clima... | {"language": "en", "license": "mit", "tags": ["text classification", "fact checking"], "datasets": ["mwong/fever-evidence-related", "mwong/climate-evidence-related"], "metrics": "f1", "widget": [{"text": "Earth\u2019s changing climate is a critical issue and poses the risk of significant environmental, social and econo... | mwong/roberta-base-climate-evidence-related | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"text classification",
"fact checking",
"en",
"dataset:mwong/fever-evidence-related",
"dataset:mwong/climate-evidence-related",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-20T11:52:55+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roberta #text-classification #text classification #fact checking #en #dataset-mwong/fever-evidence-related #dataset-mwong/climate-evidence-related #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# ClimateRoberta
ClimateRoberta is a classifier model that predicts if climate related evidence is related to query claim. The model achieved F1 score of 80.13% with test dataset "mwong/climate-evidence-related". Using pretrained roberta-base model, the classifier head is trained on Fever dataset and adapted to clima... | [
"# ClimateRoberta\n\nClimateRoberta is a classifier model that predicts if climate related evidence is related to query claim. The model achieved F1 score of 80.13% with test dataset \"mwong/climate-evidence-related\". Using pretrained roberta-base model, the classifier head is trained on Fever dataset and adapted ... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #text classification #fact checking #en #dataset-mwong/fever-evidence-related #dataset-mwong/climate-evidence-related #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# ClimateRoberta\n\nClimateRoberta is a classifier model that ... |
text-classification | transformers |
# FeverBert-related
FeverBert-related is a classifier model that predicts if climate related evidence is related to query claim. The model achieved F1 score of 91.23% with test dataset "mwong/fever-evidence-related". Using pretrained ClimateBert-f model, the classifier head is trained on Fever dataset. | {"language": "en", "license": "mit", "tags": ["text classification", "fact checking"], "datasets": ["mwong/fever-evidence-related"], "metrics": "f1", "widget": [{"text": "Earth\u2019s changing climate is a critical issue and poses the risk of significant environmental, social and economic disruptions around the globe.<... | mwong/climatebert-base-f-fever-evidence-related | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"text classification",
"fact checking",
"en",
"dataset:mwong/fever-evidence-related",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-20T11:53:52+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roberta #text-classification #text classification #fact checking #en #dataset-mwong/fever-evidence-related #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# FeverBert-related
FeverBert-related is a classifier model that predicts if climate related evidence is related to query claim. The model achieved F1 score of 91.23% with test dataset "mwong/fever-evidence-related". Using pretrained ClimateBert-f model, the classifier head is trained on Fever dataset. | [
"# FeverBert-related\n\nFeverBert-related is a classifier model that predicts if climate related evidence is related to query claim. The model achieved F1 score of 91.23% with test dataset \"mwong/fever-evidence-related\". Using pretrained ClimateBert-f model, the classifier head is trained on Fever dataset."
] | [
"TAGS\n#transformers #pytorch #roberta #text-classification #text classification #fact checking #en #dataset-mwong/fever-evidence-related #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# FeverBert-related\n\nFeverBert-related is a classifier model that predicts if climate related eviden... |
text-generation | transformers | This is an empty model card! | {} | Matthijs/test-gpt2 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-20T11:55:08+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| This is an empty model card! | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-classification | transformers |
# ClimateAlbert
ClimateAlbert is a classifier model that predicts if climate related evidence is related to query claim. The model achieved F1 score of 85.33% with test dataset "mwong/climate-claim-related". Using pretrained albert-base-v2 model, the classifier head is trained on Fever dataset and adapted to climate ... | {"language": "en", "license": "mit", "tags": ["text classification", "fact checking"], "datasets": ["mwong/fever-claim-related", "mwong/climate-claim-related"], "metrics": "f1", "widget": [{"text": "Earth\u2019s changing climate is a critical issue and poses the risk of significant environmental, social and economic di... | mwong/albert-base-climate-claim-related | null | [
"transformers",
"pytorch",
"albert",
"text-classification",
"text classification",
"fact checking",
"en",
"dataset:mwong/fever-claim-related",
"dataset:mwong/climate-claim-related",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-20T11:56:10+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #albert #text-classification #text classification #fact checking #en #dataset-mwong/fever-claim-related #dataset-mwong/climate-claim-related #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# ClimateAlbert
ClimateAlbert is a classifier model that predicts if climate related evidence is related to query claim. The model achieved F1 score of 85.33% with test dataset "mwong/climate-claim-related". Using pretrained albert-base-v2 model, the classifier head is trained on Fever dataset and adapted to climate ... | [
"# ClimateAlbert\n\nClimateAlbert is a classifier model that predicts if climate related evidence is related to query claim. The model achieved F1 score of 85.33% with test dataset \"mwong/climate-claim-related\". Using pretrained albert-base-v2 model, the classifier head is trained on Fever dataset and adapted to ... | [
"TAGS\n#transformers #pytorch #albert #text-classification #text classification #fact checking #en #dataset-mwong/fever-claim-related #dataset-mwong/climate-claim-related #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# ClimateAlbert\n\nClimateAlbert is a classifier model that predicts ... |
text-classification | transformers |
# ClimateBert-related
ClimateBert-related is a classifier model that predicts if climate related evidence is related to query claim. The model achieved F1 score of 81.90% with test dataset "mwong/climate-evidence-related". Using pretrained ClimateBert-f model, the classifier head is trained on Fever dataset and adapt... | {"language": "en", "license": "mit", "tags": ["text classification", "fact checking"], "datasets": ["mwong/fever-evidence-related", "mwong/climate-evidence-related"], "metrics": "f1", "widget": [{"text": "Earth\u2019s changing climate is a critical issue and poses the risk of significant environmental, social and econo... | mwong/climatebert-base-f-climate-evidence-related | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"text classification",
"fact checking",
"en",
"dataset:mwong/fever-evidence-related",
"dataset:mwong/climate-evidence-related",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-20T11:58:32+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roberta #text-classification #text classification #fact checking #en #dataset-mwong/fever-evidence-related #dataset-mwong/climate-evidence-related #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# ClimateBert-related
ClimateBert-related is a classifier model that predicts if climate related evidence is related to query claim. The model achieved F1 score of 81.90% with test dataset "mwong/climate-evidence-related". Using pretrained ClimateBert-f model, the classifier head is trained on Fever dataset and adapt... | [
"# ClimateBert-related\n\nClimateBert-related is a classifier model that predicts if climate related evidence is related to query claim. The model achieved F1 score of 81.90% with test dataset \"mwong/climate-evidence-related\". Using pretrained ClimateBert-f model, the classifier head is trained on Fever dataset a... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #text classification #fact checking #en #dataset-mwong/fever-evidence-related #dataset-mwong/climate-evidence-related #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# ClimateBert-related\n\nClimateBert-related is a classifier m... |
null | keras |
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'RMSprop', 'learning_rate... | {"library_name": "keras"} | samwell/english_to_twi | null | [
"keras",
"region:us"
] | null | 2022-04-20T12:12:53+00:00 | [] | [] | TAGS
#keras #region-us
| Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following h... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'RMSprop', 'learning\\_rate': 0.001, 'decay': 0.0, 'rho': 0.9, 'momentum': 0.0, 'epsilon': 1e-07, 'centered': False}\n* training\\_precision: float32\n\n\nTraining Metrics\n----------------\n\n\n\n... | [
"TAGS\n#keras #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'RMSprop', 'learning\\_rate': 0.001, 'decay': 0.0, 'rho': 0.9, 'momentum': 0.0, 'epsilon': 1e-07, 'centered': False}\n* training\\_precision: float32\n\n\nTraining M... |
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. -->
# indobert-distilled-optimized-for-classification
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingf... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["indonlu"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "indobert-distilled-optimized-for-classification", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "indonlu", "type": "i... | afbudiman/indobert-distilled-optimized-for-classification | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:indonlu",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-20T12:26:26+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-indonlu #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| indobert-distilled-optimized-for-classification
===============================================
This model is a fine-tuned version of distilbert-base-uncased on the indonlu dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5991
* Accuracy: 0.9024
* F1: 0.9021
Model description
----------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5.262995179171344e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 33\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-indonlu #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\\_rate: 5... |
automatic-speech-recognition | transformers | This is `facebook/wav2vec2-large-960h-lv60-self` enhanced with a Wikipedia language model.
The dataset used is `wikipedia/20200501.en`. All articles were used. It was cleaned of references and external links and all text inside of parantheses. It has 8092546 words.
The language model was built using KenLM. It is a 5-... | {} | gxbag/wav2vec2-large-960h-lv60-self-with-wikipedia-lm | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | null | 2022-04-20T12:34:18+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us
| This is 'facebook/wav2vec2-large-960h-lv60-self' enhanced with a Wikipedia language model.
The dataset used is 'wikipedia/URL'. All articles were used. It was cleaned of references and external links and all text inside of parantheses. It has 8092546 words.
The language model was built using KenLM. It is a 5-gram mod... | [] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers | language:
- en
tags:
- Table to text
- Data to text
## Dataset:
- [ToTTo](https://github.com/google-research-datasets/ToTTo)
A Controlled Table-to-Text Dataset. Totto is an open-source table-to-text dataset with over 1,20,000 examples in the English language. It defines a controlled generation task as: given a Wikip... | {"license": "apache-2.0"} | Tejas21/Totto_t5_base_BLEURT_24k_steps | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"arxiv:2004.04696",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-20T12:36:14+00:00 | [
"2004.04696"
] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #arxiv-2004.04696 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| language:
- en
tags:
- Table to text
- Data to text
## Dataset:
- ToTTo
A Controlled Table-to-Text Dataset. Totto is an open-source table-to-text dataset with over 1,20,000 examples in the English language. It defines a controlled generation task as: given a Wikipedia table and a set of highlighted cells, generate a... | [
"## Dataset:\n- ToTTo\nA Controlled Table-to-Text Dataset. Totto is an open-source table-to-text dataset with over 1,20,000 examples in the English language. It defines a controlled generation task as: given a Wikipedia table and a set of highlighted cells, generate a one-sentence description.",
"## Base Model - ... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #arxiv-2004.04696 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## Dataset:\n- ToTTo\nA Controlled Table-to-Text Dataset. Totto is an open-source table-to-text dataset with over 1,20,000 examples ... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion-bigger-batch-better-who-knows
This model is a fine-tuned version of [distilbert-base-u... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion-bigger-batch-better-who-knows", "results": []}]} | luquesky/distilbert-base-uncased-finetuned-emotion-bigger-batch-better-who-knows | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-20T12:44:33+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# distilbert-base-uncased-finetuned-emotion-bigger-batch-better-who-knows
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information n... | [
"# distilbert-base-uncased-finetuned-emotion-bigger-batch-better-who-knows\n\nThis model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# distilbert-base-uncased-finetuned-emotion-bigger-batch-better-who-knows\n\nThis model is a fine-tuned version of 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. -->
# xls-r-1b-bemba-15hrs
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "xls-r-1b-bemba-15hrs", "results": []}]} | csikasote/xls-r-1b-bemba-15hrs | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-20T12:46:33+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| xls-r-1b-bemba-15hrs
====================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2134
* Wer: 0.3485
Model description
-----------------
More information needed
Intended uses & limitations
------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 4\... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 765323474
- CO2 Emissions (in grams): 0.007501354635994803
## Validation Metrics
- Loss: 0.0447433702647686
- Accuracy: 0.9823788546255506
- Macro F1: 0.974405452470854
- Micro F1: 0.9823788546255506
- Weighted F1: 0.982304315317... | {"language": "en", "tags": "autotrain", "datasets": ["ktangri/autotrain-data-financial-sentiment"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 0.007501354635994803} | ktangri/autotrain-financial-sentiment-765323474 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain",
"en",
"dataset:ktangri/autotrain-data-financial-sentiment",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-20T13:34:03+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #autotrain #en #dataset-ktangri/autotrain-data-financial-sentiment #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 765323474
- CO2 Emissions (in grams): 0.007501354635994803
## Validation Metrics
- Loss: 0.0447433702647686
- Accuracy: 0.9823788546255506
- Macro F1: 0.974405452470854
- Micro F1: 0.9823788546255506
- Weighted F1: 0.982304315317... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 765323474\n- CO2 Emissions (in grams): 0.007501354635994803",
"## Validation Metrics\n\n- Loss: 0.0447433702647686\n- Accuracy: 0.9823788546255506\n- Macro F1: 0.974405452470854\n- Micro F1: 0.9823788546255506\n- Weighted ... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain #en #dataset-ktangri/autotrain-data-financial-sentiment #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 765323474\n- CO2 Emi... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Longformer_v5
This model is a fine-tuned version of [allenai/longformer-base-4096](https://huggingface.co/allenai/longformer-bas... | {"tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "Longformer_v5", "results": []}]} | brad1141/Longformer_v5 | null | [
"transformers",
"pytorch",
"tensorboard",
"longformer",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-20T13:39:26+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #longformer #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| Longformer\_v5
==============
This model is a fine-tuned version of allenai/longformer-base-4096 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7919
* Precision: 0.8516
* Recall: 0.8678
* F1: 0.6520
* Accuracy: 0.8259
Model description
-----------------
More information ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e... | [
"TAGS\n#transformers #pytorch #tensorboard #longformer #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 1\n* e... |
image-classification | transformers |
# rare-puppers
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/hugging... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | jmarshall/rare-puppers | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-20T13:45:13+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# rare-puppers
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images
#### corgi
!corgi
#### samoyed
!samoyed
#### shiba inu
!shiba inu | [
"# rare-puppers\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### corgi\n\n!corgi",
"#### samoyed\n\n!samoyed",
"#### shiba inu\n\n!shiba inu"
] | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# rare-puppers\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues ... |
text2text-generation | transformers |
<!-- 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. -->
# pegasus-samsum
This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_da... | {"tags": ["generated_from_trainer"], "datasets": ["samsum"], "model-index": [{"name": "pegasus-samsum", "results": []}]} | ffalcao/pegasus-samsum | null | [
"transformers",
"pytorch",
"tensorboard",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"dataset:samsum",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-20T13:47:55+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #pegasus #text2text-generation #generated_from_trainer #dataset-samsum #autotrain_compatible #endpoints_compatible #region-us
|
# pegasus-samsum
This model is a fine-tuned version of google/pegasus-cnn_dailymail on the samsum dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparam... | [
"# pegasus-samsum\n\nThis model is a fine-tuned version of google/pegasus-cnn_dailymail on the samsum dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedur... | [
"TAGS\n#transformers #pytorch #tensorboard #pegasus #text2text-generation #generated_from_trainer #dataset-samsum #autotrain_compatible #endpoints_compatible #region-us \n",
"# pegasus-samsum\n\nThis model is a fine-tuned version of google/pegasus-cnn_dailymail on the samsum dataset.",
"## Model description\n\n... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# deberta_amazon_reviews_v1
This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deb... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "deberta_amazon_reviews_v1", "results": []}]} | masapasa/deberta_amazon_reviews_v1 | null | [
"transformers",
"pytorch",
"tensorboard",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-20T14:10:01+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #deberta-v2 #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# deberta_amazon_reviews_v1
This model is a fine-tuned version of microsoft/deberta-v3-base on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hy... | [
"# deberta_amazon_reviews_v1\n\nThis model is a fine-tuned version of microsoft/deberta-v3-base on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training ... | [
"TAGS\n#transformers #pytorch #tensorboard #deberta-v2 #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# deberta_amazon_reviews_v1\n\nThis model is a fine-tuned version of microsoft/deberta-v3-base on an unknown dataset.",
"## Model descript... |
summarization | transformers |
# mT5-m2m-CrossSum
This repository contains the many-to-many (m2m) mT5 checkpoint finetuned on all cross-lingual pairs of the [CrossSum](https://huggingface.co/datasets/csebuetnlp/CrossSum) dataset. This model tries to **summarize text written in any language in the provided target language.** For finetuning details ... | {"language": ["am", "ar", "az", "bn", "my", "zh", "en", "fr", "gu", "ha", "hi", "ig", "id", "ja", "rn", "ko", "ky", "mr", "ne", "om", "ps", "fa", "pcm", "pt", "pa", "ru", "gd", "sr", "si", "so", "es", "sw", "ta", "te", "th", "ti", "tr", "uk", "ur", "uz", "vi", "cy", "yo"], "tags": ["summarization", "mT5"], "datasets": ... | csebuetnlp/mT5_m2m_crossSum | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"summarization",
"mT5",
"am",
"ar",
"az",
"bn",
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"zh",
"en",
"fr",
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"ko",
"ky",
"mr",
"ne",
"om",
"ps",
"fa",
"pcm",
"pt",
"pa",
"ru",
"gd",
"sr",... | null | 2022-04-20T14:11:49+00:00 | [
"2112.08804"
] | [
"am",
"ar",
"az",
"bn",
"my",
"zh",
"en",
"fr",
"gu",
"ha",
"hi",
"ig",
"id",
"ja",
"rn",
"ko",
"ky",
"mr",
"ne",
"om",
"ps",
"fa",
"pcm",
"pt",
"pa",
"ru",
"gd",
"sr",
"si",
"so",
"es",
"sw",
"ta",
"te",
"th",
"ti",
"tr",
"uk",
"ur",
"uz... | TAGS
#transformers #pytorch #mt5 #text2text-generation #summarization #mT5 #am #ar #az #bn #my #zh #en #fr #gu #ha #hi #ig #id #ja #rn #ko #ky #mr #ne #om #ps #fa #pcm #pt #pa #ru #gd #sr #si #so #es #sw #ta #te #th #ti #tr #uk #ur #uz #vi #cy #yo #dataset-csebuetnlp/CrossSum #arxiv-2112.08804 #autotrain_compatible #en... |
# mT5-m2m-CrossSum
This repository contains the many-to-many (m2m) mT5 checkpoint finetuned on all cross-lingual pairs of the CrossSum dataset. This model tries to summarize text written in any language in the provided target language. For finetuning details and scripts, see the paper and the official repository.
... | [
"# mT5-m2m-CrossSum\n\nThis repository contains the many-to-many (m2m) mT5 checkpoint finetuned on all cross-lingual pairs of the CrossSum dataset. This model tries to summarize text written in any language in the provided target language. For finetuning details and scripts, see the paper and the official repositor... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #summarization #mT5 #am #ar #az #bn #my #zh #en #fr #gu #ha #hi #ig #id #ja #rn #ko #ky #mr #ne #om #ps #fa #pcm #pt #pa #ru #gd #sr #si #so #es #sw #ta #te #th #ti #tr #uk #ur #uz #vi #cy #yo #dataset-csebuetnlp/CrossSum #arxiv-2112.08804 #autotrain_compatib... |
reinforcement-learning | ml-agents |
# **ppo** Agent playing **Huggy** 🐶
This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<r... | {"library_name": "ml-agents", "tags": ["unity-ml-agents", "ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy"]} | ThomasSimonini/ppo-Huggy | null | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] | null | 2022-04-20T14:29:08+00:00 | [] | [] | TAGS
#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Huggy #region-us
|
# ppo Agent playing Huggy
This is a trained model of a ppo agent playing Huggy using the Unity ML-Agents Library.
## Usage (with ML-Agents)
### Resume the training
### Watch your Agent
1. Move your model file into the environment Project
2. Open the Unity Editor, and select the scene.
3. Sel... | [
"# ppo Agent playing Huggy \n This is a trained model of a ppo agent playing Huggy using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n ### Resume the training\n \n ### Watch your Agent\n 1. Move your model file into the environment Project\n 2. Open the Unity Editor, and select the scene.\n ... | [
"TAGS\n#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Huggy #region-us \n",
"# ppo Agent playing Huggy \n This is a trained model of a ppo agent playing Huggy using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n ### Resume the trai... |
text-to-speech | espnet |
## ESPnet2 TTS model
### `espnet/GunnarThor_talromur_d_tacotron2`
This model was trained by Gunnar Thor using talromur recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 49a284e69308d81c142b89795de255b4ce290c54
pip install -e .
cd egs2/talromur/tt... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["talromur"]} | espnet/GunnarThor_talromur_d_tacotron2 | null | [
"espnet",
"audio",
"text-to-speech",
"en",
"dataset:talromur",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-04-20T14:33:22+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #text-to-speech #en #dataset-talromur #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## ESPnet2 TTS model
### 'espnet/GunnarThor_talromur_d_tacotron2'
This model was trained by Gunnar Thor using talromur recipe in espnet.
### Demo: How to use in ESPnet2
## TTS config
<details><summary>expand</summary>
</details>
### Citing ESPnet
or arXiv:
| [
"## ESPnet2 TTS model",
"### 'espnet/GunnarThor_talromur_d_tacotron2'\n\nThis model was trained by Gunnar Thor using talromur recipe in espnet.",
"### Demo: How to use in ESPnet2",
"## TTS config\n\n<details><summary>expand</summary>\n\n\n\n</details>",
"### Citing ESPnet\n\n\n\nor arXiv:"
] | [
"TAGS\n#espnet #audio #text-to-speech #en #dataset-talromur #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## ESPnet2 TTS model",
"### 'espnet/GunnarThor_talromur_d_tacotron2'\n\nThis model was trained by Gunnar Thor using talromur recipe in espnet.",
"### Demo: How to use in ESPnet2",
"## TTS config... |
text-to-speech | espnet |
## ESPnet2 TTS model
### `espnet/GunnarThor_talromur_d_fastspeech2`
This model was trained by Gunnar Thor using talromur recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 49a284e69308d81c142b89795de255b4ce290c54
pip install -e .
cd egs2/talromur/... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["talromur"]} | espnet/GunnarThor_talromur_d_fastspeech2 | null | [
"espnet",
"audio",
"text-to-speech",
"en",
"dataset:talromur",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-04-20T14:33:32+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #text-to-speech #en #dataset-talromur #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## ESPnet2 TTS model
### 'espnet/GunnarThor_talromur_d_fastspeech2'
This model was trained by Gunnar Thor using talromur recipe in espnet.
### Demo: How to use in ESPnet2
## TTS config
<details><summary>expand</summary>
</details>
### Citing ESPnet
or arXiv:
| [
"## ESPnet2 TTS model",
"### 'espnet/GunnarThor_talromur_d_fastspeech2'\n\nThis model was trained by Gunnar Thor using talromur recipe in espnet.",
"### Demo: How to use in ESPnet2",
"## TTS config\n\n<details><summary>expand</summary>\n\n\n\n</details>",
"### Citing ESPnet\n\n\n\nor arXiv:"
] | [
"TAGS\n#espnet #audio #text-to-speech #en #dataset-talromur #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## ESPnet2 TTS model",
"### 'espnet/GunnarThor_talromur_d_fastspeech2'\n\nThis model was trained by Gunnar Thor using talromur recipe in espnet.",
"### Demo: How to use in ESPnet2",
"## TTS conf... |
text-to-speech | espnet |
## ESPnet2 TTS model
### `espnet/GunnarThor_talromur_g_tacotron2`
This model was trained by Gunnar Thor using talromur recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 49a284e69308d81c142b89795de255b4ce290c54
pip install -e .
cd egs2/talromur/tt... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["talromur"]} | espnet/GunnarThor_talromur_g_tacotron2 | null | [
"espnet",
"audio",
"text-to-speech",
"en",
"dataset:talromur",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-04-20T14:35:25+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #text-to-speech #en #dataset-talromur #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## ESPnet2 TTS model
### 'espnet/GunnarThor_talromur_g_tacotron2'
This model was trained by Gunnar Thor using talromur recipe in espnet.
### Demo: How to use in ESPnet2
## TTS config
<details><summary>expand</summary>
</details>
### Citing ESPnet
or arXiv:
| [
"## ESPnet2 TTS model",
"### 'espnet/GunnarThor_talromur_g_tacotron2'\n\nThis model was trained by Gunnar Thor using talromur recipe in espnet.",
"### Demo: How to use in ESPnet2",
"## TTS config\n\n<details><summary>expand</summary>\n\n\n\n</details>",
"### Citing ESPnet\n\n\n\nor arXiv:"
] | [
"TAGS\n#espnet #audio #text-to-speech #en #dataset-talromur #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## ESPnet2 TTS model",
"### 'espnet/GunnarThor_talromur_g_tacotron2'\n\nThis model was trained by Gunnar Thor using talromur recipe in espnet.",
"### Demo: How to use in ESPnet2",
"## TTS config... |
text-to-speech | espnet |
## ESPnet2 TTS model
### `espnet/GunnarThor_talromur_g_fastspeech2`
This model was trained by Gunnar Thor using talromur recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 49a284e69308d81c142b89795de255b4ce290c54
pip install -e .
cd egs2/talromur/... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["talromur"]} | espnet/GunnarThor_talromur_g_fastspeech2 | null | [
"espnet",
"audio",
"text-to-speech",
"en",
"dataset:talromur",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-04-20T14:35:34+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #text-to-speech #en #dataset-talromur #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## ESPnet2 TTS model
### 'espnet/GunnarThor_talromur_g_fastspeech2'
This model was trained by Gunnar Thor using talromur recipe in espnet.
### Demo: How to use in ESPnet2
## TTS config
<details><summary>expand</summary>
</details>
### Citing ESPnet
or arXiv:
| [
"## ESPnet2 TTS model",
"### 'espnet/GunnarThor_talromur_g_fastspeech2'\n\nThis model was trained by Gunnar Thor using talromur recipe in espnet.",
"### Demo: How to use in ESPnet2",
"## TTS config\n\n<details><summary>expand</summary>\n\n\n\n</details>",
"### Citing ESPnet\n\n\n\nor arXiv:"
] | [
"TAGS\n#espnet #audio #text-to-speech #en #dataset-talromur #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## ESPnet2 TTS model",
"### 'espnet/GunnarThor_talromur_g_fastspeech2'\n\nThis model was trained by Gunnar Thor using talromur recipe in espnet.",
"### Demo: How to use in ESPnet2",
"## TTS conf... |
null | null |
### How to use
You can use this model directly with a pipeline for video question answering:
### Evaluation results
### BibTeX entry and citation info | {"license": "afl-3.0"} | Awiny/All-in-one | null | [
"license:afl-3.0",
"region:us"
] | null | 2022-04-20T14:36:11+00:00 | [] | [] | TAGS
#license-afl-3.0 #region-us
|
### How to use
You can use this model directly with a pipeline for video question answering:
### Evaluation results
### BibTeX entry and citation info | [
"### How to use\nYou can use this model directly with a pipeline for video question answering:",
"### Evaluation results",
"### BibTeX entry and citation info"
] | [
"TAGS\n#license-afl-3.0 #region-us \n",
"### How to use\nYou can use this model directly with a pipeline for video question answering:",
"### Evaluation results",
"### BibTeX entry and citation info"
] |
text-to-speech | espnet |
## ESPnet2 TTS model
### `espnet/GunnarThor_talromur_h_tacotron2`
This model was trained by Gunnar Thor using talromur recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 49a284e69308d81c142b89795de255b4ce290c54
pip install -e .
cd egs2/talromur/tt... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["talromur"]} | espnet/GunnarThor_talromur_h_tacotron2 | null | [
"espnet",
"audio",
"text-to-speech",
"en",
"dataset:talromur",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-04-20T14:36:41+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #text-to-speech #en #dataset-talromur #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## ESPnet2 TTS model
### 'espnet/GunnarThor_talromur_h_tacotron2'
This model was trained by Gunnar Thor using talromur recipe in espnet.
### Demo: How to use in ESPnet2
## TTS config
<details><summary>expand</summary>
</details>
### Citing ESPnet
or arXiv:
| [
"## ESPnet2 TTS model",
"### 'espnet/GunnarThor_talromur_h_tacotron2'\n\nThis model was trained by Gunnar Thor using talromur recipe in espnet.",
"### Demo: How to use in ESPnet2",
"## TTS config\n\n<details><summary>expand</summary>\n\n\n\n</details>",
"### Citing ESPnet\n\n\n\nor arXiv:"
] | [
"TAGS\n#espnet #audio #text-to-speech #en #dataset-talromur #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## ESPnet2 TTS model",
"### 'espnet/GunnarThor_talromur_h_tacotron2'\n\nThis model was trained by Gunnar Thor using talromur recipe in espnet.",
"### Demo: How to use in ESPnet2",
"## TTS config... |
text-to-speech | espnet |
## ESPnet2 TTS model
### `espnet/GunnarThor_talromur_h_fastspeech2`
This model was trained by Gunnar Thor using talromur recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 49a284e69308d81c142b89795de255b4ce290c54
pip install -e .
cd egs2/talromur/... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["talromur"]} | espnet/GunnarThor_talromur_h_fastspeech2 | null | [
"espnet",
"audio",
"text-to-speech",
"en",
"dataset:talromur",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-04-20T14:37:06+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #text-to-speech #en #dataset-talromur #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## ESPnet2 TTS model
### 'espnet/GunnarThor_talromur_h_fastspeech2'
This model was trained by Gunnar Thor using talromur recipe in espnet.
### Demo: How to use in ESPnet2
## TTS config
<details><summary>expand</summary>
</details>
### Citing ESPnet
or arXiv:
| [
"## ESPnet2 TTS model",
"### 'espnet/GunnarThor_talromur_h_fastspeech2'\n\nThis model was trained by Gunnar Thor using talromur recipe in espnet.",
"### Demo: How to use in ESPnet2",
"## TTS config\n\n<details><summary>expand</summary>\n\n\n\n</details>",
"### Citing ESPnet\n\n\n\nor arXiv:"
] | [
"TAGS\n#espnet #audio #text-to-speech #en #dataset-talromur #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## ESPnet2 TTS model",
"### 'espnet/GunnarThor_talromur_h_fastspeech2'\n\nThis model was trained by Gunnar Thor using talromur recipe in espnet.",
"### Demo: How to use in ESPnet2",
"## TTS conf... |
summarization | transformers |
# mT5-m2o-hindi-CrossSum
This repository contains the many-to-one (m2o) mT5 checkpoint finetuned on all cross-lingual pairs of the [CrossSum](https://huggingface.co/datasets/csebuetnlp/CrossSum) dataset, where the target summary was in **hindi**, i.e. this model tries to **summarize text written in any language in Hi... | {"language": ["am", "ar", "az", "bn", "my", "zh", "en", "fr", "gu", "ha", "hi", "ig", "id", "ja", "rn", "ko", "ky", "mr", "ne", "om", "ps", "fa", "pcm", "pt", "pa", "ru", "gd", "sr", "si", "so", "es", "sw", "ta", "te", "th", "ti", "tr", "uk", "ur", "uz", "vi", "cy", "yo"], "tags": ["summarization", "mT5"], "licenses": ... | csebuetnlp/mT5_m2o_hindi_crossSum | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"summarization",
"mT5",
"am",
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"az",
"bn",
"my",
"zh",
"en",
"fr",
"gu",
"ha",
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"ja",
"rn",
"ko",
"ky",
"mr",
"ne",
"om",
"ps",
"fa",
"pcm",
"pt",
"pa",
"ru",
"gd",
"sr",... | null | 2022-04-20T14:41:03+00:00 | [
"2112.08804"
] | [
"am",
"ar",
"az",
"bn",
"my",
"zh",
"en",
"fr",
"gu",
"ha",
"hi",
"ig",
"id",
"ja",
"rn",
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"ky",
"mr",
"ne",
"om",
"ps",
"fa",
"pcm",
"pt",
"pa",
"ru",
"gd",
"sr",
"si",
"so",
"es",
"sw",
"ta",
"te",
"th",
"ti",
"tr",
"uk",
"ur",
"uz... | TAGS
#transformers #pytorch #mt5 #text2text-generation #summarization #mT5 #am #ar #az #bn #my #zh #en #fr #gu #ha #hi #ig #id #ja #rn #ko #ky #mr #ne #om #ps #fa #pcm #pt #pa #ru #gd #sr #si #so #es #sw #ta #te #th #ti #tr #uk #ur #uz #vi #cy #yo #arxiv-2112.08804 #autotrain_compatible #endpoints_compatible #text-gene... |
# mT5-m2o-hindi-CrossSum
This repository contains the many-to-one (m2o) mT5 checkpoint finetuned on all cross-lingual pairs of the CrossSum dataset, where the target summary was in hindi, i.e. this model tries to summarize text written in any language in Hindi. For finetuning details and scripts, see the paper and th... | [
"# mT5-m2o-hindi-CrossSum\n\nThis repository contains the many-to-one (m2o) mT5 checkpoint finetuned on all cross-lingual pairs of the CrossSum dataset, where the target summary was in hindi, i.e. this model tries to summarize text written in any language in Hindi. For finetuning details and scripts, see the paper ... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #summarization #mT5 #am #ar #az #bn #my #zh #en #fr #gu #ha #hi #ig #id #ja #rn #ko #ky #mr #ne #om #ps #fa #pcm #pt #pa #ru #gd #sr #si #so #es #sw #ta #te #th #ti #tr #uk #ur #uz #vi #cy #yo #arxiv-2112.08804 #autotrain_compatible #endpoints_compatible #tex... |
fill-mask | transformers | ## albert-base-japanese-v1-with-japanese
日本語事前学習済みALBERTモデルです
このモデルではTokenizerに[BertJapaneseTokenizerクラス](https://huggingface.co/docs/transformers/main/en/model_doc/bert-japanese#transformers.BertJapaneseTokenizer)を利用しています
[albert-base-japanese-v1](https://huggingface.co/ken11/albert-base-japanese-v1)よりトークナイズ処理が楽にな... | {"language": ["ja"], "license": "mit", "tags": ["fill-mask", "japanese", "albert"], "widget": [{"text": "\u660e\u65e5\u306f\u660e\u65e5\u306e[MASK]\u304c\u5439\u304f"}]} | ken11/albert-base-japanese-v1-with-japanese-tokenizer | null | [
"transformers",
"pytorch",
"tf",
"albert",
"fill-mask",
"japanese",
"ja",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-20T15:34:22+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #tf #albert #fill-mask #japanese #ja #license-mit #autotrain_compatible #endpoints_compatible #region-us
| ## albert-base-japanese-v1-with-japanese
日本語事前学習済みALBERTモデルです
このモデルではTokenizerにBertJapaneseTokenizerクラスを利用しています
albert-base-japanese-v1よりトークナイズ処理が楽になっています
## How to use
### ファインチューニング
このモデルはPreTrainedモデルです
基本的には各種タスク用にファインチューニングして使用されることを想定しています
### Fill-Mask
#### for PyTorch
#### for TensorFlow
## Trai... | [
"## albert-base-japanese-v1-with-japanese\n日本語事前学習済みALBERTモデルです \nこのモデルではTokenizerにBertJapaneseTokenizerクラスを利用しています \nalbert-base-japanese-v1よりトークナイズ処理が楽になっています",
"## How to use",
"### ファインチューニング\nこのモデルはPreTrainedモデルです \n基本的には各種タスク用にファインチューニングして使用されることを想定しています",
"### Fill-Mask",
"#### for PyTorch",
"##... | [
"TAGS\n#transformers #pytorch #tf #albert #fill-mask #japanese #ja #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"## albert-base-japanese-v1-with-japanese\n日本語事前学習済みALBERTモデルです \nこのモデルではTokenizerにBertJapaneseTokenizerクラスを利用しています \nalbert-base-japanese-v1よりトークナイズ処理が楽になっています",
"## How... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-colab2
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/w... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-colab2", "results": []}]} | obokkkk/wav2vec2-base-timit-demo-colab2 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-20T16:01:26+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-timit-demo-colab2
===============================
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: 0.4805
* Wer: 0.3398
Model description
-----------------
More information needed
Intended uses & li... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 3... |
text2text-generation | transformers |
# T5-tiny-nl6 for Finnish
Pretrained T5 model on Finnish language using a span-based masked language modeling (MLM) objective. T5 was introduced in
[this paper](https://arxiv.org/abs/1910.10683)
and first released at [this page](https://github.com/google-research/text-to-text-transfer-transformer).
**Note:** The Hug... | {"language": ["fi"], "license": "apache-2.0", "tags": ["finnish", "t5", "t5x", "seq2seq"], "datasets": ["Finnish-NLP/mc4_fi_cleaned", "wikipedia"], "inference": false} | Finnish-NLP/t5-tiny-nl6-finnish | null | [
"transformers",
"pytorch",
"jax",
"tensorboard",
"t5",
"text2text-generation",
"finnish",
"t5x",
"seq2seq",
"fi",
"dataset:Finnish-NLP/mc4_fi_cleaned",
"dataset:wikipedia",
"arxiv:1910.10683",
"arxiv:2002.05202",
"arxiv:2109.10686",
"license:apache-2.0",
"autotrain_compatible",
"te... | null | 2022-04-20T16:20:01+00:00 | [
"1910.10683",
"2002.05202",
"2109.10686"
] | [
"fi"
] | TAGS
#transformers #pytorch #jax #tensorboard #t5 #text2text-generation #finnish #t5x #seq2seq #fi #dataset-Finnish-NLP/mc4_fi_cleaned #dataset-wikipedia #arxiv-1910.10683 #arxiv-2002.05202 #arxiv-2109.10686 #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us
| T5-tiny-nl6 for Finnish
=======================
Pretrained T5 model on Finnish language using a span-based masked language modeling (MLM) objective. T5 was introduced in
this paper
and first released at this page.
Note: The Hugging Face inference widget is deactivated because this model needs a text-to-text fine-tu... | [
"### How to use\n\n\nHere is how to use this model in PyTorch:\n\n\nand in TensorFlow:",
"### Limitations and bias\n\n\nThe training data used for this model contains a lot of unfiltered content from the internet, which is far from neutral. Therefore, the model can have biased predictions. This bias will also aff... | [
"TAGS\n#transformers #pytorch #jax #tensorboard #t5 #text2text-generation #finnish #t5x #seq2seq #fi #dataset-Finnish-NLP/mc4_fi_cleaned #dataset-wikipedia #arxiv-1910.10683 #arxiv-2002.05202 #arxiv-2109.10686 #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us \n",
"### How to use\n\n... |
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