pipeline_tag stringclasses 48
values | library_name stringclasses 198
values | text stringlengths 1 900k | metadata stringlengths 2 438k | id stringlengths 5 122 | last_modified null | tags listlengths 1 1.84k | sha null | created_at stringlengths 25 25 | arxiv listlengths 0 201 | languages listlengths 0 1.83k | tags_str stringlengths 17 9.34k | text_str stringlengths 0 389k | text_lists listlengths 0 722 | processed_texts listlengths 1 723 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
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"} | jfarray/Model_paraphrase-multilingual-mpnet-base-v2_30_Epochs | null | [
"sentence-transformers",
"pytorch",
"xlm-roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #xlm-roberta #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 #xlm-roberta #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 clusterin... |
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"} | jfarray/Model_paraphrase-multilingual-mpnet-base-v2_50_Epochs | null | [
"sentence-transformers",
"pytorch",
"xlm-roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #xlm-roberta #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 #xlm-roberta #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 clusterin... |
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"} | jfarray/Model_paraphrase-multilingual-mpnet-base-v2_5_Epochs | null | [
"sentence-transformers",
"pytorch",
"xlm-roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #xlm-roberta #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 #xlm-roberta #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 clusterin... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# MTL-bert-base-uncased-ww-squad
This model is a fine-tuned version of [jgammack/MTL-bert-base-uncased-ww](https://huggingface.co/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "MTL-bert-base-uncased-ww-squad", "results": []}]} | jgammack/MTL-bert-base-uncased-ww-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# MTL-bert-base-uncased-ww-squad
This model is a fine-tuned version of jgammack/MTL-bert-base-uncased-ww on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
###... | [
"# MTL-bert-base-uncased-ww-squad\n\nThis model is a fine-tuned version of jgammack/MTL-bert-base-uncased-ww on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MTL-bert-base-uncased-ww-squad\n\nThis model is a fine-tuned version of jgammack/MTL-bert-base-uncased-ww on the squad dataset.",
"## Model des... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# MTL-bert-base-uncased-ww
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "MTL-bert-base-uncased-ww", "results": []}]} | jgammack/MTL-bert-base-uncased-ww | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| MTL-bert-base-uncased-ww
========================
This model is a fine-tuned version of bert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 2.5261
Model description
-----------------
More information needed
Intended uses & limitations
-----------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 7\n* eval\\_batch\\_size: 7\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 15\n* mixed\\_preci... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #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\\_batch\\_size: 7\n... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# MTL-bert-base-uncased
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "MTL-bert-base-uncased", "results": []}]} | jgammack/MTL-bert-base-uncased | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| MTL-bert-base-uncased
=====================
This model is a fine-tuned version of bert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.9283
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 7\n* eval\\_batch\\_size: 7\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 15\n* mixed\\_preci... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #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\\_batch\\_size: 7\n... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# MTL-distilbert-base-uncased-squad
This model is a fine-tuned version of [jgammack/MTL-distilbert-base-uncased](https://huggingfa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "MTL-distilbert-base-uncased-squad", "results": []}]} | jgammack/MTL-distilbert-base-uncased-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# MTL-distilbert-base-uncased-squad
This model is a fine-tuned version of jgammack/MTL-distilbert-base-uncased on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedur... | [
"# MTL-distilbert-base-uncased-squad\n\nThis model is a fine-tuned version of jgammack/MTL-distilbert-base-uncased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MTL-distilbert-base-uncased-squad\n\nThis model is a fine-tuned version of jgammack/MTL-distilbert-base-uncased on the squad dataset.",
"... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# MTL-distilbert-base-uncased
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-ba... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "MTL-distilbert-base-uncased", "results": []}]} | jgammack/MTL-distilbert-base-uncased | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| MTL-distilbert-base-uncased
===========================
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 2.0874
Model description
-----------------
More information needed
Intended uses & limitations
-----------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 7\n* eval\\_batch\\_size: 7\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 15\n* mixed\\_preci... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #fill-mask #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\\_batch\\_siz... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# MTL-roberta-base
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "MTL-roberta-base", "results": []}]} | jgammack/MTL-roberta-base | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| MTL-roberta-base
================
This model is a fine-tuned version of roberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.4859
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More informati... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 7\n* eval\\_batch\\_size: 7\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 15\n* mixed\\_preci... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 7\n* ev... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SAE-bert-base-uncased
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "widget": [{"text": "Wind [MASK] was detected coming from the car door closure system.", "example_title": "Closure system"}], "model-index": [{"name": "SAE-bert-base-uncased", "results": []}]} | jgammack/SAE-bert-base-uncased | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| SAE-bert-base-uncased
=====================
This model is a fine-tuned version of bert-base-uncased on the jgammack/SAE-door-abstracts dataset.
It achieves the following results on the evaluation set:
* Loss: 2.1256
Model description
-----------------
More information needed
Intended uses & limitations
----... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 7\n* eval\\_batch\\_size: 7\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 15\n* mixed\\_preci... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #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\\_batch\\_size: 7\n... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SAE-distilbert-base-uncased-squad
This model is a fine-tuned version of [jgammack/SAE-distilbert-base-uncased](https://huggingfa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "SAE-distilbert-base-uncased-squad", "results": []}]} | jgammack/SAE-distilbert-base-uncased-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# SAE-distilbert-base-uncased-squad
This model is a fine-tuned version of jgammack/SAE-distilbert-base-uncased on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedur... | [
"# SAE-distilbert-base-uncased-squad\n\nThis model is a fine-tuned version of jgammack/SAE-distilbert-base-uncased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# SAE-distilbert-base-uncased-squad\n\nThis model is a fine-tuned version of jgammack/SAE-distilbert-base-uncased on the squad dataset.",
"... |
fill-mask | transformers |
# SAE-distilbert-base-uncased
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the [jgammack/SAE-door-abstracts](https://huggingface.co/datasets/jgammack/SAE-door-abstracts) dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "widget": [{"text": "Wind noise was detected coming from the car [MASK] closure system.", "example_title": "Closure system"}], "model-index": [{"name": "SAE-distilbert-base-uncased", "results": []}]} | jgammack/SAE-distilbert-base-uncased | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| SAE-distilbert-base-uncased
===========================
This model is a fine-tuned version of distilbert-base-uncased on the jgammack/SAE-door-abstracts dataset.
It achieves the following results on the evaluation set:
* Loss: 2.2970
### Training hyperparameters
The following hyperparameters were used during ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 15\n* eval\\_batch\\_size: 15\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 15\n* mixed\\_pre... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #fill-mask #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\\_batch\\_siz... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SAE-roberta-base-squad
This model is a fine-tuned version of [jgammack/SAE-roberta-base](https://huggingface.co/jgammack/SAE-rob... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "SAE-roberta-base-squad", "results": []}]} | jgammack/SAE-roberta-base-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #question-answering #generated_from_trainer #dataset-squad #license-mit #endpoints_compatible #region-us
|
# SAE-roberta-base-squad
This model is a fine-tuned version of jgammack/SAE-roberta-base on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperp... | [
"# SAE-roberta-base-squad\n\nThis model is a fine-tuned version of jgammack/SAE-roberta-base on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training proc... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #question-answering #generated_from_trainer #dataset-squad #license-mit #endpoints_compatible #region-us \n",
"# SAE-roberta-base-squad\n\nThis model is a fine-tuned version of jgammack/SAE-roberta-base on the squad dataset.",
"## Model description\n\nMore inf... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SAE-roberta-base
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "SAE-roberta-base", "results": []}]} | jgammack/SAE-roberta-base | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| SAE-roberta-base
================
This model is a fine-tuned version of roberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.6959
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More informati... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 7\n* eval\\_batch\\_size: 7\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 15\n* mixed\\_preci... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 7\n* ev... |
sentence-similarity | sentence-transformers |
# jgammack/distilbert-base-mean-pooling
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... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | jgammack/distilbert-base-mean-pooling | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# jgammack/distilbert-base-mean-pooling
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 ins... | [
"# jgammack/distilbert-base-mean-pooling\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-transf... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# jgammack/distilbert-base-mean-pooling\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used ... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "distilbert-base-uncased-squad", "results": []}]} | jgammack/distilbert-base-uncased-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# distilbert-base-uncased-squad
This model is a fine-tuned version of distilbert-base-uncased on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training h... | [
"# distilbert-base-uncased-squad\n\nThis model is a fine-tuned version of distilbert-base-uncased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# distilbert-base-uncased-squad\n\nThis model is a fine-tuned version of distilbert-base-uncased on the squad dataset.",
"## Model descript... |
sentence-similarity | sentence-transformers |
# jgammack/multi-qa-MTL-distilbert-base-uncased-40k
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)... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | jgammack/multi-qa-MTL-distilbert-base-uncased-40k | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# jgammack/multi-qa-MTL-distilbert-base-uncased-40k
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-tran... | [
"# jgammack/multi-qa-MTL-distilbert-base-uncased-40k\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 sen... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# jgammack/multi-qa-MTL-distilbert-base-uncased-40k\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and ... |
sentence-similarity | sentence-transformers |
# jgammack/multi-qa-MTL-distilbert-base-uncased
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)
Us... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | jgammack/multi-qa-MTL-distilbert-base-uncased | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# jgammack/multi-qa-MTL-distilbert-base-uncased
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transfor... | [
"# jgammack/multi-qa-MTL-distilbert-base-uncased\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentenc... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# jgammack/multi-qa-MTL-distilbert-base-uncased\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can ... |
sentence-similarity | sentence-transformers |
# jgammack/multi-qa-SAE-distilbert-base
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | jgammack/multi-qa-SAE-distilbert-base-uncased | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# jgammack/multi-qa-SAE-distilbert-base
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers ins... | [
"# jgammack/multi-qa-SAE-distilbert-base\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transf... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# jgammack/multi-qa-SAE-distilbert-base\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used ... |
sentence-similarity | sentence-transformers |
# jgammack/multi-qa-distilbert-base-uncased
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 ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | jgammack/multi-qa-distilbert-base-uncased | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# jgammack/multi-qa-distilbert-base-uncased
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... | [
"# jgammack/multi-qa-distilbert-base-uncased\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-tr... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# jgammack/multi-qa-distilbert-base-uncased\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be u... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-squad
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the squad datase... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "roberta-base-squad", "results": []}]} | jgammack/roberta-base-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #question-answering #generated_from_trainer #dataset-squad #license-mit #endpoints_compatible #region-us
|
# roberta-base-squad
This model is a fine-tuned version of roberta-base on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The fo... | [
"# roberta-base-squad\n\nThis model is a fine-tuned version of roberta-base on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Tr... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #question-answering #generated_from_trainer #dataset-squad #license-mit #endpoints_compatible #region-us \n",
"# roberta-base-squad\n\nThis model is a fine-tuned version of roberta-base on the squad dataset.",
"## Model description\n\nMore information needed",... |
sentence-similarity | sentence-transformers |
# This model is superseded by [https://github.com/ORNL/affinity_pred](https://github.com/ORNL/affinity_pred)
# jglaser/protein-ligand-mlp-1
This is a [sentence-transformers](https://www.SBERT.net) model: It maps pairs of protein and chemical sequences (canonical SMILES) onto binding affinities (pIC50 values).
Each ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"} | jglaser/protein-ligand-mlp-1 | null | [
"sentence-transformers",
"feature-extraction",
"sentence-similarity",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #feature-extraction #sentence-similarity #endpoints_compatible #region-us
|
# This model is superseded by URL
# jglaser/protein-ligand-mlp-1
This is a sentence-transformers model: It maps pairs of protein and chemical sequences (canonical SMILES) onto binding affinities (pIC50 values).
Each member of the ensemble has been trained using a different seed and you can use the different models ... | [
"# This model is superseded by URL",
"# jglaser/protein-ligand-mlp-1\n\nThis is a sentence-transformers model: It maps pairs of protein and chemical sequences (canonical SMILES) onto binding affinities (pIC50 values).\n\nEach member of the ensemble has been trained using a different seed and you can use the diffe... | [
"TAGS\n#sentence-transformers #feature-extraction #sentence-similarity #endpoints_compatible #region-us \n",
"# This model is superseded by URL",
"# jglaser/protein-ligand-mlp-1\n\nThis is a sentence-transformers model: It maps pairs of protein and chemical sequences (canonical SMILES) onto binding affinities (... |
sentence-similarity | sentence-transformers |
# This model is superseded by [https://github.com/ORNL/affinity_pred](https://github.com/ORNL/affinity_pred)
# jglaser/protein-ligand-mlp-2
This is a [sentence-transformers](https://www.SBERT.net) model: It maps pairs of protein and chemical sequences (canonical SMILES) onto binding affinities (pIC50 values).
Each ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"} | jglaser/protein-ligand-mlp-2 | null | [
"sentence-transformers",
"feature-extraction",
"sentence-similarity",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #feature-extraction #sentence-similarity #endpoints_compatible #region-us
|
# This model is superseded by URL
# jglaser/protein-ligand-mlp-2
This is a sentence-transformers model: It maps pairs of protein and chemical sequences (canonical SMILES) onto binding affinities (pIC50 values).
Each member of the ensemble has been trained using a different seed and you can use the different models ... | [
"# This model is superseded by URL",
"# jglaser/protein-ligand-mlp-2\n\nThis is a sentence-transformers model: It maps pairs of protein and chemical sequences (canonical SMILES) onto binding affinities (pIC50 values).\n\nEach member of the ensemble has been trained using a different seed and you can use the diffe... | [
"TAGS\n#sentence-transformers #feature-extraction #sentence-similarity #endpoints_compatible #region-us \n",
"# This model is superseded by URL",
"# jglaser/protein-ligand-mlp-2\n\nThis is a sentence-transformers model: It maps pairs of protein and chemical sequences (canonical SMILES) onto binding affinities (... |
sentence-similarity | sentence-transformers |
# This model is superseded by [https://github.com/ORNL/affinity_pred](https://github.com/ORNL/affinity_pred)
# jglaser/protein-ligand-mlp-3
This is a [sentence-transformers](https://www.SBERT.net) model: It maps pairs of protein and chemical sequences (canonical SMILES) onto binding affinities (pIC50 values).
Each ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"} | jglaser/protein-ligand-mlp-3 | null | [
"sentence-transformers",
"feature-extraction",
"sentence-similarity",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #feature-extraction #sentence-similarity #endpoints_compatible #region-us
|
# This model is superseded by URL
# jglaser/protein-ligand-mlp-3
This is a sentence-transformers model: It maps pairs of protein and chemical sequences (canonical SMILES) onto binding affinities (pIC50 values).
Each member of the ensemble has been trained using a different seed and you can use the different models ... | [
"# This model is superseded by URL",
"# jglaser/protein-ligand-mlp-3\n\nThis is a sentence-transformers model: It maps pairs of protein and chemical sequences (canonical SMILES) onto binding affinities (pIC50 values).\n\nEach member of the ensemble has been trained using a different seed and you can use the diffe... | [
"TAGS\n#sentence-transformers #feature-extraction #sentence-similarity #endpoints_compatible #region-us \n",
"# This model is superseded by URL",
"# jglaser/protein-ligand-mlp-3\n\nThis is a sentence-transformers model: It maps pairs of protein and chemical sequences (canonical SMILES) onto binding affinities (... |
sentence-similarity | sentence-transformers |
# jhemmingsson/lab2
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 ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | jhemmingsson/lab2 | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# jhemmingsson/lab2
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 ... | [
"# jhemmingsson/lab2\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\... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# jhemmingsson/lab2\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 clust... |
sentence-similarity | sentence-transformers |
# ko-sbert-multitask
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... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | jhgan/ko-sbert-multitask | null | [
"sentence-transformers",
"pytorch",
"tf",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #tf #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# ko-sbert-multitask
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... | [
"# ko-sbert-multitask\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... | [
"TAGS\n#sentence-transformers #pytorch #tf #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# ko-sbert-multitask\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 cluste... |
sentence-similarity | sentence-transformers |
# ko-sbert-nli
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"} | jhgan/ko-sbert-nli | null | [
"sentence-transformers",
"pytorch",
"tf",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #tf #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# ko-sbert-nli
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... | [
"# ko-sbert-nli\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 #tf #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# ko-sbert-nli\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 o... |
sentence-similarity | sentence-transformers |
# ko-sbert-sts
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"} | jhgan/ko-sbert-sts | null | [
"sentence-transformers",
"pytorch",
"tf",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #tf #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# ko-sbert-sts
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... | [
"# ko-sbert-sts\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 #tf #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# ko-sbert-sts\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 o... |
sentence-similarity | sentence-transformers |
# ko-sroberta-multitask
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 e... | {"language": "ko", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | jhgan/ko-sroberta-multitask | null | [
"sentence-transformers",
"pytorch",
"tf",
"roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"ko",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ko"
] | TAGS
#sentence-transformers #pytorch #tf #roberta #feature-extraction #sentence-similarity #transformers #ko #endpoints_compatible #has_space #region-us
|
# ko-sroberta-multitask
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 ... | [
"# ko-sroberta-multitask\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... | [
"TAGS\n#sentence-transformers #pytorch #tf #roberta #feature-extraction #sentence-similarity #transformers #ko #endpoints_compatible #has_space #region-us \n",
"# ko-sroberta-multitask\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used ... |
sentence-similarity | sentence-transformers |
# ko-sroberta-nli
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 wh... | {"language": "ko", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | jhgan/ko-sroberta-nli | null | [
"sentence-transformers",
"pytorch",
"tf",
"roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"ko",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ko"
] | TAGS
#sentence-transformers #pytorch #tf #roberta #feature-extraction #sentence-similarity #transformers #ko #endpoints_compatible #region-us
|
# ko-sroberta-nli
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 ca... | [
"# ko-sroberta-nli\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\... | [
"TAGS\n#sentence-transformers #pytorch #tf #roberta #feature-extraction #sentence-similarity #transformers #ko #endpoints_compatible #region-us \n",
"# ko-sroberta-nli\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 cl... |
sentence-similarity | sentence-transformers |
# ko-sroberta-sts
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 wh... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | jhgan/ko-sroberta-sts | null | [
"sentence-transformers",
"pytorch",
"tf",
"roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #tf #roberta #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# ko-sroberta-sts
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 ca... | [
"# ko-sroberta-sts\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\... | [
"TAGS\n#sentence-transformers #pytorch #tf #roberta #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# ko-sroberta-sts\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 cluste... |
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-guarani-small
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface... | {"license": "apache-2.0", "tags": ["generated_from_trainer", "robust-speech-event", "hf-asr-leaderboard"], "datasets": ["common_voice", "gn"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-guarani-small", "results": []}]} | jhonparra18/wav2vec2-large-xls-r-300m-guarani-small | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"robust-speech-event",
"hf-asr-leaderboard",
"dataset:common_voice",
"dataset:gn",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #robust-speech-event #hf-asr-leaderboard #dataset-common_voice #dataset-gn #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xls-r-300m-guarani-small
=======================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4964
* Wer: 0.5957
Model description
-----------------
More informat... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\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 #robust-speech-event #hf-asr-leaderboard #dataset-common_voice #dataset-gn #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used... |
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-spanish-custom
This model was trained from scratch on the common_voice dataset.
It achieves the follow... | {"tags": ["generated_from_trainer", "robust-speech-event"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-spanish-custom", "results": []}]} | jhonparra18/wav2vec2-large-xls-r-300m-spanish-custom | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"robust-speech-event",
"dataset:common_voice",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #robust-speech-event #dataset-common_voice #endpoints_compatible #region-us
|
# wav2vec2-large-xls-r-300m-spanish-custom
This model was trained from scratch on the common_voice dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.2245
- eval_wer: 0.2082
- eval_runtime: 801.6784
- eval_samples_per_second: 18.822
- eval_steps_per_second: 2.354
- epoch: 0.76
- step: ... | [
"# wav2vec2-large-xls-r-300m-spanish-custom\n\nThis model was trained from scratch on the common_voice dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 0.2245\n- eval_wer: 0.2082\n- eval_runtime: 801.6784\n- eval_samples_per_second: 18.822\n- eval_steps_per_second: 2.354\n- epoch: 0.... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #robust-speech-event #dataset-common_voice #endpoints_compatible #region-us \n",
"# wav2vec2-large-xls-r-300m-spanish-custom\n\nThis model was trained from scratch on the common_voice dataset.\nIt achieves the following ... |
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-spanish-large
This model is a fine-tuned version of [tomascufaro/xls-r-es-test](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer", "es", "robust-speech-event"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-spanish-large", "results": []}]} | jhonparra18/wav2vec2-xls-r-300m-spanish-large-noLM | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"es",
"robust-speech-event",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #es #robust-speech-event #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xls-r-300m-spanish-large
=======================================
This model is a fine-tuned version of tomascufaro/xls-r-es-test on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1431
* Wer: 0.1197
Model description
-----------------
More information... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 10\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 20\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #es #robust-speech-event #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:... |
fill-mask | transformers | Our bibert-ende is a bilingual English-German Language Model. Please check out our EMNLP 2021 paper "[BERT, mBERT, or BiBERT? A Study on Contextualized Embeddings for Neural Machine Translation](https://aclanthology.org/2021.emnlp-main.534.pdf)" for more details.
```
@inproceedings{xu-etal-2021-bert,
title = "{BERT... | {"language": ["en", "de"]} | jhu-clsp/bibert-ende | null | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"fill-mask",
"en",
"de",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en",
"de"
] | TAGS
#transformers #pytorch #safetensors #roberta #fill-mask #en #de #autotrain_compatible #endpoints_compatible #region-us
| Our bibert-ende is a bilingual English-German Language Model. Please check out our EMNLP 2021 paper "BERT, mBERT, or BiBERT? A Study on Contextualized Embeddings for Neural Machine Translation" for more details.
# Download
Note that tokenizer package is 'BertTokenizer' not 'AutoTokenizer'.
| [
"# Download\n\nNote that tokenizer package is 'BertTokenizer' not 'AutoTokenizer'."
] | [
"TAGS\n#transformers #pytorch #safetensors #roberta #fill-mask #en #de #autotrain_compatible #endpoints_compatible #region-us \n",
"# Download\n\nNote that tokenizer package is 'BertTokenizer' not 'AutoTokenizer'."
] |
null | transformers | This is the pre-trained model presented in [Automated Chemical Reaction Extraction from Scientific Literature](https://pubs.acs.org/doi/pdf/10.1021/acs.jcim.1c00284), which is a BERT model trained on chemical literature data.
The training corpus was taken from ~200K ACS publications, more details can be found in the... | {} | jiangg/chembert_cased | null | [
"transformers",
"pytorch",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #endpoints_compatible #region-us
| This is the pre-trained model presented in Automated Chemical Reaction Extraction from Scientific Literature, which is a BERT model trained on chemical literature data.
The training corpus was taken from ~200K ACS publications, more details can be found in the paper.
If using these models, please cite the followi... | [] | [
"TAGS\n#transformers #pytorch #endpoints_compatible #region-us \n"
] |
null | transformers | KcELECTRA([https://github.com/Beomi/KcELECTRA](https://github.com/Beomi/KcELECTRA))의 Tokenizer에서 [UNK]로 대체되는 토큰들을 추가했습니다. | {} | jiho0304/bad-korean-tokenizer | null | [
"transformers",
"electra",
"pretraining",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #electra #pretraining #endpoints_compatible #region-us
| KcELECTRA(URL)의 Tokenizer에서 [UNK]로 대체되는 토큰들을 추가했습니다. | [] | [
"TAGS\n#transformers #electra #pretraining #endpoints_compatible #region-us \n"
] |
text-classification | transformers | ElectraBERT tuned with korean-bad-speeches | {} | jiho0304/curseELECTRA | null | [
"transformers",
"pytorch",
"electra",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #electra #text-classification #autotrain_compatible #endpoints_compatible #region-us
| ElectraBERT tuned with korean-bad-speeches | [] | [
"TAGS\n#transformers #pytorch #electra #text-classification #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-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "distilbert-base-uncased-finetuned-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "ar... | jimmyliao/distilbert-base-uncased-finetuned-cola | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-cola
======================================
This model is a fine-tuned version of distilbert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8394
* Matthews Correlation: 0.5414
Model description
-----------------
More informa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-0... |
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. -->
# BERTreach-finetuned-ner
This model is a fine-tuned version of [jimregan/BERTreach](https://huggingface.co/jimregan/BERTreach) on... | {"language": "ga", "license": "apache-2.0", "tags": ["generated_from_trainer", "irish"], "datasets": ["wikiann"], "metrics": ["precision", "recall", "f1", "accuracy"], "widget": [{"text": "Saola\u00edodh P\u00e1draic \u00d3 Conaire i nGaillimh sa bhliain 1882."}], "model-index": [{"name": "BERTreach-finetuned-ner", "re... | jimregan/BERTreach-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"token-classification",
"generated_from_trainer",
"irish",
"ga",
"dataset:wikiann",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ga"
] | TAGS
#transformers #pytorch #tensorboard #roberta #token-classification #generated_from_trainer #irish #ga #dataset-wikiann #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| BERTreach-finetuned-ner
=======================
This model is a fine-tuned version of jimregan/BERTreach on the wikiann dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4944
* Precision: 0.5201
* Recall: 0.5667
* F1: 0.5424
* Accuracy: 0.8366
Model description
-----------------
More i... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #token-classification #generated_from_trainer #irish #ga #dataset-wikiann #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... |
fill-mask | transformers |
## BERTreach
([beirtreach](https://www.teanglann.ie/en/fgb/beirtreach) means 'oyster bed')
**Model size:** 84M
**Training data:**
* [PARSEME 1.2](https://gitlab.com/parseme/parseme_corpus_ga/-/blob/master/README.md)
* Newscrawl 300k portion of the [Leipzig Corpora](https://wortschatz.uni-leipzig.de/en/download/ir... | {"language": "ga", "license": "apache-2.0", "tags": ["irish"]} | jimregan/BERTreach | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"roberta",
"fill-mask",
"irish",
"ga",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ga"
] | TAGS
#transformers #pytorch #jax #safetensors #roberta #fill-mask #irish #ga #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
## BERTreach
(beirtreach means 'oyster bed')
Model size: 84M
Training data:
* PARSEME 1.2
* Newscrawl 300k portion of the Leipzig Corpora
* Private news corpus crawled with Corpus Crawler
(2125804 sentences, 47419062 tokens, as reckoned by wc)
| [
"## BERTreach\n\n(beirtreach means 'oyster bed')\n\nModel size: 84M\n\nTraining data: \n* PARSEME 1.2 \n* Newscrawl 300k portion of the Leipzig Corpora\n* Private news corpus crawled with Corpus Crawler\n\n(2125804 sentences, 47419062 tokens, as reckoned by wc)"
] | [
"TAGS\n#transformers #pytorch #jax #safetensors #roberta #fill-mask #irish #ga #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"## BERTreach\n\n(beirtreach means 'oyster bed')\n\nModel size: 84M\n\nTraining data: \n* PARSEME 1.2 \n* Newscrawl 300k portion of the Leipzig Corpora\n* ... |
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. -->
# bert-base-irish-cased-v1-finetuned-ner
This model is a fine-tuned version of [DCU-NLP/bert-base-irish-cased-v1](https://huggingf... | {"language": "ga", "license": "apache-2.0", "tags": ["generated_from_trainer", "irish"], "datasets": ["wikiann"], "metrics": ["precision", "recall", "f1", "accuracy"], "widget": [{"text": "Saola\u00edodh P\u00e1draic \u00d3 Conaire i nGaillimh sa bhliain 1882."}], "base_model": "DCU-NLP/bert-base-irish-cased-v1", "mode... | jimregan/bert-base-irish-cased-v1-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"bert",
"token-classification",
"generated_from_trainer",
"irish",
"ga",
"dataset:wikiann",
"base_model:DCU-NLP/bert-base-irish-cased-v1",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"regi... | null | 2022-03-02T23:29:05+00:00 | [] | [
"ga"
] | TAGS
#transformers #pytorch #tensorboard #safetensors #bert #token-classification #generated_from_trainer #irish #ga #dataset-wikiann #base_model-DCU-NLP/bert-base-irish-cased-v1 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-base-irish-cased-v1-finetuned-ner
======================================
This model is a fine-tuned version of DCU-NLP/bert-base-irish-cased-v1 on the wikiann dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2468
* Precision: 0.8191
* Recall: 0.8363
* F1: 0.8276
* Accuracy: 0.9307
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #bert #token-classification #generated_from_trainer #irish #ga #dataset-wikiann #base_model-DCU-NLP/bert-base-irish-cased-v1 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe f... |
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. -->
# electra-base-irish-cased-discriminator-v1-finetuned-ner
This model is a fine-tuned version of [DCU-NLP/electra-base-irish-cased-... | {"language": "ga", "license": "apache-2.0", "tags": ["generated_from_trainer", "irish"], "datasets": ["wikiann"], "metrics": ["precision", "recall", "f1", "accuracy"], "widget": [{"text": "Saola\u00edodh P\u00e1draic \u00d3 Conaire i nGaillimh sa bhliain 1882."}], "model-index": [{"name": "electra-base-irish-cased-disc... | jimregan/electra-base-irish-cased-discriminator-v1-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"electra",
"token-classification",
"generated_from_trainer",
"irish",
"ga",
"dataset:wikiann",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ga"
] | TAGS
#transformers #pytorch #tensorboard #safetensors #electra #token-classification #generated_from_trainer #irish #ga #dataset-wikiann #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| electra-base-irish-cased-discriminator-v1-finetuned-ner
=======================================================
This model is a fine-tuned version of DCU-NLP/electra-base-irish-cased-generator-v1 on the wikiann dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6654
* Precision: 0.5414
* Re... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #electra #token-classification #generated_from_trainer #irish #ga #dataset-wikiann #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during ... |
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-irish-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.c... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-irish-colab", "results": []}]} | jimregan/wav2vec2-large-xls-r-300m-irish-colab | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xls-r-300m-irish-colab
=====================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 1.4286
* Wer: 0.5097
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: 32\n* eval\\_batch\\_size: 16\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilo... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #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\n* train\\_batch\... |
automatic-speech-recognition | transformers | # Wav2Vec2-Large-XLSR-Irish
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53)
on the [Irish Common Voice dataset](https://huggingface.co/datasets/common_voice).
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used ... | {"language": "ga", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "XLSR Wav2Vec2 Irish by Jim O'Regan", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech... | jimregan/wav2vec2-large-xlsr-irish-basic | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"ga",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ga"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #ga #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
| # Wav2Vec2-Large-XLSR-Irish
Fine-tuned facebook/wav2vec2-large-xlsr-53
on the Irish Common Voice dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
## Evaluation
The model can be evaluated as follows on th... | [
"# Wav2Vec2-Large-XLSR-Irish\nFine-tuned facebook/wav2vec2-large-xlsr-53\non the Irish Common Voice dataset.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\nThe model can be evaluat... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #ga #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-Irish\nFine-tuned facebook/wav2vec2-large-xlsr-53\non the Irish Common Voice data... |
automatic-speech-recognition | transformers | # Wav2Vec2-Large-XLSR-Latvian
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53)
on the [Latvian Common Voice dataset](https://huggingface.co/datasets/common_voice).
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can b... | {"language": "lv", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "base_model": "facebook/wav2vec2-large-xlsr-53", "model-index": [{"name": "jimregan/wav2vec2-large-xlsr-latvian-cv", "results": [{"task": {"... | jimregan/wav2vec2-large-xlsr-latvian-cv | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"lv",
"dataset:common_voice",
"base_model:facebook/wav2vec2-large-xlsr-53",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"lv"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #lv #dataset-common_voice #base_model-facebook/wav2vec2-large-xlsr-53 #license-apache-2.0 #model-index #endpoints_compatible #region-us
| # Wav2Vec2-Large-XLSR-Latvian
Fine-tuned facebook/wav2vec2-large-xlsr-53
on the Latvian Common Voice dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
## Evaluation
The model can be evaluated as fol... | [
"# Wav2Vec2-Large-XLSR-Latvian\n\nFine-tuned facebook/wav2vec2-large-xlsr-53\non the Latvian Common Voice dataset.\n\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model can ... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #lv #dataset-common_voice #base_model-facebook/wav2vec2-large-xlsr-53 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-Latvian\n\nFine-tuned facebook/wav2ve... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-Upper-Sorbian
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53)
on the [Upper Sorbian Common Voice dataset](https://huggingface.co/datasets/common_voice), with an
extra 28 minutes of audio from an online [Sorbian course](https://sprachkurs.sorb... | {"language": "hsb", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "model-index": [{"name": "XLSR Wav2Vec2 Upper Sorbian mixed by Jim O'Regan", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Reco... | jimregan/wav2vec2-large-xlsr-upper-sorbian-mixed | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"hsb",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"hsb"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #hsb #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-Upper-Sorbian
Fine-tuned facebook/wav2vec2-large-xlsr-53
on the Upper Sorbian Common Voice dataset, with an
extra 28 minutes of audio from an online Sorbian course.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a... | [
"# Wav2Vec2-Large-XLSR-Upper-Sorbian\n\nFine-tuned facebook/wav2vec2-large-xlsr-53\non the Upper Sorbian Common Voice dataset, with an \nextra 28 minutes of audio from an online Sorbian course.\n\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used dir... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #hsb #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-Upper-Sorbian\n\nFine-tuned facebook/wav2vec2-large-xlsr-53\non the Upper Sorbia... |
question-answering | transformers |
# BERT-Base Uncased SQuADv1
`bert-base-uncased` trained on question answering with `squad`.
Evalulation scores:
```
***** eval metrics *****
epoch = 3.0
eval_exact_match = 80.6906
eval_f1 = 88.1129
eval_samples = 10784
``` | {"license": "apache-2.0"} | jimypbr/bert-base-uncased-squad | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #question-answering #license-apache-2.0 #endpoints_compatible #region-us
|
# BERT-Base Uncased SQuADv1
'bert-base-uncased' trained on question answering with 'squad'.
Evalulation scores:
| [
"# BERT-Base Uncased SQuADv1\r\n\r\n'bert-base-uncased' trained on question answering with 'squad'. \r\n\r\nEvalulation scores:"
] | [
"TAGS\n#transformers #pytorch #bert #question-answering #license-apache-2.0 #endpoints_compatible #region-us \n",
"# BERT-Base Uncased SQuADv1\r\n\r\n'bert-base-uncased' trained on question answering with 'squad'. \r\n\r\nEvalulation scores:"
] |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-large-multiwoz
This model is a fine-tuned version of [t5-large](https://huggingface.co/t5-large) on an unknown dataset.
It ac... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "t5-large-multiwoz", "results": []}]} | jinlmsft/t5-large-multiwoz | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-large-multiwoz
=================
This model is a fine-tuned version of t5-large on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0064
* Acc: 1.0
* True Num: 56671
* Num: 56776
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: 4\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=... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-large-slots
This model is a fine-tuned version of [t5-large](https://huggingface.co/t5-large) on an unknown dataset.
It achie... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "t5-large-slots", "results": []}]} | jinlmsft/t5-large-slots | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-large-slots
==============
This model is a fine-tuned version of t5-large on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0889
* Acc: 0.76
* True Num: 11167
* Num: 14748
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: 4\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=... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate... |
null | transformers | # DALL-E-Tokenizer
Huggingface package for the discrete VAE usded for [DALL-E](https://github.com/openai/DALL-E).
# How to use
```python
# from dall_e_tok import DallEEncoder
from dall_e_tok import DALLETokenizer
tokenizer = DALLETokenizer.from_pretrained("jinmang2/dall-e-tokenizer")
```
| {} | jinmang2/dall-e-tokenizer | null | [
"transformers",
"pytorch",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #endpoints_compatible #region-us
| # DALL-E-Tokenizer
Huggingface package for the discrete VAE usded for DALL-E.
# How to use
| [
"# DALL-E-Tokenizer\n\nHuggingface package for the discrete VAE usded for DALL-E.",
"# How to use"
] | [
"TAGS\n#transformers #pytorch #endpoints_compatible #region-us \n",
"# DALL-E-Tokenizer\n\nHuggingface package for the discrete VAE usded for DALL-E.",
"# How to use"
] |
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-TPU-cv-fine-tune
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-base-TPU-cv-fine-tune", "results": []}]} | jiobiala24/wav2vec2-base-checkpoint-1 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-TPU-cv-fine-tune
==============================
This model is a fine-tuned version of facebook/wav2vec2-base on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 1.6987
* Wer: 0.6019
Model description
-----------------
More information needed
Intended us... | [
"### 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 #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\n* t... |
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-checkpoint-10
This model is a fine-tuned version of [jiobiala24/wav2vec2-base-checkpoint-9](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-base-checkpoint-10", "results": []}]} | jiobiala24/wav2vec2-base-checkpoint-10 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-checkpoint-10
===========================
This model is a fine-tuned version of jiobiala24/wav2vec2-base-checkpoint-9 on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9567
* Wer: 0.3292
Model description
-----------------
More information needed
In... | [
"### 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 #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: 0.0001\n* t... |
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-checkpoint-11.1
This model is a fine-tuned version of [jiobiala24/wav2vec2-base-checkpoint-10](https://huggingface... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-base-checkpoint-11.1", "results": []}]} | jiobiala24/wav2vec2-base-checkpoint-11.1 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-checkpoint-11.1
=============================
This model is a fine-tuned version of jiobiala24/wav2vec2-base-checkpoint-10 on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 1.0173
* Wer: 0.3350
Model description
-----------------
More information needed... | [
"### 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 #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: 0.0001\n* t... |
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-checkpoint-12
This model is a fine-tuned version of [jiobiala24/wav2vec2-base-checkpoint-11.1](https://huggingface... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-base-checkpoint-12", "results": []}]} | jiobiala24/wav2vec2-base-checkpoint-12 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-checkpoint-12
===========================
This model is a fine-tuned version of jiobiala24/wav2vec2-base-checkpoint-11.1 on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 1.0795
* Wer: 0.3452
Model description
-----------------
More information needed
... | [
"### 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 #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: 0.0001\n* t... |
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-checkpoint-13
This model is a fine-tuned version of [jiobiala24/wav2vec2-base-checkpoint-12](https://huggingface.c... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-base-checkpoint-13", "results": []}]} | jiobiala24/wav2vec2-base-checkpoint-13 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-checkpoint-13
===========================
This model is a fine-tuned version of jiobiala24/wav2vec2-base-checkpoint-12 on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1804
* Wer: 0.3809
Model description
-----------------
More information needed
I... | [
"### 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 #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: 0.0001\n* t... |
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-checkpoint-14
This model is a fine-tuned version of [jiobiala24/wav2vec2-base-checkpoint-13](https://huggingface.c... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-base-checkpoint-14", "results": []}]} | jiobiala24/wav2vec2-base-checkpoint-14 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-checkpoint-14
===========================
This model is a fine-tuned version of jiobiala24/wav2vec2-base-checkpoint-13 on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2822
* Wer: 0.4068
Model description
-----------------
More information needed
I... | [
"### 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 #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: 0.0001\n* t... |
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-TPU-cv-fine-tune-2
This model is a fine-tuned version of [jiobiala24/wav2vec2-base-TPU-cv-fine-tune](https://huggi... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-base-TPU-cv-fine-tune-2", "results": []}]} | jiobiala24/wav2vec2-base-checkpoint-2 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-TPU-cv-fine-tune-2
================================
This model is a fine-tuned version of jiobiala24/wav2vec2-base-TPU-cv-fine-tune on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 1.6051
* Wer: 0.5484
Model description
-----------------
More informati... | [
"### 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 #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\n* t... |
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-checkpoint-3
This model is a fine-tuned version of [jiobiala24/wav2vec2-base-checkpoint-2](https://huggingface.co/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-base-checkpoint-3", "results": []}]} | jiobiala24/wav2vec2-base-checkpoint-3 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-checkpoint-3
==========================
This model is a fine-tuned version of jiobiala24/wav2vec2-base-checkpoint-2 on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 1.7007
* Wer: 0.5514
Model description
-----------------
More information needed
Inte... | [
"### 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 #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\n* t... |
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-checkpoint-4
This model is a fine-tuned version of [jiobiala24/wav2vec2-base-checkpoint-3](https://huggingface.co/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-base-checkpoint-4", "results": []}]} | jiobiala24/wav2vec2-base-checkpoint-4 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-base-checkpoint-4
This model is a fine-tuned version of jiobiala24/wav2vec2-base-checkpoint-3 on the common_voice dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedu... | [
"# wav2vec2-base-checkpoint-4\n\nThis model is a fine-tuned version of jiobiala24/wav2vec2-base-checkpoint-3 on the common_voice dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information neede... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-base-checkpoint-4\n\nThis model is a fine-tuned version of jiobiala24/wav2vec2-base-checkpoint-3 on the common_voic... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-checkpoint-5
This model is a fine-tuned version of [jiobiala24/wav2vec2-base-checkpoint-4](https://huggingface.co/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-base-checkpoint-5", "results": []}]} | jiobiala24/wav2vec2-base-checkpoint-5 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-checkpoint-5
==========================
This model is a fine-tuned version of jiobiala24/wav2vec2-base-checkpoint-4 on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9849
* Wer: 0.3354
Model description
-----------------
More information needed
Inte... | [
"### 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 #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: 0.0001\n* t... |
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-checkpoint-6
This model is a fine-tuned version of [jiobiala24/wav2vec2-base-checkpoint-5](https://huggingface.co/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-base-checkpoint-6", "results": []}]} | jiobiala24/wav2vec2-base-checkpoint-6 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-checkpoint-6
==========================
This model is a fine-tuned version of jiobiala24/wav2vec2-base-checkpoint-5 on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9738
* Wer: 0.3323
Model description
-----------------
More information needed
Inte... | [
"### 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 #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: 0.0001\n* t... |
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-checkpoint-7.1
This model is a fine-tuned version of [jiobiala24/wav2vec2-base-checkpoint-6](https://huggingface.c... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-base-checkpoint-7.1", "results": []}]} | jiobiala24/wav2vec2-base-checkpoint-7.1 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-checkpoint-7.1
============================
This model is a fine-tuned version of jiobiala24/wav2vec2-base-checkpoint-6 on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9369
* Wer: 0.3243
Model description
-----------------
More information needed
... | [
"### 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 #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: 0.0001\n* t... |
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-checkpoint-8
This model is a fine-tuned version of [jiobiala24/wav2vec2-base-checkpoint-7.1](https://huggingface.c... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-base-checkpoint-8", "results": []}]} | jiobiala24/wav2vec2-base-checkpoint-8 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-checkpoint-8
==========================
This model is a fine-tuned version of jiobiala24/wav2vec2-base-checkpoint-7.1 on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9561
* Wer: 0.3271
Model description
-----------------
More information needed
In... | [
"### 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 #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: 0.0001\n* t... |
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-checkpoint-9
This model is a fine-tuned version of [jiobiala24/wav2vec2-base-checkpoint-8](https://huggingface.co/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-base-checkpoint-9", "results": []}]} | jiobiala24/wav2vec2-base-checkpoint-9 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-checkpoint-9
==========================
This model is a fine-tuned version of jiobiala24/wav2vec2-base-checkpoint-8 on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9203
* Wer: 0.3258
Model description
-----------------
More information needed
Inte... | [
"### 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 #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: 0.0001\n* t... |
fill-mask | transformers |
# BERT multilingual base model (cased)
Pretrained model on the top 104 languages with the largest Wikipedia using a masked language modeling (MLM) objective.
It was introduced in [this paper](https://arxiv.org/abs/1810.04805) and first released in
[this repository](https://github.com/google-research/bert). This model... | {"language": "multilingual", "license": "apache-2.0", "datasets": ["wikipedia"]} | jirmauritz/bert-multilingual-emoji | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"multilingual",
"dataset:wikipedia",
"arxiv:1810.04805",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1810.04805"
] | [
"multilingual"
] | TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #multilingual #dataset-wikipedia #arxiv-1810.04805 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# BERT multilingual base model (cased)
Pretrained model on the top 104 languages with the largest Wikipedia using a masked language modeling (MLM) objective.
It was introduced in this paper and first released in
this repository. This model is case sensitive: it makes a difference
between english and English.
Disclai... | [
"# BERT multilingual base model (cased)\n\nPretrained model on the top 104 languages with the largest Wikipedia using a masked language modeling (MLM) objective.\nIt was introduced in this paper and first released in\nthis repository. This model is case sensitive: it makes a difference\nbetween english and English.... | [
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #multilingual #dataset-wikipedia #arxiv-1810.04805 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# BERT multilingual base model (cased)\n\nPretrained model on the top 104 languages with the largest Wikipedia using a masked ... |
fill-mask | transformers |
<p align="center">
<img src="https://github.com/iPieter/RobBERT/raw/master/res/robbert_logo_with_name.png" alt="RobBERT: A Dutch RoBERTa-based Language Model" width="75%">
</p>
# RobBERT: Dutch RoBERTa-based Language Model.
[RobBERT](https://github.com/iPieter/RobBERT) is the state-of-the-art Dutch BERT model.... | {"language": "nl", "license": "mit", "tags": ["Dutch", "Flemish", "RoBERTa", "RobBERT"], "datasets": ["oscar", "oscar (NL)", "dbrd", "lassy-ud", "europarl-mono", "conll2002"], "thumbnail": "https://github.com/iPieter/RobBERT/raw/master/res/robbert_logo.png", "widget": [{"text": "Hallo, ik ben RobBERT, een <mask> taalmo... | jirmauritz/robbert-v2-dutch-base | null | [
"transformers",
"pytorch",
"tf",
"jax",
"roberta",
"fill-mask",
"Dutch",
"Flemish",
"RoBERTa",
"RobBERT",
"nl",
"arxiv:2001.06286",
"arxiv:2004.02814",
"arxiv:2010.13652",
"arxiv:2101.05716",
"arxiv:1907.11692",
"arxiv:2001.02943",
"arxiv:1909.11942",
"license:mit",
"autotrain_... | null | 2022-03-02T23:29:05+00:00 | [
"2001.06286",
"2004.02814",
"2010.13652",
"2101.05716",
"1907.11692",
"2001.02943",
"1909.11942"
] | [
"nl"
] | TAGS
#transformers #pytorch #tf #jax #roberta #fill-mask #Dutch #Flemish #RoBERTa #RobBERT #nl #arxiv-2001.06286 #arxiv-2004.02814 #arxiv-2010.13652 #arxiv-2101.05716 #arxiv-1907.11692 #arxiv-2001.02943 #arxiv-1909.11942 #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
RobBERT: Dutch RoBERTa-based Language Model.
============================================
RobBERT is the state-of-the-art Dutch BERT model. It is a large pre-trained general Dutch language model that can be fine-tuned on a given dataset to perform any text classification, regression or token-tagging... | [
"### Our Performance Evaluation Results\n\n\nAll experiments are described in more detail in our paper, with the code in our GitHub repository.",
"### Sentiment analysis\n\n\nPredicting whether a review is positive or negative using the Dutch Book Reviews Dataset.",
"### Die/Dat (coreference resolution)\n\n\nWe... | [
"TAGS\n#transformers #pytorch #tf #jax #roberta #fill-mask #Dutch #Flemish #RoBERTa #RobBERT #nl #arxiv-2001.06286 #arxiv-2004.02814 #arxiv-2010.13652 #arxiv-2101.05716 #arxiv-1907.11692 #arxiv-2001.02943 #arxiv-1909.11942 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Our Performan... |
fill-mask | transformers | BERT MLM
| {} | jivatneet/bert-mlm-batchsize8 | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| BERT MLM
| [] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] |
feature-extraction | sentence-transformers |
# sentence-transformers/gtr-t5-base
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space. The model was specifically trained for the task of sematic search.
This model was converted from the Tensorflow model [gtr-base-1](https://t... | {"language": "en", "license": "apache-2.0", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "feature-extraction"} | jj-co/gtr-t5-base | null | [
"sentence-transformers",
"pytorch",
"t5",
"feature-extraction",
"sentence-similarity",
"transformers",
"en",
"arxiv:2112.07899",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2112.07899"
] | [
"en"
] | TAGS
#sentence-transformers #pytorch #t5 #feature-extraction #sentence-similarity #transformers #en #arxiv-2112.07899 #license-apache-2.0 #endpoints_compatible #region-us
|
# sentence-transformers/gtr-t5-base
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space. The model was specifically trained for the task of sematic search.
This model was converted from the Tensorflow model gtr-base-1 to PyTorch. When using this model, h... | [
"# sentence-transformers/gtr-t5-base\r\n\r\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space. The model was specifically trained for the task of sematic search.\r\n\r\nThis model was converted from the Tensorflow model gtr-base-1 to PyTorch. When using th... | [
"TAGS\n#sentence-transformers #pytorch #t5 #feature-extraction #sentence-similarity #transformers #en #arxiv-2112.07899 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# sentence-transformers/gtr-t5-base\r\n\r\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional d... |
image-classification | transformers |
# lotr
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | jjhoffstein/lotr | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# lotr
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
#### aragorn
!aragorn
#### frodo
!frodo
#### gandalf
!gandalf
#### gollum
!gollum
#### legolas
!legolas | [
"# lotr\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",
"#### aragorn\n\n!aragorn",
"#### frodo\n\n!frodo",
"#### gandalf\n\n!gandalf",
"#### gollum\n\n!go... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# lotr\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... |
null | keras |
# Simple CNN-based Artist Classifier
This repo contains a simple CNN-based Keras model which classifies images into one of 10 selected artists/painters.
- The purpose of this model was for a quick prototyping
- Data has been web-crawled using `https://github.com/YoongiKim/AutoCrawler`
- 10 popular artists/painters w... | {"language": "en", "license": "mit", "datasets": ["web crawled (coming soon)"]} | jkang/drawing-artist-classifier | null | [
"keras",
"en",
"license:mit",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#keras #en #license-mit #has_space #region-us
|
# Simple CNN-based Artist Classifier
This repo contains a simple CNN-based Keras model which classifies images into one of 10 selected artists/painters.
- The purpose of this model was for a quick prototyping
- Data has been web-crawled using 'URL
- 10 popular artists/painters were chosen:
- \[ARTIST\]: \[ID\]
... | [
"# Simple CNN-based Artist Classifier\n\nThis repo contains a simple CNN-based Keras model which classifies images into one of 10 selected artists/painters.\n\n- The purpose of this model was for a quick prototyping\n- Data has been web-crawled using 'URL\n- 10 popular artists/painters were chosen:\n - \\[ARTIST... | [
"TAGS\n#keras #en #license-mit #has_space #region-us \n",
"# Simple CNN-based Artist Classifier\n\nThis repo contains a simple CNN-based Keras model which classifies images into one of 10 selected artists/painters.\n\n- The purpose of this model was for a quick prototyping\n- Data has been web-crawled using 'URL\... |
null | keras |
# Simple CNN-based Artist Classifier
This repo contains a simple CNN-based Keras model which classifies images into one of 8 artistic trends.
See also: `https://huggingface.co/jkang/drawing-artist-classifier`
- The purpose of this model was for a quick prototyping
- Data has been web-crawled using `https://github.c... | {"language": "en", "license": "mit", "datasets": ["web crawled (coming soon)"]} | jkang/drawing-artistic-trend-classifier | null | [
"keras",
"en",
"license:mit",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#keras #en #license-mit #has_space #region-us
|
# Simple CNN-based Artist Classifier
This repo contains a simple CNN-based Keras model which classifies images into one of 8 artistic trends.
See also: 'URL
- The purpose of this model was for a quick prototyping
- Data has been web-crawled using 'URL
- 8 popular artists/painters were chosen:
- \[TREND\]: \[ID\... | [
"# Simple CNN-based Artist Classifier\n\nThis repo contains a simple CNN-based Keras model which classifies images into one of 8 artistic trends.\n\nSee also: 'URL\n\n- The purpose of this model was for a quick prototyping\n- Data has been web-crawled using 'URL\n- 8 popular artists/painters were chosen:\n - \\[... | [
"TAGS\n#keras #en #license-mit #has_space #region-us \n",
"# Simple CNN-based Artist Classifier\n\nThis repo contains a simple CNN-based Keras model which classifies images into one of 8 artistic trends.\n\nSee also: 'URL\n\n- The purpose of this model was for a quick prototyping\n- Data has been web-crawled usin... |
automatic-speech-recognition | espnet |
## ESPnet2 ASR model
### `jkang/espnet2_an4_asr`
This model was trained by jaekookang using an4 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 48422215e272812feb9bbac9d7cf4aae6a316bca
pip install -e .
cd egs2/an4/asr1
./run.sh --skip_data_prep... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["an4"]} | jkang/espnet2_an4_asr | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"dataset:an4",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #automatic-speech-recognition #en #dataset-an4 #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ESPnet2 ASR model
-----------------
### 'jkang/espnet2\_an4\_asr'
This model was trained by jaekookang using an4 recipe in espnet.
### Demo: How to use in ESPnet2
RESULTS
=======
Environments
------------
* date: 'Tue Feb 1 13:22:35 KST 2022'
* python version: '3.9.7 (default, Sep 16 2021, 13:09:58) [GCC 7.... | [
"### 'jkang/espnet2\\_an4\\_asr'\n\n\nThis model was trained by jaekookang using an4 recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Tue Feb 1 13:22:35 KST 2022'\n* python version: '3.9.7 (default, Sep 16 2021, 13:09:58) [GCC 7.5.0]'\n* espn... | [
"TAGS\n#espnet #audio #automatic-speech-recognition #en #dataset-an4 #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"### 'jkang/espnet2\\_an4\\_asr'\n\n\nThis model was trained by jaekookang using an4 recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n-----------... |
automatic-speech-recognition | espnet |
## ESPnet2 ASR model
### `jkang/espnet2_librispeech_100_conformer`
- This model was trained by jaekookang using librispeech_100 recipe in [espnet](https://github.com/espnet/espnet/).
- Gradio Demo: [🤗 ESPNet2 ASR Librispeech Conformer](https://huggingface.co/spaces/jkang/espnet2_asr_librispeech_100h)
### Demo: Ho... | {"language": "noinfo", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["librispeech_100"]} | jkang/espnet2_librispeech_100_conformer | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"dataset:librispeech_100",
"arxiv:1804.00015",
"license:cc-by-4.0",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"noinfo"
] | TAGS
#espnet #audio #automatic-speech-recognition #dataset-librispeech_100 #arxiv-1804.00015 #license-cc-by-4.0 #has_space #region-us
| ESPnet2 ASR model
-----------------
### 'jkang/espnet2\_librispeech\_100\_conformer'
* This model was trained by jaekookang using librispeech\_100 recipe in espnet.
* Gradio Demo: ESPNet2 ASR Librispeech Conformer
### Demo: How to use in ESPnet2
RESULTS
=======
Environments
------------
* date: 'Fri Feb 11 ... | [
"### 'jkang/espnet2\\_librispeech\\_100\\_conformer'\n\n\n* This model was trained by jaekookang using librispeech\\_100 recipe in espnet.\n* Gradio Demo: ESPNet2 ASR Librispeech Conformer",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Fri Feb 11 01:42:52 KS... | [
"TAGS\n#espnet #audio #automatic-speech-recognition #dataset-librispeech_100 #arxiv-1804.00015 #license-cc-by-4.0 #has_space #region-us \n",
"### 'jkang/espnet2\\_librispeech\\_100\\_conformer'\n\n\n* This model was trained by jaekookang using librispeech\\_100 recipe in espnet.\n* Gradio Demo: ESPNet2 ASR Libris... |
automatic-speech-recognition | espnet |
## ESPnet2 ASR model
### `jkang/espnet2_librispeech_100_conformer_char`
This model was trained by jaekookang using librispeech_100 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 82a0a0fa97b8a4a578f0a2c031ec49b3afec1504
pip install -e .
cd egs2... | {"language": "noinfo", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["librispeech_100"]} | jkang/espnet2_librispeech_100_conformer_char | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"dataset:librispeech_100",
"arxiv:1804.00015",
"license:cc-by-4.0",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"noinfo"
] | TAGS
#espnet #audio #automatic-speech-recognition #dataset-librispeech_100 #arxiv-1804.00015 #license-cc-by-4.0 #has_space #region-us
| ESPnet2 ASR model
-----------------
### 'jkang/espnet2\_librispeech\_100\_conformer\_char'
This model was trained by jaekookang using librispeech\_100 recipe in espnet.
### Demo: How to use in ESPnet2
RESULTS
=======
Environments
------------
* date: 'Thu Feb 24 17:47:04 KST 2022'
* python version: '3.9.7 (... | [
"### 'jkang/espnet2\\_librispeech\\_100\\_conformer\\_char'\n\n\nThis model was trained by jaekookang using librispeech\\_100 recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Thu Feb 24 17:47:04 KST 2022'\n* python version: '3.9.7 (default, S... | [
"TAGS\n#espnet #audio #automatic-speech-recognition #dataset-librispeech_100 #arxiv-1804.00015 #license-cc-by-4.0 #has_space #region-us \n",
"### 'jkang/espnet2\\_librispeech\\_100\\_conformer\\_char'\n\n\nThis model was trained by jaekookang using librispeech\\_100 recipe in espnet.",
"### Demo: How to use in ... |
automatic-speech-recognition | espnet |
## ESPnet2 ASR model
### `jkang/espnet2_librispeech_100_conformer_word`
This model was trained by jaekookang using librispeech_100 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 82a0a0fa97b8a4a578f0a2c031ec49b3afec1504
pip install -e .
cd egs2... | {"language": "noinfo", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["librispeech_100"]} | jkang/espnet2_librispeech_100_conformer_word | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"dataset:librispeech_100",
"arxiv:1804.00015",
"license:cc-by-4.0",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"noinfo"
] | TAGS
#espnet #audio #automatic-speech-recognition #dataset-librispeech_100 #arxiv-1804.00015 #license-cc-by-4.0 #has_space #region-us
| ESPnet2 ASR model
-----------------
### 'jkang/espnet2\_librispeech\_100\_conformer\_word'
This model was trained by jaekookang using librispeech\_100 recipe in espnet.
### Demo: How to use in ESPnet2
RESULTS
=======
Environments
------------
* date: 'Tue Feb 22 17:38:22 KST 2022'
* python version: '3.9.7 (... | [
"### 'jkang/espnet2\\_librispeech\\_100\\_conformer\\_word'\n\n\nThis model was trained by jaekookang using librispeech\\_100 recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Tue Feb 22 17:38:22 KST 2022'\n* python version: '3.9.7 (default, S... | [
"TAGS\n#espnet #audio #automatic-speech-recognition #dataset-librispeech_100 #arxiv-1804.00015 #license-cc-by-4.0 #has_space #region-us \n",
"### 'jkang/espnet2\\_librispeech\\_100\\_conformer\\_word'\n\n\nThis model was trained by jaekookang using librispeech\\_100 recipe in espnet.",
"### Demo: How to use in ... |
null | espnet |
## ESPnet2 DIAR model
### `jkang/espnet2_mini_librispeech_diar`
This model was trained by jaekookang using mini_librispeech recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout e08a89e0a43db7fc12bec835c62a000ad10bd417
pip install -e .
cd egs2/mini_l... | {"language": "noinfo", "license": "cc-by-4.0", "tags": ["espnet", "audio", "diarization"], "datasets": ["mini_librispeech"]} | jkang/espnet2_mini_librispeech_diar | null | [
"espnet",
"audio",
"diarization",
"dataset:mini_librispeech",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"noinfo"
] | TAGS
#espnet #audio #diarization #dataset-mini_librispeech #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ESPnet2 DIAR model
------------------
### 'jkang/espnet2\_mini\_librispeech\_diar'
This model was trained by jaekookang using mini\_librispeech recipe in espnet.
### Demo: How to use in ESPnet2
RESULTS
=======
Environments
------------
* date: 'Tue Feb 8 16:41:16 KST 2022'
* python version: '3.9.7 (default,... | [
"### 'jkang/espnet2\\_mini\\_librispeech\\_diar'\n\n\nThis model was trained by jaekookang using mini\\_librispeech recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Tue Feb 8 16:41:16 KST 2022'\n* python version: '3.9.7 (default, Sep 16 2021,... | [
"TAGS\n#espnet #audio #diarization #dataset-mini_librispeech #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"### 'jkang/espnet2\\_mini\\_librispeech\\_diar'\n\n\nThis model was trained by jaekookang using mini\\_librispeech recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEn... |
fill-mask | transformers | # LitBERTa uncased model
Not the best model because of limited resources (Trained on ~4.7 GB of data on RTX2070 8GB for ~10 days) but it covers special lithuanian symbols `ąčęėįšųūž`. 128K vocabulary chosen because language has a lot of word forms.
## How to use
```python
from transformers import pipeline
unmasker = ... | {"language": "lt", "license": "mit", "tags": ["exbert"]} | jkeruotis/LitBERTa-uncased | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"roberta",
"fill-mask",
"exbert",
"lt",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"lt"
] | TAGS
#transformers #pytorch #jax #safetensors #roberta #fill-mask #exbert #lt #license-mit #autotrain_compatible #endpoints_compatible #region-us
| # LitBERTa uncased model
Not the best model because of limited resources (Trained on ~4.7 GB of data on RTX2070 8GB for ~10 days) but it covers special lithuanian symbols 'ąčęėįšųūž'. 128K vocabulary chosen because language has a lot of word forms.
## How to use
| [
"# LitBERTa uncased model\n\nNot the best model because of limited resources (Trained on ~4.7 GB of data on RTX2070 8GB for ~10 days) but it covers special lithuanian symbols 'ąčęėįšųūž'. 128K vocabulary chosen because language has a lot of word forms.",
"## How to use"
] | [
"TAGS\n#transformers #pytorch #jax #safetensors #roberta #fill-mask #exbert #lt #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# LitBERTa uncased model\n\nNot the best model because of limited resources (Trained on ~4.7 GB of data on RTX2070 8GB for ~10 days) but it covers special lithu... |
question-answering | transformers | # XLNet Fine-tuned on SQuAD / Quoref Dataset
[XLNet](https://arxiv.org/abs/1906.08237) jointly developed by Google and CMU and fine-tuned on [SQuAD / SQuAD 2.0](https://rajpurkar.github.io/SQuAD-explorer/) and [Quoref](https://leaderboard.allenai.org/quoref) for question answering down-stream task.
## Evaluation Resu... | {} | jkgrad/xlnet-base-cased-squad-quoref | null | [
"transformers",
"pytorch",
"xlnet",
"question-answering",
"arxiv:1906.08237",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1906.08237"
] | [] | TAGS
#transformers #pytorch #xlnet #question-answering #arxiv-1906.08237 #endpoints_compatible #region-us
| XLNet Fine-tuned on SQuAD / Quoref Dataset
==========================================
XLNet jointly developed by Google and CMU and fine-tuned on SQuAD / SQuAD 2.0 and Quoref for question answering down-stream task.
Evaluation Result on Quoref
---------------------------
Results Comparison on Quoref
-------------... | [] | [
"TAGS\n#transformers #pytorch #xlnet #question-answering #arxiv-1906.08237 #endpoints_compatible #region-us \n"
] |
question-answering | transformers | # XLNet Fine-tuned on SQuAD 2.0 Dataset
[XLNet](https://arxiv.org/abs/1906.08237) jointly developed by Google and CMU and fine-tuned on [SQuAD 2.0](https://rajpurkar.github.io/SQuAD-explorer/) for question answering down-stream task.
## Training Results (Metrics)
```
{
"HasAns_exact": 74.7132253711201
"HasAns_... | {} | jkgrad/xlnet-base-squadv2 | null | [
"transformers",
"pytorch",
"xlnet",
"question-answering",
"arxiv:1906.08237",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1906.08237"
] | [] | TAGS
#transformers #pytorch #xlnet #question-answering #arxiv-1906.08237 #endpoints_compatible #region-us
| XLNet Fine-tuned on SQuAD 2.0 Dataset
=====================================
XLNet jointly developed by Google and CMU and fine-tuned on SQuAD 2.0 for question answering down-stream task.
Training Results (Metrics)
--------------------------
Results Comparison
------------------
Metric: EM, Paper: 78.46, Model: ... | [] | [
"TAGS\n#transformers #pytorch #xlnet #question-answering #arxiv-1906.08237 #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. -->
# sentiment-model-sample
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on th... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "metrics": ["accuracy"], "model-index": [{"name": "sentiment-model-sample", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "imdb", "type": "imdb", "args": "plain_text"}, "metrics... | jkhan447/sentiment-model-sample | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# sentiment-model-sample
This model is a fine-tuned version of bert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5280
- Accuracy: 0.9395
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and ev... | [
"# sentiment-model-sample\n\nThis model is a fine-tuned version of bert-base-uncased on the imdb dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.5280\n- Accuracy: 0.9395",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed"... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# sentiment-model-sample\n\nThis model is a fine-tuned version of bert-base-uncased on the imdb dataset.\nIt ach... |
null | transformers | ### electra-ka is first of its kind, Transformer based, open source Georgian language model.
The model is trained on 33GB of Georgian text collected from 4854621 pages in commoncrowl archive.
| {} | jnz/electra-ka | null | [
"transformers",
"pytorch",
"electra",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #electra #endpoints_compatible #region-us
| ### electra-ka is first of its kind, Transformer based, open source Georgian language model.
The model is trained on 33GB of Georgian text collected from 4854621 pages in commoncrowl archive.
| [
"### electra-ka is first of its kind, Transformer based, open source Georgian language model.\n\n\nThe model is trained on 33GB of Georgian text collected from 4854621 pages in commoncrowl archive."
] | [
"TAGS\n#transformers #pytorch #electra #endpoints_compatible #region-us \n",
"### electra-ka is first of its kind, Transformer based, open source Georgian language model.\n\n\nThe model is trained on 33GB of Georgian text collected from 4854621 pages in commoncrowl archive."
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# BERT_Tweet_Sentiment_100_2epochs
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) o... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "BERT_Tweet_Sentiment_100_2epochs", "results": []}]} | joe5campbell/BERT_Tweet_Sentiment_100_2epochs | null | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #tf #bert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| BERT\_Tweet\_Sentiment\_100\_2epochs
====================================
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.6279
* Train Accuracy: 0.6824
* Validation Loss: 0.7791
* Validation Accuracy: 0.2667
* Ep... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'clipnorm': 1.0, 'learning\\_rate': 3e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}\n* training\\_precision: float32",
"### Training results",... | [
"TAGS\n#transformers #tf #bert #text-classification #generated_from_keras_callback #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* optimizer: {'name': 'Adam', 'clipnorm': 1.0, 'learni... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# BERT_Tweet_Sentiment_100k_2eps
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on ... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "BERT_Tweet_Sentiment_100k_2eps", "results": []}]} | joe5campbell/BERT_Tweet_Sentiment_100k_2eps | null | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #tf #bert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| BERT\_Tweet\_Sentiment\_100k\_2eps
==================================
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.1259
* Train Accuracy: 0.9542
* Validation Loss: 0.6133
* Validation Accuracy: 0.8315
* Epoch:... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'clipnorm': 1.0, 'learning\\_rate': 3e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}\n* training\\_precision: float32",
"### Training results",... | [
"TAGS\n#transformers #tf #bert #text-classification #generated_from_keras_callback #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* optimizer: {'name': 'Adam', 'clipnorm': 1.0, 'learni... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# BERT_Tweet_Sentiment_10k
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unk... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "BERT_Tweet_Sentiment_10k", "results": []}]} | joe5campbell/BERT_Tweet_Sentiment_10k | null | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #tf #bert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| BERT\_Tweet\_Sentiment\_10k
===========================
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.3891
* Train Accuracy: 0.8273
* Validation Loss: 0.4749
* Validation Accuracy: 0.8073
* Epoch: 0
Model des... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'clipnorm': 1.0, 'learning\\_rate': 3e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}\n* training\\_precision: float32",
"### Training results",... | [
"TAGS\n#transformers #tf #bert #text-classification #generated_from_keras_callback #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* optimizer: {'name': 'Adam', 'clipnorm': 1.0, 'learni... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# BERT_Tweet_Sentiment_50k_2eps
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on a... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "BERT_Tweet_Sentiment_50k_2eps", "results": []}]} | joe5campbell/BERT_Tweet_Sentiment_50k_2eps | null | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #tf #bert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| BERT\_Tweet\_Sentiment\_50k\_2eps
=================================
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.1131
* Train Accuracy: 0.9596
* Validation Loss: 0.6972
* Validation Accuracy: 0.8229
* Epoch: 1... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'clipnorm': 1.0, 'learning\\_rate': 3e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}\n* training\\_precision: float32",
"### Training results",... | [
"TAGS\n#transformers #tf #bert #text-classification #generated_from_keras_callback #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* optimizer: {'name': 'Adam', 'clipnorm': 1.0, 'learni... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# BERT_Tweet_Sentiment_50k_5eps
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on a... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "BERT_Tweet_Sentiment_50k_5eps", "results": []}]} | joe5campbell/BERT_Tweet_Sentiment_50k_5eps | null | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #tf #bert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| BERT\_Tweet\_Sentiment\_50k\_5eps
=================================
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0256
* Train Accuracy: 0.9913
* Validation Loss: 0.8905
* Validation Accuracy: 0.8291
* Epoch: 4... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'clipnorm': 1.0, 'learning\\_rate': 3e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}\n* training\\_precision: float32",
"### Training results",... | [
"TAGS\n#transformers #tf #bert #text-classification #generated_from_keras_callback #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* optimizer: {'name': 'Adam', 'clipnorm': 1.0, 'learni... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# BERT_Tweet_Sentiment_TEST
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an un... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "BERT_Tweet_Sentiment_TEST", "results": []}]} | joe5campbell/BERT_Tweet_Sentiment_TEST | null | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #tf #bert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| BERT\_Tweet\_Sentiment\_TEST
============================
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.5541
* Train Accuracy: 0.9375
* Validation Loss: 0.6546
* Validation Accuracy: 1.0
* Epoch: 1
Model desc... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'clipnorm': 1.0, 'learning\\_rate': 3e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}\n* training\\_precision: float32",
"### Training results",... | [
"TAGS\n#transformers #tf #bert #text-classification #generated_from_keras_callback #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* optimizer: {'name': 'Adam', 'clipnorm': 1.0, 'learni... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# ROBERTA_Tweet_Sentiment_50_2eps
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-sentiment](https://huggingface.... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "ROBERTA_Tweet_Sentiment_50_2eps", "results": []}]} | joe5campbell/ROBERTA_Tweet_Sentiment_50_2eps | null | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #tf #bert #text-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us
| ROBERTA\_Tweet\_Sentiment\_50\_2eps
===================================
This model is a fine-tuned version of cardiffnlp/twitter-roberta-base-sentiment on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.6625
* Train Accuracy: 0.6310
* Validation Loss: 0.8607
* Validation... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'clipnorm': 1.0, 'learning\\_rate': 3e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}\n* training\\_precision: float32",
"### Training results",... | [
"TAGS\n#transformers #tf #bert #text-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'clipnorm': 1.0, 'learning\\_rate': 3e-05, '... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# ROBERTA_Tweet_Sentiment_50k_2eps
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-sentiment](https://huggingface... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "ROBERTA_Tweet_Sentiment_50k_2eps", "results": []}]} | joe5campbell/ROBERTA_Tweet_Sentiment_50k_2eps | null | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #tf #bert #text-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us
| ROBERTA\_Tweet\_Sentiment\_50k\_2eps
====================================
This model is a fine-tuned version of cardiffnlp/twitter-roberta-base-sentiment on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.3553
* Train Accuracy: 0.8504
* Validation Loss: 0.5272
* Validati... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'clipnorm': 1.0, 'learning\\_rate': 3e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}\n* training\\_precision: float32",
"### Training results",... | [
"TAGS\n#transformers #tf #bert #text-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'clipnorm': 1.0, 'learning\\_rate': 3e-05, '... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# TEST
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It ach... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "TEST", "results": []}]} | joe5campbell/TEST | null | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #tf #bert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| TEST
====
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.4904
* Train Accuracy: 0.9375
* Validation Loss: 0.7016
* Validation Accuracy: 0.5
* Epoch: 1
Model description
-----------------
More information nee... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'clipnorm': 1.0, 'learning\\_rate': 3e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}\n* training\\_precision: float32",
"### Training results",... | [
"TAGS\n#transformers #tf #bert #text-classification #generated_from_keras_callback #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* optimizer: {'name': 'Adam', 'clipnorm': 1.0, 'learni... |
zero-shot-classification | transformers |
# bart-lage-mnli-yahoo-answers
## Model Description
This model takes [facebook/bart-large-mnli](https://huggingface.co/facebook/bart-large-mnli) and fine-tunes it on Yahoo Answers topic classification. It can be used to predict whether a topic label can be assigned to a given sequence, whether or not the label has b... | {"language": "en", "tags": ["text-classification", "pytorch"], "datasets": ["yahoo-answers"], "pipeline_tag": "zero-shot-classification"} | joeddav/bart-large-mnli-yahoo-answers | null | [
"transformers",
"pytorch",
"jax",
"bart",
"text-classification",
"zero-shot-classification",
"en",
"dataset:yahoo-answers",
"arxiv:1909.00161",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1909.00161"
] | [
"en"
] | TAGS
#transformers #pytorch #jax #bart #text-classification #zero-shot-classification #en #dataset-yahoo-answers #arxiv-1909.00161 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# bart-lage-mnli-yahoo-answers
## Model Description
This model takes facebook/bart-large-mnli and fine-tunes it on Yahoo Answers topic classification. It can be used to predict whether a topic label can be assigned to a given sequence, whether or not the label has been seen before.
You can play with an interactive ... | [
"# bart-lage-mnli-yahoo-answers",
"## Model Description\n\nThis model takes facebook/bart-large-mnli and fine-tunes it on Yahoo Answers topic classification. It can be used to predict whether a topic label can be assigned to a given sequence, whether or not the label has been seen before.\n\nYou can play with an ... | [
"TAGS\n#transformers #pytorch #jax #bart #text-classification #zero-shot-classification #en #dataset-yahoo-answers #arxiv-1909.00161 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# bart-lage-mnli-yahoo-answers",
"## Model Description\n\nThis model takes facebook/bart-large-mnli and fin... |
text-classification | transformers |
# distilbert-base-uncased-agnews-student
## Model Description
This model is distilled from the zero-shot classification pipeline on the unlabeled AG's News dataset using [this
script](https://github.com/huggingface/transformers/tree/master/examples/research_projects/zero-shot-distillation).
It is the result of the d... | {"language": "en", "license": "mit", "tags": ["text-classification", "pytorch", "tensorflow"], "datasets": ["ag_news"], "widget": [{"text": "Armed conflict has been a near-constant policial and economic burden."}, {"text": "Tom Brady won his seventh Super Bowl last night."}, {"text": "Dow falls more than 100 points aft... | joeddav/distilbert-base-uncased-agnews-student | null | [
"transformers",
"pytorch",
"tf",
"distilbert",
"text-classification",
"tensorflow",
"en",
"dataset:ag_news",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tf #distilbert #text-classification #tensorflow #en #dataset-ag_news #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# distilbert-base-uncased-agnews-student
## Model Description
This model is distilled from the zero-shot classification pipeline on the unlabeled AG's News dataset using this
script.
It is the result of the demo notebook
here, where more details
about the model can be found.
- Teacher model: roberta-large-mnli
- Te... | [
"# distilbert-base-uncased-agnews-student",
"## Model Description\n\nThis model is distilled from the zero-shot classification pipeline on the unlabeled AG's News dataset using this\nscript.\nIt is the result of the demo notebook\nhere, where more details\nabout the model can be found.\n\n- Teacher model: roberta... | [
"TAGS\n#transformers #pytorch #tf #distilbert #text-classification #tensorflow #en #dataset-ag_news #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# distilbert-base-uncased-agnews-student",
"## Model Description\n\nThis model is distilled from the zero-shot classification pipeline on ... |
text-classification | transformers |
# distilbert-base-uncased-go-emotions-student
## Model Description
This model is distilled from the zero-shot classification pipeline on the unlabeled GoEmotions dataset using [this
script](https://github.com/huggingface/transformers/tree/master/examples/research_projects/zero-shot-distillation).
It was trained with... | {"language": "en", "license": "mit", "tags": ["text-classification", "pytorch", "tensorflow"], "datasets": ["go_emotions"], "widget": [{"text": "I feel lucky to be here."}]} | joeddav/distilbert-base-uncased-go-emotions-student | null | [
"transformers",
"pytorch",
"tf",
"distilbert",
"text-classification",
"tensorflow",
"en",
"dataset:go_emotions",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tf #distilbert #text-classification #tensorflow #en #dataset-go_emotions #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# distilbert-base-uncased-go-emotions-student
## Model Description
This model is distilled from the zero-shot classification pipeline on the unlabeled GoEmotions dataset using this
script.
It was trained with mixed precision for 10 epochs and otherwise used the default script arguments.
## Intended Usage
The mode... | [
"# distilbert-base-uncased-go-emotions-student",
"## Model Description\n\nThis model is distilled from the zero-shot classification pipeline on the unlabeled GoEmotions dataset using this\nscript.\nIt was trained with mixed precision for 10 epochs and otherwise used the default script arguments.",
"## Intended ... | [
"TAGS\n#transformers #pytorch #tf #distilbert #text-classification #tensorflow #en #dataset-go_emotions #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# distilbert-base-uncased-go-emotions-student",
"## Model Description\n\nThis model is distilled from the zero-shot classif... |
zero-shot-classification | transformers |
# xlm-roberta-large-xnli
## Model Description
This model takes [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) and fine-tunes it on a combination of NLI data in 15 languages. It is intended to be used for zero-shot text classification, such as with the Hugging Face [ZeroShotClassificationPipeline](http... | {"language": ["multilingual", "en", "fr", "es", "de", "el", "bg", "ru", "tr", "ar", "vi", "th", "zh", "hi", "sw", "ur"], "license": "mit", "tags": ["text-classification", "pytorch", "tensorflow"], "datasets": ["multi_nli", "xnli"], "pipeline_tag": "zero-shot-classification", "widget": [{"text": "\u0417\u0430 \u043a\u04... | joeddav/xlm-roberta-large-xnli | null | [
"transformers",
"pytorch",
"tf",
"xlm-roberta",
"text-classification",
"tensorflow",
"zero-shot-classification",
"multilingual",
"en",
"fr",
"es",
"de",
"el",
"bg",
"ru",
"tr",
"ar",
"vi",
"th",
"zh",
"hi",
"sw",
"ur",
"dataset:multi_nli",
"dataset:xnli",
"arxiv:191... | null | 2022-03-02T23:29:05+00:00 | [
"1911.02116"
] | [
"multilingual",
"en",
"fr",
"es",
"de",
"el",
"bg",
"ru",
"tr",
"ar",
"vi",
"th",
"zh",
"hi",
"sw",
"ur"
] | TAGS
#transformers #pytorch #tf #xlm-roberta #text-classification #tensorflow #zero-shot-classification #multilingual #en #fr #es #de #el #bg #ru #tr #ar #vi #th #zh #hi #sw #ur #dataset-multi_nli #dataset-xnli #arxiv-1911.02116 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# xlm-roberta-large-xnli
## Model Description
This model takes xlm-roberta-large and fine-tunes it on a combination of NLI data in 15 languages. It is intended to be used for zero-shot text classification, such as with the Hugging Face ZeroShotClassificationPipeline.
## Intended Usage
This model is intended to be ... | [
"# xlm-roberta-large-xnli",
"## Model Description\n\nThis model takes xlm-roberta-large and fine-tunes it on a combination of NLI data in 15 languages. It is intended to be used for zero-shot text classification, such as with the Hugging Face ZeroShotClassificationPipeline.",
"## Intended Usage\n\nThis model is... | [
"TAGS\n#transformers #pytorch #tf #xlm-roberta #text-classification #tensorflow #zero-shot-classification #multilingual #en #fr #es #de #el #bg #ru #tr #ar #vi #th #zh #hi #sw #ur #dataset-multi_nli #dataset-xnli #arxiv-1911.02116 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
... |
text2text-generation | transformers |
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 21895237
- CO2 Emissions (in grams): 1.5688902203257171
## Validation Metrics
- Loss: 1.6614878177642822
- Rouge1: 32.4158
- Rouge2: 24.6194
- RougeL: 29.9278
- RougeLsum: 29.4988
- Gen Len: 58.7778
## Usage
You can use cURL to access this mo... | {"language": "unk", "tags": "autonlp", "datasets": ["joehdownardkainos/autonlp-data-intent-modelling"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 1.5688902203257171} | joehdownardkainos/autonlp-intent-modelling-21895237 | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"autonlp",
"unk",
"dataset:joehdownardkainos/autonlp-data-intent-modelling",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #bart #text2text-generation #autonlp #unk #dataset-joehdownardkainos/autonlp-data-intent-modelling #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 21895237
- CO2 Emissions (in grams): 1.5688902203257171
## Validation Metrics
- Loss: 1.6614878177642822
- Rouge1: 32.4158
- Rouge2: 24.6194
- RougeL: 29.9278
- RougeLsum: 29.4988
- Gen Len: 58.7778
## Usage
You can use cURL to access this mo... | [
"# Model Trained Using AutoNLP\n\n- Problem type: Summarization\n- Model ID: 21895237\n- CO2 Emissions (in grams): 1.5688902203257171",
"## Validation Metrics\n\n- Loss: 1.6614878177642822\n- Rouge1: 32.4158\n- Rouge2: 24.6194\n- RougeL: 29.9278\n- RougeLsum: 29.4988\n- Gen Len: 58.7778",
"## Usage\n\nYou can u... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #autonlp #unk #dataset-joehdownardkainos/autonlp-data-intent-modelling #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Summarization\n- Model ID: 21895237\n- CO2 Emissions (in ... |
text-classification | transformers | # bert-base-uncased-sem_eval_2010_task_8
Task: sem_eval_2010_task_8
Base Model: bert-base-uncased
Trained for 3 epochs
Batch-size: 6
Seed: 42
Test F1-Score: 0.8 | {} | joelniklaus/bert-base-uncased-sem_eval_2010_task_8 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #jax #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| # bert-base-uncased-sem_eval_2010_task_8
Task: sem_eval_2010_task_8
Base Model: bert-base-uncased
Trained for 3 epochs
Batch-size: 6
Seed: 42
Test F1-Score: 0.8 | [
"# bert-base-uncased-sem_eval_2010_task_8\n\nTask: sem_eval_2010_task_8\n\nBase Model: bert-base-uncased\n\nTrained for 3 epochs\n\nBatch-size: 6\n\nSeed: 42\n\nTest F1-Score: 0.8"
] | [
"TAGS\n#transformers #pytorch #tf #jax #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# bert-base-uncased-sem_eval_2010_task_8\n\nTask: sem_eval_2010_task_8\n\nBase Model: bert-base-uncased\n\nTrained for 3 epochs\n\nBatch-size: 6\n\nSeed: 42\n\nTest F1-Score: 0.8"
] |
token-classification | transformers | # distilbert-base-german-cased-ler
Task: ler
Base Model: distilbert-base-german-cased
Trained for 3 epochs
Batch-size: 12
Seed: 42
Test F1-Score: 0.936 | {} | joelniklaus/distilbert-based-german-cased-ler | null | [
"transformers",
"pytorch",
"tf",
"distilbert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #distilbert #token-classification #autotrain_compatible #endpoints_compatible #region-us
| # distilbert-base-german-cased-ler
Task: ler
Base Model: distilbert-base-german-cased
Trained for 3 epochs
Batch-size: 12
Seed: 42
Test F1-Score: 0.936 | [
"# distilbert-base-german-cased-ler\n\nTask: ler\n\nBase Model: distilbert-base-german-cased\n\nTrained for 3 epochs\n\nBatch-size: 12\n\nSeed: 42\n\nTest F1-Score: 0.936"
] | [
"TAGS\n#transformers #pytorch #tf #distilbert #token-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# distilbert-base-german-cased-ler\n\nTask: ler\n\nBase Model: distilbert-base-german-cased\n\nTrained for 3 epochs\n\nBatch-size: 12\n\nSeed: 42\n\nTest F1-Score: 0.936"
] |
token-classification | transformers | # gbert-base-ler
Task: ler
Base Model: deepset/gbert-base
Trained for 3 epochs
Batch-size: 6
Seed: 42
Test F1-Score: 0.956 | {} | joelniklaus/gbert-base-ler | null | [
"transformers",
"pytorch",
"tf",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us
| # gbert-base-ler
Task: ler
Base Model: deepset/gbert-base
Trained for 3 epochs
Batch-size: 6
Seed: 42
Test F1-Score: 0.956 | [
"# gbert-base-ler\n\nTask: ler\n\nBase Model: deepset/gbert-base\n\nTrained for 3 epochs\n\nBatch-size: 6\n\nSeed: 42\n\nTest F1-Score: 0.956"
] | [
"TAGS\n#transformers #pytorch #tf #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# gbert-base-ler\n\nTask: ler\n\nBase Model: deepset/gbert-base\n\nTrained for 3 epochs\n\nBatch-size: 6\n\nSeed: 42\n\nTest F1-Score: 0.956"
] |
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. -->
# POCTS
This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on an unknown data... | {"license": "apache-2.0", "tags": ["summarization"], "metrics": ["rouge"]} | jogonba2/POCTS | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"summarization",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #summarization #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| POCTS
=====
This model is a fine-tuned version of facebook/bart-large on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 3.0970
* Rouge1: 26.1391
* Rouge2: 7.3101
* Rougel: 19.1217
* Rougelsum: 21.9706
* Gen Len: 46.2245
Model description
-----------------
More information 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* 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\\_ratio: ... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #summarization #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: 5e-05\n* train\\_batch\\_size: 4... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.