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text-classification | transformers |
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text-generation | transformers | # BashGPT-Neo
## What is it ?
BashGPT-Neo is a [Neural Program Synthesis](https://www.microsoft.com/en-us/research/project/neural-program-synthesis/) Model for Bash Commands and Shell Scripts. Trained on the data provided by [NLC2CMD](https://nlc2cmd.us-east.mybluemix.net/). It is fine-tuned version of GPTNeo-125M by ... | {"language": ["English", "Bash"], "tags": ["code-representation-learning", "program-synthesis"], "datasets": ["nlc2cmd"], "metrics": ["metric1", "metric2"], "thumbnail": "Neural Program Synthesis for Bash"} | reshinthadith/BashGPTNeo | null | [
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"dataset:nlc2cmd",
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"has_space",
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] | null | 2022-03-02T23:29:05+00:00 | [] | [
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] | TAGS
#transformers #pytorch #gpt_neo #text-generation #code-representation-learning #program-synthesis #dataset-nlc2cmd #autotrain_compatible #endpoints_compatible #has_space #region-us
| # BashGPT-Neo
## What is it ?
BashGPT-Neo is a Neural Program Synthesis Model for Bash Commands and Shell Scripts. Trained on the data provided by NLC2CMD. It is fine-tuned version of GPTNeo-125M by EleutherAI.
## Usage
## Core Contributors
- Reshinth Adithyan
- Aditya Thuruvas | [
"# BashGPT-Neo",
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"## What is it ?\nBashGPT-Neo is a Neural Program Synthesis Model for Bash Commands and Shell Scripts.... |
image-classification | transformers |
# string_instrument_detector
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/n... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | rexoscare/string_instrument_detector | null | [
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"huggingpics",
"model-index",
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"endpoints_compatible",
"has_space",
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] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# string_instrument_detector
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
#### Banjo
!Banjo
#### Guitar
!Guitar
#### Mandolin
!Mandolin
#### Ukulele
!Ukulele | [
"# string_instrument_detector\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.",
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"#### Banjo\n\n!Banjo",
"#### Guitar\n\n!Guitar",
"#### Mandolin\n\n!Mandolin",... | [
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text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 467612250
- CO2 Emissions (in grams): 73.72876780772296
## Validation Metrics
- Loss: 0.18261319398880005
- Accuracy: 0.9541659567217584
- Precision: 0.9530625832223701
- Recall: 0.9572049481778669
- AUC: 0.9901737875196123
- F1: 0.9551... | {"language": "unk", "tags": "autonlp", "datasets": ["rexxar96/autonlp-data-roberta-large-finetuned"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 73.72876780772296} | rexxar96/autonlp-roberta-large-finetuned-467612250 | null | [
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|
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 467612250
- CO2 Emissions (in grams): 73.72876780772296
## Validation Metrics
- Loss: 0.18261319398880005
- Accuracy: 0.9541659567217584
- Precision: 0.9530625832223701
- Recall: 0.9572049481778669
- AUC: 0.9901737875196123
- F1: 0.9551... | [
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text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 456211724
- CO2 Emissions (in grams): 22.28263989637389
## Validation Metrics
- Loss: 0.23710417747497559
- Accuracy: 0.9119100357812234
- Precision: 0.8882611424984307
- Recall: 0.9461718488799733
- AUC: 0.974790366001874
- F1: 0.91630... | {"language": "unk", "tags": "autonlp", "datasets": ["rexxar96/autonlp-data-sentiment-analysis"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 22.28263989637389} | rexxar96/autonlp-sentiment-analysis-456211724 | null | [
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|
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 456211724
- CO2 Emissions (in grams): 22.28263989637389
## Validation Metrics
- Loss: 0.23710417747497559
- Accuracy: 0.9119100357812234
- Precision: 0.8882611424984307
- Recall: 0.9461718488799733
- AUC: 0.974790366001874
- F1: 0.91630... | [
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text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-marc-en
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["amazon_reviews_multi"], "model-index": [{"name": "xlm-roberta-base-finetuned-marc-en", "results": []}]} | rzsgrt/xlm-roberta-base-finetuned-marc-en | null | [
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"license:mit",
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"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
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| xlm-roberta-base-finetuned-marc-en
==================================
This model is a fine-tuned version of xlm-roberta-base on the amazon\_reviews\_multi dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9569
* Mae: 0.5244
Model description
-----------------
More information needed
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_... |
text-generation | transformers |
# Cpt Rogers DialoGPT Model | {"tags": ["conversational"]} | rhollings/DialoGPT_small_steverogers | null | [
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"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Cpt Rogers DialoGPT Model | [
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] |
null | null | https://escape-net.eu/groups/film-complet-venom-let-there-be-carnage-streaming-vf-gratuit-en-francais/
https://escape-net.eu/groups/venom-let-there-be-carnage-2021-streaming-vf-film-complet-en-francais/
https://escape-net.eu/groups/venom-let-there-be-carnage-streaming-vf-en-hd-fr/
https://escape-net.eu/groups/venom-let... | {} | rhtnr/erhthh | null | [
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null | null | https://escape-net.eu/groups/film-complet-venom-let-there-be-carnage-streaming-vf-gratuit-en-francais/
https://escape-net.eu/groups/venom-let-there-be-carnage-2021-streaming-vf-film-complet-en-francais/
https://escape-net.eu/groups/venom-let-there-be-carnage-streaming-vf-en-hd-fr/
https://escape-net.eu/groups/venom-let... | {} | rhtnr/ssgtrh | null | [
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fill-mask | transformers | hello
| {} | ricardo-filho/BERT-pt-institutional | 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
| hello
| [] | [
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] |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | ricardo-filho/bert-base-portuguese-cased-nli-assin-2 | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #has_space #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... | [
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sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | ricardo-filho/bert-base-portuguese-cased-nli-assin | null | [
"sentence-transformers",
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"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can u... | [
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sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | ricardo-filho/bert-portuguese-cased-nli-assin-assin-2 | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can u... | [
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sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | ricardo-filho/bertimbau_base_snli_mnrl | null | [
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"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
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] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can u... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
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sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | ricardo-filho/sbertimbau-base-allnli-mnrl | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can u... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\n... | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or se... |
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"} | ricardo-filho/sbertimbau-base-nli-sts | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can u... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\n... | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or se... |
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"} | ricardo-filho/sbertimbau-base-quora-multitask | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can u... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\n... | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or se... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 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"} | ricardo-filho/sbertimbau-large-allnli-mnrl | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 1024 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 ... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\... | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or s... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 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"} | ricardo-filho/sbertimbau-large-nli-sts | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 1024 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 ... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\... | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or s... |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 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"} | ricardo-filho/sbertimbau-large-quora-multitask | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 1024 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 ... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\... | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or s... |
text-generation | transformers |
# Childe DialoGPT Model | {"tags": ["conversational"]} | richiellei/Childe | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Childe DialoGPT Model | [
"# Childe DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Childe DialoGPT Model"
] |
text-generation | transformers |
# Childe3 DialoGPT Model | {"tags": ["conversational"]} | richiellei/Childe3 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Childe3 DialoGPT Model | [
"# Childe3 DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Childe3 DialoGPT Model"
] |
text-generation | transformers |
# Rick DialoGPT Model | {"tags": ["conversational"]} | richiellei/DialoGPT-small-rick | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Rick DialoGPT Model | [
"# Rick DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Rick DialoGPT Model"
] |
text-generation | transformers |
# Childe Chatbot Model | {"tags": ["conversational"]} | richielleisart/Childe | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Childe Chatbot Model | [
"# Childe Chatbot Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Childe Chatbot Model"
] |
text-generation | transformers |
# Misaki Ayuzawa Model | {"tags": ["conversational"]} | ridwanpratama/DialoGPT-small-misaki | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Misaki Ayuzawa Model | [
"# Misaki Ayuzawa Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Misaki Ayuzawa Model"
] |
fill-mask | transformers | Ushbu model, HuggingFace-da RoBERTa transformatorini amalga oshirishga asoslangan. Bizning RoBERTa dasturimiz 12 ta diqqat boshi va 6 ta qatlamdan foydalanadi, natijada 72 ta aniq e'tibor mexanizmlari paydo bo'ladi. Biz har bir kirish satridagi tokenlarning 15 foizini niqoblaydigan RoBERTa-dan dastlabki tekshirish prot... | {} | rifkat/pubchem_1M | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"doi:10.57967/hf/0177",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #fill-mask #doi-10.57967/hf/0177 #autotrain_compatible #endpoints_compatible #region-us
| Ushbu model, HuggingFace-da RoBERTa transformatorini amalga oshirishga asoslangan. Bizning RoBERTa dasturimiz 12 ta diqqat boshi va 6 ta qatlamdan foydalanadi, natijada 72 ta aniq e'tibor mexanizmlari paydo bo'ladi. Biz har bir kirish satridagi tokenlarning 15 foizini niqoblaydigan RoBERTa-dan dastlabki tekshirish prot... | [] | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #doi-10.57967/hf/0177 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers |
<p><b>UzRoBerta model.</b>
Pre-prepared model in Uzbek (Cyrillic and latin script) to model the masked language and predict the next sentences.
<p><b>How to use.</b>
You can use this model directly with a pipeline for masked language modeling:
<pre><code class="language-python">
from transformers import pipeline
... | {"language": ["uz"], "license": "apache-2.0", "tags": ["transformers", "mit", "robert", "uzrobert", "uzbek", "cyrillic", "latin"], "widget": [{"text": "Kuchli yomg\u2018irlar tufayli bir qator <mask> kuchli sel oqishi kuzatildi.", "example_title": "Latin script"}, {"text": "\u0410\u043b\u0438\u0448\u0435\u0440 \u041d\u... | rifkat/uztext-3Gb-BPE-Roberta | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"mit",
"robert",
"uzrobert",
"uzbek",
"cyrillic",
"latin",
"uz",
"doi:10.57967/hf/0210",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"uz"
] | TAGS
#transformers #pytorch #roberta #fill-mask #mit #robert #uzrobert #uzbek #cyrillic #latin #uz #doi-10.57967/hf/0210 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
<p><b>UzRoBerta model.</b>
Pre-prepared model in Uzbek (Cyrillic and latin script) to model the masked language and predict the next sentences.
<p><b>How to use.</b>
You can use this model directly with a pipeline for masked language modeling:
<pre><code class="language-python">
from transformers import pipeline
... | [] | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #mit #robert #uzrobert #uzbek #cyrillic #latin #uz #doi-10.57967/hf/0210 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
fill-mask | transformers | <p><b>UzRoBerta model.</b>
Pre-prepared model in Uzbek (Cyrillic script) to model the masked language and predict the next sentences.
<p><b>Training data.</b>
UzBERT model was pretrained on ≈167K news articles (≈568Mb).
| {} | rifkat/uztext_568Mb_Roberta_BPE | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| <p><b>UzRoBerta model.</b>
Pre-prepared model in Uzbek (Cyrillic script) to model the masked language and predict the next sentences.
<p><b>Training data.</b>
UzBERT model was pretrained on ≈167K news articles (≈568Mb).
| [] | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \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. -->
# bert-base-cased-finetuned-COVID-tweets
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bert-base-cased-finetuned-COVID-tweets", "results": []}]} | ringabelle/bert-base-cased-finetuned-COVID-tweets | 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
| bert-base-cased-finetuned-COVID-tweets
======================================
This model is a fine-tuned version of bert-base-cased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 2.2694
Model description
-----------------
More information needed
Intended uses & limitatio... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
"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: 8\n... |
text-generation | transformers |
# japanese-gpt-1b
This repository provides a 1.3B-parameter Japanese GPT model. The model was trained by [rinna Co., Ltd.](https://corp.rinna.co.jp/)
# How to use the model
~~~~
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pret... | {"language": "ja", "license": "mit", "tags": ["ja", "japanese", "gpt", "text-generation", "lm", "nlp"], "datasets": ["cc100", "wikipedia", "c4"], "thumbnail": "https://github.com/rinnakk/japanese-pretrained-models/blob/master/rinna.png", "widget": [{"text": "\u897f\u7530\u5e7e\u591a\u90ce\u306f\u3001"}]} | rinna/japanese-gpt-1b | null | [
"transformers",
"pytorch",
"safetensors",
"gpt2",
"text-generation",
"ja",
"japanese",
"gpt",
"lm",
"nlp",
"dataset:cc100",
"dataset:wikipedia",
"dataset:c4",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #safetensors #gpt2 #text-generation #ja #japanese #gpt #lm #nlp #dataset-cc100 #dataset-wikipedia #dataset-c4 #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# japanese-gpt-1b
!rinna-icon
This repository provides a 1.3B-parameter Japanese GPT model. The model was trained by rinna Co., Ltd.
# How to use the model
~~~~
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("rinna/japanese-gpt-1b", use_fast=Fal... | [
"# japanese-gpt-1b\n\n!rinna-icon\n\nThis repository provides a 1.3B-parameter Japanese GPT model. The model was trained by rinna Co., Ltd.",
"# How to use the model\n\n~~~~\nimport torch\nfrom transformers import AutoTokenizer, AutoModelForCausalLM\n\ntokenizer = AutoTokenizer.from_pretrained(\"rinna/japanese-gp... | [
"TAGS\n#transformers #pytorch #safetensors #gpt2 #text-generation #ja #japanese #gpt #lm #nlp #dataset-cc100 #dataset-wikipedia #dataset-c4 #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# japanese-gpt-1b\n\n!rinna-icon\n\nThis repository provides a... |
text-generation | transformers |
# japanese-gpt2-medium
This repository provides a medium-sized Japanese GPT-2 model. The model was trained using code from Github repository [rinnakk/japanese-pretrained-models](https://github.com/rinnakk/japanese-pretrained-models) by [rinna Co., Ltd.](https://corp.rinna.co.jp/)
# How to... | {"language": "ja", "license": "mit", "tags": ["ja", "japanese", "gpt2", "text-generation", "lm", "nlp"], "datasets": ["cc100", "wikipedia"], "thumbnail": "https://github.com/rinnakk/japanese-gpt2/blob/master/rinna.png", "widget": [{"text": "\u751f\u547d\u3001\u5b87\u5b99\u3001\u305d\u3057\u3066\u4e07\u7269\u306b\u3064\... | rinna/japanese-gpt2-medium | null | [
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"gpt2",
"text-generation",
"ja",
"japanese",
"lm",
"nlp",
"dataset:cc100",
"dataset:wikipedia",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #tf #jax #safetensors #gpt2 #text-generation #ja #japanese #lm #nlp #dataset-cc100 #dataset-wikipedia #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# japanese-gpt2-medium
!rinna-icon
This repository provides a medium-sized Japanese GPT-2 model. The model was trained using code from Github repository rinnakk/japanese-pretrained-models by rinna Co., Ltd.
# How to use the model
~~~~
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTo... | [
"# japanese-gpt2-medium\n\n!rinna-icon\n\nThis repository provides a medium-sized Japanese GPT-2 model. The model was trained using code from Github repository rinnakk/japanese-pretrained-models by rinna Co., Ltd.",
"# How to use the model\n\n~~~~\nfrom transformers import AutoTokenizer, AutoModelForCausalLM\n\nt... | [
"TAGS\n#transformers #pytorch #tf #jax #safetensors #gpt2 #text-generation #ja #japanese #lm #nlp #dataset-cc100 #dataset-wikipedia #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# japanese-gpt2-medium\n\n!rinna-icon\n\nThis repository provides a me... |
text-generation | transformers |
# japanese-gpt2-small
This repository provides a small-sized Japanese GPT-2 model. The model was trained using code from Github repository [rinnakk/japanese-pretrained-models](https://github.com/rinnakk/japanese-pretrained-models) by [rinna Co., Ltd.](https://corp.rinna.co.jp/)
# How to u... | {"language": "ja", "license": "mit", "tags": ["ja", "japanese", "gpt2", "text-generation", "lm", "nlp"], "datasets": ["cc100", "wikipedia"], "thumbnail": "https://github.com/rinnakk/japanese-gpt2/blob/master/rinna.png", "widget": [{"text": "\u751f\u547d\u3001\u5b87\u5b99\u3001\u305d\u3057\u3066\u4e07\u7269\u306b\u3064\... | rinna/japanese-gpt2-small | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"gpt2",
"text-generation",
"ja",
"japanese",
"lm",
"nlp",
"dataset:cc100",
"dataset:wikipedia",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #tf #safetensors #gpt2 #text-generation #ja #japanese #lm #nlp #dataset-cc100 #dataset-wikipedia #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# japanese-gpt2-small
!rinna-icon
This repository provides a small-sized Japanese GPT-2 model. The model was trained using code from Github repository rinnakk/japanese-pretrained-models by rinna Co., Ltd.
# How to use the model
~~~~
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoToke... | [
"# japanese-gpt2-small\n\n!rinna-icon\n\nThis repository provides a small-sized Japanese GPT-2 model. The model was trained using code from Github repository rinnakk/japanese-pretrained-models by rinna Co., Ltd.",
"# How to use the model\n\n~~~~\nfrom transformers import AutoTokenizer, AutoModelForCausalLM\n\ntok... | [
"TAGS\n#transformers #pytorch #tf #safetensors #gpt2 #text-generation #ja #japanese #lm #nlp #dataset-cc100 #dataset-wikipedia #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# japanese-gpt2-small\n\n!rinna-icon\n\nThis repository provides a small-si... |
text-generation | transformers |
# japanese-gpt2-xsmall
This repository provides an extra-small-sized Japanese GPT-2 model. The model was trained using code from Github repository [rinnakk/japanese-pretrained-models](https://github.com/rinnakk/japanese-pretrained-models) by [rinna Co., Ltd.](https://corp.rinna.co.jp/)
# ... | {"language": "ja", "license": "mit", "tags": ["ja", "japanese", "gpt2", "text-generation", "lm", "nlp"], "datasets": ["cc100", "wikipedia"], "thumbnail": "https://github.com/rinnakk/japanese-gpt2/blob/master/rinna.png", "widget": [{"text": "\u751f\u547d\u3001\u5b87\u5b99\u3001\u305d\u3057\u3066\u4e07\u7269\u306b\u3064\... | rinna/japanese-gpt2-xsmall | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"gpt2",
"text-generation",
"ja",
"japanese",
"lm",
"nlp",
"dataset:cc100",
"dataset:wikipedia",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #tf #safetensors #gpt2 #text-generation #ja #japanese #lm #nlp #dataset-cc100 #dataset-wikipedia #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# japanese-gpt2-xsmall
!rinna-icon
This repository provides an extra-small-sized Japanese GPT-2 model. The model was trained using code from Github repository rinnakk/japanese-pretrained-models by rinna Co., Ltd.
# How to use the model
~~~~
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = ... | [
"# japanese-gpt2-xsmall\n\n!rinna-icon\n\nThis repository provides an extra-small-sized Japanese GPT-2 model. The model was trained using code from Github repository rinnakk/japanese-pretrained-models by rinna Co., Ltd.",
"# How to use the model\n\n~~~~\nfrom transformers import AutoTokenizer, AutoModelForCausalL... | [
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"# japanese-gpt2-xsmall\n\n!rinna-icon\n\nThis repository provides an extra-... |
fill-mask | transformers |
# japanese-roberta-base
This repository provides a base-sized Japanese RoBERTa model. The model was trained using code from Github repository [rinnakk/japanese-pretrained-models](https://github.com/rinnakk/japanese-pretrained-models) by [rinna Co., Ltd.](https://corp.rinna.co.jp/)
# How t... | {"language": "ja", "license": "mit", "tags": ["ja", "japanese", "roberta", "masked-lm", "nlp"], "datasets": ["cc100", "wikipedia"], "thumbnail": "https://github.com/rinnakk/japanese-gpt2/blob/master/rinna.png", "mask_token": "[MASK]", "widget": [{"text": "[CLS]4\u5e74\u306b1\u5ea6[MASK]\u306f\u958b\u304b\u308c\u308b\u3... | rinna/japanese-roberta-base | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"roberta",
"fill-mask",
"ja",
"japanese",
"masked-lm",
"nlp",
"dataset:cc100",
"dataset:wikipedia",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #tf #safetensors #roberta #fill-mask #ja #japanese #masked-lm #nlp #dataset-cc100 #dataset-wikipedia #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# japanese-roberta-base
!rinna-icon
This repository provides a base-sized Japanese RoBERTa model. The model was trained using code from Github repository rinnakk/japanese-pretrained-models by rinna Co., Ltd.
# How to load the model
~~~~
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = Auto... | [
"# japanese-roberta-base\n\n!rinna-icon\n\nThis repository provides a base-sized Japanese RoBERTa model. The model was trained using code from Github repository rinnakk/japanese-pretrained-models by rinna Co., Ltd.",
"# How to load the model\n\n~~~~\nfrom transformers import AutoTokenizer, AutoModelForMaskedLM\n\... | [
"TAGS\n#transformers #pytorch #tf #safetensors #roberta #fill-mask #ja #japanese #masked-lm #nlp #dataset-cc100 #dataset-wikipedia #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# japanese-roberta-base\n\n!rinna-icon\n\nThis repository provides a base-sized Japanese RoBERTa m... |
text-generation | transformers |
# Harry Potter model | {"tags": ["conversational"]} | rinz/DialoGPT-small-Harry-Potterrr | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Harry Potter model | [
"# Harry Potter model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Harry Potter model"
] |
text-classification | transformers |
# Hate Speech Detector
This model is a fork of the [bert-based-uncased-hatespeech-movies](https://huggingface.co/uhhlt/bert-based-uncased-hatespeech-movies) model. It is used to classify text as **normal**, **offensive**, **hatespeech**. The model is initially a pre-trained transformer model(bert-based-uncased) which... | {"language": "en", "datasets": ["twitter", "movies subtitles"], "tag": "text-classification"} | risingodegua/hate-speech-detector | null | [
"transformers",
"tf",
"bert",
"text-classification",
"en",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #tf #bert #text-classification #en #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Hate Speech Detector
This model is a fork of the bert-based-uncased-hatespeech-movies model. It is used to classify text as normal, offensive, hatespeech. The model is initially a pre-trained transformer model(bert-based-uncased) which is further trained on Twitter comments which can be normal, offensive and hate t... | [
"# Hate Speech Detector \nThis model is a fork of the bert-based-uncased-hatespeech-movies model. It is used to classify text as normal, offensive, hatespeech. The model is initially a pre-trained transformer model(bert-based-uncased) which is further trained on Twitter comments which can be normal, offensive and h... | [
"TAGS\n#transformers #tf #bert #text-classification #en #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Hate Speech Detector \nThis model is a fork of the bert-based-uncased-hatespeech-movies model. It is used to classify text as normal, offensive, hatespeech. The model is initially a pr... |
null | sklearn |
## Wine Quality classification
### A Simple Example of Scikit-learn Pipeline
> Inspired by https://towardsdatascience.com/a-simple-example-of-pipeline-in-machine-learning-with-scikit-learn-e726ffbb6976 by Saptashwa Bhattacharyya
### How to use
```python
from huggingface_hub import hf_hub_url, cached_download
impo... | {"tags": ["structured-data-classification", "sklearn", "joblib"], "dataset": ["wine-quality"], "widget": {"structuredData": {"fixed_acidity": [7.4, 7.8, 10.3], "volatile_acidity": [0.7, 0.88, 0.32], "citric_acid": [0, 0, 0.45], "residual_sugar": [1.9, 2.6, 6.4], "chlorides": [0.076, 0.098, 0.073], "free_sulfur_dioxide"... | risingodegua/wine-quality-model | null | [
"sklearn",
"joblib",
"structured-data-classification",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sklearn #joblib #structured-data-classification #has_space #region-us
| Wine Quality classification
---------------------------
### A Simple Example of Scikit-learn Pipeline
>
> Inspired by URL by Saptashwa Bhattacharyya
>
>
>
### How to use
#### Get sample data from this repo
#### Get your prediction
#### Eval
### Disclaimer
No red wine was drunk (unfortunately) whil... | [
"### A Simple Example of Scikit-learn Pipeline\n\n\n\n> \n> Inspired by URL by Saptashwa Bhattacharyya\n> \n> \n>",
"### How to use",
"#### Get sample data from this repo",
"#### Get your prediction",
"#### Eval",
"### Disclaimer\n\n\nNo red wine was drunk (unfortunately) while training this model"
] | [
"TAGS\n#sklearn #joblib #structured-data-classification #has_space #region-us \n",
"### A Simple Example of Scikit-learn Pipeline\n\n\n\n> \n> Inspired by URL by Saptashwa Bhattacharyya\n> \n> \n>",
"### How to use",
"#### Get sample data from this repo",
"#### Get your prediction",
"#### Eval",
"### Di... |
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": {"name": "Text Classification", "type": "text-classification"}, "dataset": {"name": "glue", "type": "glue", "ar... | riyadhctg/distilbert-base-uncased-finetuned-cola | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #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: 0.7691
* Matthews Correlation: 0.5527
Model description
-----------------
More informa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #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... |
null | null | https://sites.google.com/view/watchonline-full-hd-we-need-to/
https://sites.google.com/view/watch-hdthegateway2021fullmovi/
https://sites.google.com/view/downloadwatch-hdwildindian2021/
https://sites.google.com/view/putlocker123movieswatchkaren20/
https://sites.google.com/view/full-hdzone4142021moviewatchon/
https://si... | {} | rizky22/IndoBERT | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| URL
URL
URL
URL
URL
URL
URL
URL
URL
URL
URL
URL
URL
URL
URL
URL
URL
URL
URL | [] | [
"TAGS\n#region-us \n"
] |
text-generation | transformers |
# Model name
Magic The Generating
## Model description
This is a fine tuned GPT-2 model trained on a corpus of all available English language Magic the Gathering card flavour texts.
## Intended uses & limitations
This is intended only for use in generating new, novel, and sometimes surprising, MtG like flavour tex... | {"widget": [{"text": "Even the Dwarves"}, {"text": "The secrets of"}]} | rjbownes/Magic-The-Generating | null | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model name
Magic The Generating
## Model description
This is a fine tuned GPT-2 model trained on a corpus of all available English language Magic the Gathering card flavour texts.
## Intended uses & limitations
This is intended only for use in generating new, novel, and sometimes surprising, MtG like flavour tex... | [
"# Model name\nMagic The Generating",
"## Model description\n\nThis is a fine tuned GPT-2 model trained on a corpus of all available English language Magic the Gathering card flavour texts.",
"## Intended uses & limitations\n\nThis is intended only for use in generating new, novel, and sometimes surprising, MtG... | [
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model name\nMagic The Generating",
"## Model description\n\nThis is a fine tuned GPT-2 model trained on a corpus of all available English language Magic the Gatheri... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-colab
This model is a fine-tuned version of [facebook/hubert-large-ls960-ft](https://huggingface.co/fac... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-colab", "results": []}]} | rkmt/wav2vec2-base-timit-demo-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"hubert",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #hubert #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-timit-demo-colab
==============================
This model is a fine-tuned version of facebook/hubert-large-ls960-ft on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0280
* Wer: 0.0082
Model description
-----------------
More information needed
Intended use... | [
"### 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 #hubert #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\... |
text-generation | transformers | ---
#12 epochs, each batch size 2, gradient accumulation steps 2, tail 20000 | {"tags": ["conversational"]} | rlagusrlagus123/XTC20000 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| ---
#12 epochs, each batch size 2, gradient accumulation steps 2, tail 20000 | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers | ---
#12 epochs, each batch size 4, gradient accumulation steps 1, tail 4096.
#THIS SEEMS TO BE THE OPTIMAL SETUP. | {"tags": ["conversational"]} | rlagusrlagus123/XTC4096 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| ---
#12 epochs, each batch size 4, gradient accumulation steps 1, tail 4096.
#THIS SEEMS TO BE THE OPTIMAL SETUP. | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
null | null | hello
| {} | rlu39gt/xlm-r-test | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| hello
| [] | [
"TAGS\n#region-us \n"
] |
text-generation | transformers |
# Steven Universe DialoGPT Model | {"tags": ["conversational"]} | rmicheal48/DialoGPT-small-steven_universe | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Steven Universe DialoGPT Model | [
"# Steven Universe DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Steven Universe DialoGPT Model"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-xls-r-300m-uk
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav... | {"language": ["uk"], "license": "mit", "tags": ["automatic-speech-recognition", "common_voice", "generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-xls-r-300m-uk", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Co... | robinhad/wav2vec2-xls-r-300m-uk | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"common_voice",
"generated_from_trainer",
"uk",
"dataset:common_voice",
"license:mit",
"model-index",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"uk"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #uk #dataset-common_voice #license-mit #model-index #endpoints_compatible #has_space #region-us
| wav2vec2-xls-r-300m-uk
======================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0927
* Wer: 0.1222
* Cer: 0.0204
Model description
-----------------
More information needed
Intended uses & ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 40\n* eval\\_batch\\_size: 40\n* seed: 42\n* gradient\\_accumulation\\_steps: 6\n* total\\_train\\_batch\\_size: 240\n* optimizer: Adam with betas=(0.9,0.999) and epsilo... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #uk #dataset-common_voice #license-mit #model-index #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learni... |
null | null | Toy Wordlevel Tokenizer created for testing.
Code used for its creation:
```
from tokenizers import Tokenizer, normalizers, pre_tokenizers
from tokenizers.models import WordLevel
from tokenizers.normalizers import NFD, Lowercase, StripAccents
from tokenizers.pre_tokenizers import Digits, Whitespace
from tokenizers.pr... | {} | robot-test/dummy-tokenizer-wordlevel | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| Toy Wordlevel Tokenizer created for testing.
Code used for its creation:
| [] | [
"TAGS\n#region-us \n"
] |
null | null | Old version of the CLIP fast tokenizer
cf [this issue](https://github.com/huggingface/transformers/issues/12648) on transformers | {} | robot-test/old-clip-tokenizer | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| Old version of the CLIP fast tokenizer
cf this issue on transformers | [] | [
"TAGS\n#region-us \n"
] |
null | null | Info here: https://github.com/josephrocca/openai-clip-js | {} | rocca/openai-clip-js | null | [
"onnx",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#onnx #has_space #region-us
| Info here: URL | [] | [
"TAGS\n#onnx #has_space #region-us \n"
] |
text-classification | transformers | labeled by "YES" : 1, "NO" : 0, "No Answer" : 2
fine tuned by klue/roberta-large | {} | rockmiin/ko-boolq-model | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us
| labeled by "YES" : 1, "NO" : 0, "No Answer" : 2
fine tuned by klue/roberta-large | [] | [
"TAGS\n#transformers #pytorch #roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-generation | transformers |
# Issei DialoGPT Model | {"tags": ["conversational"]} | rodrigodz/DialoGPT-medium-dxd | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Issei DialoGPT Model | [
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"# Issei DialoGPT Model"
] |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 534915130
- CO2 Emissions (in grams): 1.4862856774320061
## Validation Metrics
- Loss: 0.37066277861595154
- Accuracy: 0.9204545454545454
- Macro F1: 0.9103715740678612
- Micro F1: 0.9204545454545455
- Weighted F1: 0.91968716075099... | {"language": "unk", "tags": "autonlp", "datasets": ["rodrigogelacio/autonlp-data-department-classification"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 1.4862856774320061} | rodrigogelacio/autonlp-department-classification-534915130 | null | [
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"dataset:rodrigogelacio/autonlp-data-department-classification",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"unk"
] | TAGS
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|
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 534915130
- CO2 Emissions (in grams): 1.4862856774320061
## Validation Metrics
- Loss: 0.37066277861595154
- Accuracy: 0.9204545454545454
- Macro F1: 0.9103715740678612
- Micro F1: 0.9204545454545455
- Weighted F1: 0.91968716075099... | [
"# Model Trained Using AutoNLP\n\n- Problem type: Multi-class Classification\n- Model ID: 534915130\n- CO2 Emissions (in grams): 1.4862856774320061",
"## Validation Metrics\n\n- Loss: 0.37066277861595154\n- Accuracy: 0.9204545454545454\n- Macro F1: 0.9103715740678612\n- Micro F1: 0.9204545454545455\n- Weighted F1... | [
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"# Model Trained Using AutoNLP\n\n- Problem type: Multi-class Classification\n- Model ID: 534915130\n-... |
text-classification | transformers |
# Model name
## Model description
I took a bert-base-multilingual-cased model from huggingface and finetuned it on SAIL 2017 dataset.
## Intended uses & limitations
#### How to use
```python
# You can include sample code which will be formatted
#Coming soon!
```
#### Limitations and bias
Provide examples of l... | {"language": ["hi", "en"], "tags": ["hi", "en", "codemix"], "datasets": ["SAIL 2017"]} | rohanrajpal/bert-base-codemixed-uncased-sentiment | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"text-classification",
"hi",
"en",
"codemix",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"hi",
"en"
] | TAGS
#transformers #pytorch #tf #jax #bert #text-classification #hi #en #codemix #autotrain_compatible #endpoints_compatible #region-us
|
# Model name
## Model description
I took a bert-base-multilingual-cased model from huggingface and finetuned it on SAIL 2017 dataset.
## Intended uses & limitations
#### How to use
#### Limitations and bias
Provide examples of latent issues and potential remediations.
## Training data
I trained on the SAIL... | [
"# Model name",
"## Model description\n\nI took a bert-base-multilingual-cased model from huggingface and finetuned it on SAIL 2017 dataset.",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\nProvide examples of latent issues and potential remediations.",
"## Training dat... | [
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"## Model description\n\nI took a bert-base-multilingual-cased model from huggingface and finetuned it on SAIL 2017 dataset.",
"## Intended uses & limi... |
text-classification | transformers |
# BERT codemixed base model for spanglish (cased)
This model was built using [lingualytics](https://github.com/lingualytics/py-lingualytics), an open-source library that supports code-mixed analytics.
## Model description
Input for the model: Any codemixed spanglish text
Output for the model: Sentiment. (0 - Negati... | {"language": ["es", "en"], "license": "apache-2.0", "tags": ["es", "en", "codemix"], "datasets": ["SAIL 2017"], "metrics": ["fscore", "accuracy", "precision", "recall"]} | rohanrajpal/bert-base-en-es-codemix-cased | null | [
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"text-classification",
"es",
"en",
"codemix",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"es",
"en"
] | TAGS
#transformers #pytorch #tf #jax #bert #text-classification #es #en #codemix #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| BERT codemixed base model for spanglish (cased)
===============================================
This model was built using lingualytics, an open-source library that supports code-mixed analytics.
Model description
-----------------
Input for the model: Any codemixed spanglish text
Output for the model: Sentiment.... | [
"#### How to use\n\n\nHere is how to use this model to get the features of a given text in *PyTorch*:\n\n\nand in *TensorFlow*:",
"#### Limitations and bias\n\n\nSince I dont know spanish, I cant verify the quality of annotations or the dataset itself. This is a very simple transfer learning approach and I'm open... | [
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"#### How to use\n\n\nHere is how to use this model to get the features of a given text in *PyTorch*:\n\n\nand in *TensorFlow*:",
"#### Limitations a... |
text-classification | transformers |
# BERT codemixed base model for Hinglish (cased)
This model was built using [lingualytics](https://github.com/lingualytics/py-lingualytics), an open-source library that supports code-mixed analytics.
## Model description
Input for the model: Any codemixed Hinglish text
Output for the model: Sentiment. (0 - Negative... | {"language": ["hi", "en"], "license": "apache-2.0", "tags": ["es", "en", "codemix"], "datasets": ["SAIL 2017"], "metrics": ["fscore", "accuracy", "precision", "recall"]} | rohanrajpal/bert-base-en-hi-codemix-cased | null | [
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"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"hi",
"en"
] | TAGS
#transformers #pytorch #tf #jax #bert #text-classification #es #en #codemix #hi #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| BERT codemixed base model for Hinglish (cased)
==============================================
This model was built using lingualytics, an open-source library that supports code-mixed analytics.
Model description
-----------------
Input for the model: Any codemixed Hinglish text
Output for the model: Sentiment. (0... | [
"#### How to use\n\n\nHere is how to use this model to get the features of a given text in *PyTorch*:\n\n\nand in *TensorFlow*:",
"#### Preprocessing\n\n\nFollowed standard preprocessing techniques:\n\n\n* removed digits\n* removed punctuation\n* removed stopwords\n* removed excess whitespace\nHere's the snippet\... | [
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"#### How to use\n\n\nHere is how to use this model to get the features of a given text in *PyTorch*:\n\n\nand in *TensorFlow*:",
"#### Preproces... |
text-classification | transformers |
# BERT codemixed base model for hinglish (cased)
## Model description
Input for the model: Any codemixed hinglish text
Output for the model: Sentiment. (0 - Negative, 1 - Neutral, 2 - Positive)
I took a bert-base-multilingual-cased model from Huggingface and finetuned it on [SAIL 2017](http://www.dasdipankar.com/SA... | {"language": ["hi", "en"], "license": "apache-2.0", "tags": ["hi", "en", "codemix"], "datasets": ["SAIL 2017"], "metrics": ["fscore", "accuracy"]} | rohanrajpal/bert-base-multilingual-codemixed-cased-sentiment | null | [
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"text-classification",
"hi",
"en",
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"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"hi",
"en"
] | TAGS
#transformers #pytorch #tf #jax #bert #text-classification #hi #en #codemix #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| BERT codemixed base model for hinglish (cased)
==============================================
Model description
-----------------
Input for the model: Any codemixed hinglish text
Output for the model: Sentiment. (0 - Negative, 1 - Neutral, 2 - Positive)
I took a bert-base-multilingual-cased model from Huggingface... | [
"#### How to use\n\n\nHere is how to use this model to get the features of a given text in *PyTorch*:\n\n\nand in *TensorFlow*:",
"#### Limitations and bias\n\n\nComing soon!\n\n\nTraining data\n-------------\n\n\nI trained on the SAIL 2017 dataset link on this pretrained model.\n\n\nTraining procedure\n---------... | [
"TAGS\n#transformers #pytorch #tf #jax #bert #text-classification #hi #en #codemix #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"#### How to use\n\n\nHere is how to use this model to get the features of a given text in *PyTorch*:\n\n\nand in *TensorFlow*:",
"#### Limitations a... |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 29906863
- CO2 Emissions (in grams): 3.8624397961432106
## Validation Metrics
- Loss: 0.2536192238330841
- Accuracy: 0.9084807809640024
- Precision: 0.9421172886519421
- Recall: 0.9435545385202135
- AUC: 0.9517288050454876
- F1: 0.94283... | {"language": "hi", "tags": "autonlp", "datasets": ["rohansingh/autonlp-data-Fake-news-detection-system"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 3.8624397961432106} | rohansingh/autonlp-Fake-news-detection-system-29906863 | null | [
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"dataset:rohansingh/autonlp-data-Fake-news-detection-system",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"hi"
] | TAGS
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|
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 29906863
- CO2 Emissions (in grams): 3.8624397961432106
## Validation Metrics
- Loss: 0.2536192238330841
- Accuracy: 0.9084807809640024
- Precision: 0.9421172886519421
- Recall: 0.9435545385202135
- AUC: 0.9517288050454876
- F1: 0.94283... | [
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"## Validation Metrics\n\n- Loss: 0.2536192238330841\n- Accuracy: 0.9084807809640024\n- Precision: 0.9421172886519421\n- Recall: 0.9435545385202135\n- AUC: 0.951728805045... | [
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"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 29906863\n- CO... |
text2text-generation | transformers |
## Paper
## [Domain Adapted Abstractive Summarization of Dialogue using Transfer Learning](https://dl.acm.org/doi/10.1145/3508546.3508640)
Authors: *Rohit Sroch*
## Abstract
Recently, the abstractive dialogue summarization task has been gaining a lot of attention from researchers. Also, unlike news articles and doc... | {"language": ["en"], "license": "apache-2.0", "tags": ["dialogue-summarization"], "datasets": ["icsi"], "model_index": [{"name": "hybrid_hbh_bart-base_icsi_sum", "results": [{"task": {"name": "Summarization", "type": "summarization"}}]}], "base_model": "facebook/bart-base"} | rohitsroch/hybrid_hbh_bart-base_icsi_sum | null | [
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"safetensors",
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"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
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|
## Paper
## Domain Adapted Abstractive Summarization of Dialogue using Transfer Learning
Authors: *Rohit Sroch*
## Abstract
Recently, the abstractive dialogue summarization task has been gaining a lot of attention from researchers. Also, unlike news articles and documents with well-structured text, dialogue differs... | [
"## Paper",
"## Domain Adapted Abstractive Summarization of Dialogue using Transfer Learning\nAuthors: *Rohit Sroch*",
"## Abstract\n\nRecently, the abstractive dialogue summarization task has been gaining a lot of attention from researchers. Also, unlike news articles and documents with well-structured text, d... | [
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"## Paper",
"## Domain Adapted Abstractive Summarization of Dialogue using Transfer Lear... |
text2text-generation | transformers |
## Paper
## [Domain Adapted Abstractive Summarization of Dialogue using Transfer Learning](https://dl.acm.org/doi/10.1145/3508546.3508640)
Authors: *Rohit Sroch*
## Abstract
Recently, the abstractive dialogue summarization task has been gaining a lot of attention from researchers. Also, unlike news articles and doc... | {"language": ["en"], "license": "apache-2.0", "tags": ["dialogue-summarization"], "datasets": ["ami"], "model_index": [{"name": "hybrid_hbh_t5-small_ami_sum", "results": [{"task": {"name": "Summarization", "type": "summarization"}}]}], "base_model": "t5-small"} | rohitsroch/hybrid_hbh_t5-small_ami_sum | null | [
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"safetensors",
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"dialogue-summarization",
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"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
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|
## Paper
## Domain Adapted Abstractive Summarization of Dialogue using Transfer Learning
Authors: *Rohit Sroch*
## Abstract
Recently, the abstractive dialogue summarization task has been gaining a lot of attention from researchers. Also, unlike news articles and documents with well-structured text, dialogue differs... | [
"## Paper",
"## Domain Adapted Abstractive Summarization of Dialogue using Transfer Learning\nAuthors: *Rohit Sroch*",
"## Abstract\n\nRecently, the abstractive dialogue summarization task has been gaining a lot of attention from researchers. Also, unlike news articles and documents with well-structured text, d... | [
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"## Paper",
"## Domain Adapted Abstractive Summarization of Dialogue using... |
text-generation | transformers |
# mine
| {"tags": ["conversational"]} | romuNoob/Mine | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# mine
| [
"# mine"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# mine"
] |
text-generation | transformers | # mine
| {"tags": ["conversational"]} | romuNoob/test | null | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # mine
| [
"# mine"
] | [
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# mine"
] |
sentence-similarity | sentence-transformers |
# ronanki/ml_mpnet_768_MNR
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 become... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | ronanki/ml_mpnet_768_MNR | 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
|
# ronanki/ml_mpnet_768_MNR
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:
Th... | [
"# ronanki/ml_mpnet_768_MNR\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 instal... | [
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"# ronanki/ml_mpnet_768_MNR\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks li... |
sentence-similarity | sentence-transformers |
# ronanki/ml_mpnet_768_MNR_10
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 bec... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | ronanki/ml_mpnet_768_MNR_10 | 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
|
# ronanki/ml_mpnet_768_MNR_10
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:
... | [
"# ronanki/ml_mpnet_768_MNR_10\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 ins... | [
"TAGS\n#sentence-transformers #pytorch #xlm-roberta #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# ronanki/ml_mpnet_768_MNR_10\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks... |
sentence-similarity | sentence-transformers |
# ronanki/ml_use_512_MNR_10
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 512 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 becom... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"} | ronanki/ml_use_512_MNR_10 | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #endpoints_compatible #region-us
|
# ronanki/ml_use_512_MNR_10
This is a sentence-transformers model: It maps sentences & paragraphs to a 512 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:
T... | [
"# ronanki/ml_use_512_MNR_10\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 512 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 insta... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #endpoints_compatible #region-us \n",
"# ronanki/ml_use_512_MNR_10\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering ... |
sentence-similarity | sentence-transformers |
# ronanki/xlmr_02-02-2022
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... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | ronanki/xlmr_02-02-2022 | 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
|
# ronanki/xlmr_02-02-2022
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:
The... | [
"# ronanki/xlmr_02-02-2022\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 install... | [
"TAGS\n#sentence-transformers #pytorch #xlm-roberta #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# ronanki/xlmr_02-02-2022\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks lik... |
sentence-similarity | sentence-transformers |
# ronanki/xlmr_17-01-2022_v3
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model beco... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | ronanki/xlmr_17-01-2022_v3 | 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
|
# ronanki/xlmr_17-01-2022_v3
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:
... | [
"# ronanki/xlmr_17-01-2022_v3\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers inst... | [
"TAGS\n#sentence-transformers #pytorch #xlm-roberta #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# ronanki/xlmr_17-01-2022_v3\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks ... |
null | null | aa | {} | rontom/Entitya | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| aa | [] | [
"TAGS\n#region-us \n"
] |
image-classification | transformers |
# dog-races-v2
Autogenerated Model created thannks to HuggingPics🤗🖼️. You can 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).
This Model is an improvement to my last model, where the ... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | roschmid/dog-races | 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
|
# dog-races-v2
Autogenerated Model created thannks to HuggingPics️. You can create your own image classifier for anything by running the demo on Google Colab.
This Model is an improvement to my last model, where the Chow Chow data included images of American pickles with the same name (contaminated data).
Current ... | [
"# dog-races-v2\n\n\nAutogenerated Model created thannks to HuggingPics️. You can create your own image classifier for anything by running the demo on Google Colab.\n\nThis Model is an improvement to my last model, where the Chow Chow data included images of American pickles with the same name (contaminated data). ... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# dog-races-v2\n\n\nAutogenerated Model created thannks to HuggingPics️. You can create your own image classifier for anything by running the demo on Google ... |
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. -->
# opus-mt-finetuned-en-es
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-es](https://huggingface.co/Helsinki-NLP/o... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["opus_books"], "metrics": ["bleu"], "model-index": [{"name": "opus-mt-finetuned-en-es", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "opus_books", "type": "opus_book... | rossanez/opus-mt-finetuned-en-es | null | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"generated_from_trainer",
"dataset:opus_books",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #marian #text2text-generation #generated_from_trainer #dataset-opus_books #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| opus-mt-finetuned-en-es
=======================
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-es on the opus\_books dataset.
It achieves the following results on the evaluation set:
* Loss: 1.9813
* Bleu: 21.5636
* Gen Len: 30.0992
Model description
-----------------
More information needed
In... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #tensorboard #marian #text2text-generation #generated_from_trainer #dataset-opus_books #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-base-finetuned-de-en
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wmt14 dataset.
... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt14"], "model-index": [{"name": "t5-base-finetuned-de-en", "results": []}]} | rossanez/t5-base-finetuned-de-en | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:wmt14",
"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 #dataset-wmt14 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-base-finetuned-de-en
=======================
This model is a fine-tuned version of t5-small on the wmt14 dataset.
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wmt14 #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* ... |
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-small-finetuned-de-en-256-epochs2
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wm... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt14"], "metrics": ["bleu"], "model-index": [{"name": "t5-small-finetuned-de-en-256-epochs2", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "wmt14", "type": "wmt14"... | rossanez/t5-small-finetuned-de-en-256-epochs2 | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:wmt14",
"license:apache-2.0",
"model-index",
"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 #dataset-wmt14 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-de-en-256-epochs2
====================================
This model is a fine-tuned version of t5-small on the wmt14 dataset.
It achieves the following results on the evaluation set:
* Loss: 2.1073
* Bleu: 7.8579
* Gen Len: 17.3896
Model description
-----------------
More information needed
I... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wmt14 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trai... |
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-small-finetuned-de-en-256-lr2e-4
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wmt... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt14"], "model-index": [{"name": "t5-small-finetuned-de-en-256-lr2e-4", "results": []}]} | rossanez/t5-small-finetuned-de-en-256-lr2e-4 | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:wmt14",
"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 #dataset-wmt14 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-de-en-256-lr2e-4
===================================
This model is a fine-tuned version of t5-small on the wmt14 dataset.
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation dat... | [
"### 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: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_pre... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wmt14 #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* ... |
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-small-finetuned-de-en-256-nofp16
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wmt... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt14"], "model-index": [{"name": "t5-small-finetuned-de-en-256-nofp16", "results": []}]} | rossanez/t5-small-finetuned-de-en-256-nofp16 | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:wmt14",
"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 #dataset-wmt14 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-de-en-256-nofp16
===================================
This model is a fine-tuned version of t5-small on the wmt14 dataset.
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation dat... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wmt14 #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* ... |
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-small-finetuned-de-en-256-wd-01
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wmt1... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt14"], "model-index": [{"name": "t5-small-finetuned-de-en-256-wd-01", "results": []}]} | rossanez/t5-small-finetuned-de-en-256-wd-01 | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:wmt14",
"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 #dataset-wmt14 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-de-en-256-wd-01
==================================
This model is a fine-tuned version of t5-small on the wmt14 dataset.
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wmt14 #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* ... |
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-small-finetuned-de-en-256
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wmt14 data... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt14"], "model-index": [{"name": "t5-small-finetuned-de-en-256", "results": []}]} | rossanez/t5-small-finetuned-de-en-256 | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:wmt14",
"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 #dataset-wmt14 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-de-en-256
============================
This model is a fine-tuned version of t5-small on the wmt14 dataset.
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wmt14 #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* ... |
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-small-finetuned-de-en-64
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wmt14 datas... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt14"], "model-index": [{"name": "t5-small-finetuned-de-en-64", "results": []}]} | rossanez/t5-small-finetuned-de-en-64 | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:wmt14",
"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 #dataset-wmt14 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-de-en-64
===========================
This model is a fine-tuned version of t5-small on the wmt14 dataset.
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
--------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wmt14 #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* ... |
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-small-finetuned-de-en-batch8
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wmt14 d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt14"], "metrics": ["bleu"], "model-index": [{"name": "t5-small-finetuned-de-en-batch8", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "wmt14", "type": "wmt14", "ar... | rossanez/t5-small-finetuned-de-en-batch8 | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:wmt14",
"license:apache-2.0",
"model-index",
"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 #dataset-wmt14 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-de-en-batch8
===============================
This model is a fine-tuned version of t5-small on the wmt14 dataset.
It achieves the following results on the evaluation set:
* Loss: 2.1282
* Bleu: 10.039
* Gen Len: 17.3839
Model description
-----------------
More information needed
Intended us... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5\n* mixed\\_preci... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during trai... |
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-small-finetuned-de-en-epochs5
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wmt14 ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt14"], "metrics": ["bleu"], "model-index": [{"name": "t5-small-finetuned-de-en-epochs5", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "wmt14", "type": "wmt14", "a... | rossanez/t5-small-finetuned-de-en-epochs5 | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:wmt14",
"license:apache-2.0",
"model-index",
"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 #dataset-wmt14 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-de-en-epochs5
================================
This model is a fine-tuned version of t5-small on the wmt14 dataset.
It achieves the following results on the evaluation set:
* Loss: 2.2040
* Bleu: 5.8913
* Gen Len: 17.5408
Model description
-----------------
More information needed
Intended ... | [
"### 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\n* mixed\\_prec... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during trai... |
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-small-finetuned-de-en-final
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wmt14 da... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt14"], "metrics": ["bleu"], "model-index": [{"name": "t5-small-finetuned-de-en-final", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "wmt14", "type": "wmt14", "arg... | rossanez/t5-small-finetuned-de-en-final | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
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"generated_from_trainer",
"dataset:wmt14",
"license:apache-2.0",
"model-index",
"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 #dataset-wmt14 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-de-en-final
==============================
This model is a fine-tuned version of t5-small on the wmt14 dataset.
It achieves the following results on the evaluation set:
* Loss: 2.3285
* Bleu: 9.8394
* Gen Len: 17.325
Model description
-----------------
More information needed
Intended uses ... | [
"### 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: 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\n* mixed\\_pre... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during trai... |
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-small-finetuned-de-en-lr2e-4
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wmt14 d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt14"], "metrics": ["bleu"], "model-index": [{"name": "t5-small-finetuned-de-en-lr2e-4", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "wmt14", "type": "wmt14", "ar... | rossanez/t5-small-finetuned-de-en-lr2e-4 | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
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"dataset:wmt14",
"license:apache-2.0",
"model-index",
"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 #dataset-wmt14 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-de-en-lr2e-4
===============================
This model is a fine-tuned version of t5-small on the wmt14 dataset.
It achieves the following results on the evaluation set:
* Loss: 2.0115
* Bleu: 9.12
* Gen Len: 17.4026
Model description
-----------------
More information needed
Intended uses... | [
"### 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: 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\n* mixed\\_pre... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during trai... |
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-small-finetuned-de-en-nofp16
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wmt14 d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt14"], "metrics": ["bleu"], "model-index": [{"name": "t5-small-finetuned-de-en-nofp16", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "wmt14", "type": "wmt14", "ar... | rossanez/t5-small-finetuned-de-en-nofp16 | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:wmt14",
"license:apache-2.0",
"model-index",
"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 #dataset-wmt14 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-de-en-nofp16
===============================
This model is a fine-tuned version of t5-small on the wmt14 dataset.
It achieves the following results on the evaluation set:
* Loss: 2.1460
* Bleu: 9.5801
* Gen Len: 17.333
Model description
-----------------
More information needed
Intended use... | [
"### 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: 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",
"### Train... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during trai... |
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-small-finetuned-de-en-wd-01
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wmt14 da... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt14"], "metrics": ["bleu"], "model-index": [{"name": "t5-small-finetuned-de-en-wd-01", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "wmt14", "type": "wmt14", "arg... | rossanez/t5-small-finetuned-de-en-wd-01 | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:wmt14",
"license:apache-2.0",
"model-index",
"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 #dataset-wmt14 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-de-en-wd-01
==============================
This model is a fine-tuned version of t5-small on the wmt14 dataset.
It achieves the following results on the evaluation set:
* Loss: 2.0482
* Bleu: 9.6027
* Gen Len: 17.3776
Model description
-----------------
More information needed
Intended uses... | [
"### 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: 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\n* mixed\\_pre... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during trai... |
text-generation | transformers |
#MIHO | {"tags": ["conversational", "gpt2"]} | rovai/AI | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#MIHO | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers | # CARRIE | {"tags": ["conversational"]} | rovai/CARRIE | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # CARRIE | [
"# CARRIE"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# CARRIE"
] |
text-generation | transformers | #chat_pytorch1 | {"tags": ["conversational"]} | rovai/Chat_pytorch1 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| #chat_pytorch1 | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers | # chatbot | {"tags": ["conversational"]} | rovai/chatbotmedium1 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # chatbot | [
"# chatbot"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# chatbot"
] |
text-generation | transformers | # chatbot2 | {"tags": ["conversational"]} | rovai/chatbotmedium2 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # chatbot2 | [
"# chatbot2"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# chatbot2"
] |
text-generation | transformers | # chatbotmedium3 | {"tags": ["conversational"]} | rovai/chatbotmedium3 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # chatbotmedium3 | [
"# chatbotmedium3"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# chatbotmedium3"
] |
text-generation | transformers | # chatbot4 | {"tags": ["conversational"]} | rovai/chatbotmedium4 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # chatbot4 | [
"# chatbot4"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# chatbot4"
] |
text-generation | null |
#chatbotone | {"tags": ["conversational", "gpt2"]} | rovai/chatbotone | null | [
"tensorboard",
"conversational",
"gpt2",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#tensorboard #conversational #gpt2 #region-us
|
#chatbotone | [] | [
"TAGS\n#tensorboard #conversational #gpt2 #region-us \n"
] |
text-generation | transformers |
#Eren Yeager DialoGPT Model | {"tags": ["conversational"]} | rpeng35/DialoGPT-small-erenyeager | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#Eren Yeager DialoGPT Model | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \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. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad", "results": []}]} | rpv/distilbert-base-uncased-finetuned-squad | null | [
"transformers",
"pytorch",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of distilbert-base-uncased on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### ... | [
"# distilbert-base-uncased-finetuned-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",
"#... | [
"TAGS\n#transformers #pytorch #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"# distilbert-base-uncased-finetuned-squad\n\nThis model is a fine-tuned version of distilbert-base-uncased on the squad dataset.",
"## Model description... |
text-generation | transformers |
# Shang-Chi DialoGPT Model | {"tags": ["conversational"]} | rrtong/DialoGPT-medium-shang-chi | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Shang-Chi DialoGPT Model | [
"# Shang-Chi DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Shang-Chi DialoGPT Model"
] |
text-generation | transformers |
# House Bot | {"tags": ["conversational"]} | rsd511/DialoGPT-small-house | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# House Bot | [
"# House Bot"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# House Bot"
] |
text-generation | transformers |
# DialoGPT-small model trained on dialogue from Rick and Morty
### [Chat to me on Chai!](https://chai.ml/chat/share/_bot_de374c84-9598-4848-996b-736d0cc02f6b)
Make your own Rick bot [here](https://colab.research.google.com/drive/1o5LxBspm-C28HQvXN-PRQavapDbm5WjG?usp=sharing) | {"tags": ["conversational"]} | rsedlr/RickBot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# DialoGPT-small model trained on dialogue from Rick and Morty
### Chat to me on Chai!
Make your own Rick bot here | [
"# DialoGPT-small model trained on dialogue from Rick and Morty",
"### Chat to me on Chai!\n\nMake your own Rick bot here"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# DialoGPT-small model trained on dialogue from Rick and Morty",
"### Chat to me on Chai!\n\nMake your own Rick bot here"
] |
text-generation | transformers | # RickBot built for [Chai](https://chai.ml/)
Make your own [here](https://colab.research.google.com/drive/1o5LxBspm-C28HQvXN-PRQavapDbm5WjG?usp=sharing)
| {"tags": ["conversational"]} | rsedlr/RickBotExample | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # RickBot built for Chai
Make your own here
| [
"# RickBot built for Chai\nMake your own here"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# RickBot built for Chai\nMake your own here"
] |
text-classification | transformers |
# ROTA
## Rapid Offense Text Autocoder
[](https://huggingface.co/rti-international/rota)
[](https://huggingface.co/spaces/rti-internat... | {"language": ["en"], "license": "apache-2.0", "widget": [{"text": "theft 3"}, {"text": "forgery"}, {"text": "unlawful possession short-barreled shotgun"}, {"text": "criminal trespass 2nd degree"}, {"text": "eluding a police vehicle"}, {"text": "upcs synthetic narcotic"}]} | rti-international/rota | null | [
"transformers",
"pytorch",
"jax",
"roberta",
"text-classification",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #jax #roberta #text-classification #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| ROTA
====
Rapid Offense Text Autocoder
----------------------------
![HuggingFace Models](URL
![HuggingFace Spaces](URL
![GitHub Model Release](URL
![DOI](URL
ROTA Application hosted on Hugging Face Spaces: URL
Criminal justice research often requires conversion of free-text offense descriptions into overall ch... | [
"### Data Preprocessing\n\n\nThe input text is standardized through a series of preprocessing steps. The text is first passed through a sequence of 500+ case-insensitive regular expressions that identify common misspellings and abbreviations and expand the text to a more full, correct English text. Some data-specif... | [
"TAGS\n#transformers #pytorch #jax #roberta #text-classification #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Data Preprocessing\n\n\nThe input text is standardized through a series of preprocessing steps. The text is first passed through a sequence of 500+ case-insensi... |
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-small-finetuned-en-to-ro-fp16_off-lr_2e-7-weight_decay_0.001
This model is a fine-tuned version of [t5-small](https://hugging... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "metrics": ["bleu"], "model-index": [{"name": "t5-small-finetuned-en-to-ro-fp16_off-lr_2e-7-weight_decay_0.001", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name... | rtoguchi/t5-small-finetuned-en-to-ro-fp16_off-lr_2e-7-weight_decay_0.001 | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:wmt16",
"license:apache-2.0",
"model-index",
"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 #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-en-to-ro-fp16\_off-lr\_2e-7-weight\_decay\_0.001
===================================================================
This model is a fine-tuned version of t5-small on the wmt16 dataset.
It achieves the following results on the evaluation set:
* Loss: 1.4943
* Bleu: 4.7258
* Gen Len: 18.7149
Mod... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-07\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trai... |
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-small-finetuned-en-to-ro-fp16_off
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wm... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "metrics": ["bleu"], "model-index": [{"name": "t5-small-finetuned-en-to-ro-fp16_off", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "wmt16", "type": "wmt16"... | rtoguchi/t5-small-finetuned-en-to-ro-fp16_off | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:wmt16",
"license:apache-2.0",
"model-index",
"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 #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-en-to-ro-fp16\_off
=====================================
This model is a fine-tuned version of t5-small on the wmt16 dataset.
It achieves the following results on the evaluation set:
* Loss: 1.4078
* Bleu: 7.3056
* Gen Len: 18.2556
Model description
-----------------
More information needed
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trai... |
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-small-finetuned-en-to-ro-weight_decay_0.001
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small)... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "metrics": ["bleu"], "model-index": [{"name": "t5-small-finetuned-en-to-ro-weight_decay_0.001", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "wmt16", "type... | rtoguchi/t5-small-finetuned-en-to-ro-weight_decay_0.001 | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:wmt16",
"license:apache-2.0",
"model-index",
"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 #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-en-to-ro-weight\_decay\_0.001
================================================
This model is a fine-tuned version of t5-small on the wmt16 dataset.
It achieves the following results on the evaluation set:
* Loss: 1.4509
* Bleu: 7.3524
* Gen Len: 18.2581
Model description
-----------------
Mor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trai... |
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