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text-classification | transformers | # Cross-Encoder for Quora Duplicate Questions Detection
This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class.
## Training Data
Given a question and paragraph, can the question be answered by the paragraph? Th... | {"license": "apache-2.0"} | cross-encoder/qnli-electra-base | null | [
"transformers",
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"license:apache-2.0",
"autotrain_compatible",
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"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.07461"
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#transformers #pytorch #electra #text-classification #arxiv-1804.07461 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| # Cross-Encoder for Quora Duplicate Questions Detection
This model was trained using SentenceTransformers Cross-Encoder class.
## Training Data
Given a question and paragraph, can the question be answered by the paragraph? The models have been trained on the GLUE QNLI dataset, which transformed the SQuAD dataset into ... | [
"# Cross-Encoder for Quora Duplicate Questions Detection\nThis model was trained using SentenceTransformers Cross-Encoder class.",
"## Training Data\nGiven a question and paragraph, can the question be answered by the paragraph? The models have been trained on the GLUE QNLI dataset, which transformed the SQuAD da... | [
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text-classification | transformers | # Cross-Encoder for Quora Duplicate Questions Detection
This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class.
## Training Data
This model was trained on the [Quora Duplicate Questions](https://www.quora.com/q... | {"license": "apache-2.0"} | cross-encoder/quora-distilroberta-base | null | [
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#transformers #pytorch #jax #roberta #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| # Cross-Encoder for Quora Duplicate Questions Detection
This model was trained using SentenceTransformers Cross-Encoder class.
## Training Data
This model was trained on the Quora Duplicate Questions dataset. The model will predict a score between 0 and 1 how likely the two given questions are duplicates.
Note: The m... | [
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text-classification | transformers | # Cross-Encoder for Quora Duplicate Questions Detection
This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class.
## Training Data
This model was trained on the [Quora Duplicate Questions](https://www.quora.com/q... | {"license": "apache-2.0"} | cross-encoder/quora-roberta-base | null | [
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#transformers #pytorch #jax #roberta #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| # Cross-Encoder for Quora Duplicate Questions Detection
This model was trained using SentenceTransformers Cross-Encoder class.
## Training Data
This model was trained on the Quora Duplicate Questions dataset. The model will predict a score between 0 and 1 how likely the two given questions are duplicates.
Note: The m... | [
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text-classification | transformers | # Cross-Encoder for Quora Duplicate Questions Detection
This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class.
## Training Data
This model was trained on the [Quora Duplicate Questions](https://www.quora.com/q... | {"license": "apache-2.0"} | cross-encoder/quora-roberta-large | null | [
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] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #roberta #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| # Cross-Encoder for Quora Duplicate Questions Detection
This model was trained using SentenceTransformers Cross-Encoder class.
## Training Data
This model was trained on the Quora Duplicate Questions dataset. The model will predict a score between 0 and 1 how likely the two given questions are duplicates.
Note: The m... | [
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text-classification | transformers | # Cross-Encoder for Quora Duplicate Questions Detection
This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class.
## Training Data
This model was trained on the [STS benchmark dataset](http://ixa2.si.ehu.eus/stsw... | {"license": "apache-2.0"} | cross-encoder/stsb-TinyBERT-L-4 | null | [
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"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
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] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bert #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| # Cross-Encoder for Quora Duplicate Questions Detection
This model was trained using SentenceTransformers Cross-Encoder class.
## Training Data
This model was trained on the STS benchmark dataset. The model will predict a score between 0 and 1 how for the semantic similarity of two sentences.
## Usage and Performan... | [
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text-classification | transformers | # Cross-Encoder for Quora Duplicate Questions Detection
This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class.
## Training Data
This model was trained on the [STS benchmark dataset](http://ixa2.si.ehu.eus/stsw... | {"license": "apache-2.0"} | cross-encoder/stsb-distilroberta-base | null | [
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"autotrain_compatible",
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"has_space",
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] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #roberta #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| # Cross-Encoder for Quora Duplicate Questions Detection
This model was trained using SentenceTransformers Cross-Encoder class.
## Training Data
This model was trained on the STS benchmark dataset. The model will predict a score between 0 and 1 how for the semantic similarity of two sentences.
## Usage and Performan... | [
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text-classification | transformers | # Cross-Encoder for Quora Duplicate Questions Detection
This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class.
## Training Data
This model was trained on the [STS benchmark dataset](http://ixa2.si.ehu.eus/stsw... | {"license": "apache-2.0"} | cross-encoder/stsb-roberta-base | null | [
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"text-classification",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #roberta #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| # Cross-Encoder for Quora Duplicate Questions Detection
This model was trained using SentenceTransformers Cross-Encoder class.
## Training Data
This model was trained on the STS benchmark dataset. The model will predict a score between 0 and 1 how for the semantic similarity of two sentences.
## Usage and Performan... | [
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text-classification | transformers | # Cross-Encoder for Quora Duplicate Questions Detection
This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class.
## Training Data
This model was trained on the [STS benchmark dataset](http://ixa2.si.ehu.eus/stsw... | {"license": "apache-2.0"} | cross-encoder/stsb-roberta-large | null | [
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"text-classification",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #roberta #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| # Cross-Encoder for Quora Duplicate Questions Detection
This model was trained using SentenceTransformers Cross-Encoder class.
## Training Data
This model was trained on the STS benchmark dataset. The model will predict a score between 0 and 1 how for the semantic similarity of two sentences.
## Usage and Performan... | [
"# Cross-Encoder for Quora Duplicate Questions Detection\nThis model was trained using SentenceTransformers Cross-Encoder class.",
"## Training Data\nThis model was trained on the STS benchmark dataset. The model will predict a score between 0 and 1 how for the semantic similarity of two sentences.",
"## Usage ... | [
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"## Training Data\nThis m... |
text-generation | transformers | ### Kw2Poem
| {"language": "vi", "tags": ["gpt"], "widget": [{"text": "<s> n\u00fai nh\u00e0 xe [SEP] "}]} | crylake/kw2poem-generation | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"gpt",
"vi",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"vi"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #gpt #vi #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| ### Kw2Poem
| [
"### Kw2Poem"
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"### Kw2Poem"
] |
text-generation | transformers |
#Rick Dialogpt model | {"tags": ["conversational"]} | crystalgate/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 | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
token-classification | spacy | NER Model for 'Ministerratsprotokolle'
| Feature | Description |
| --- | --- |
| **Name** | `de_MRP_NER` |
| **Version** | `0.0.0` |
| **spaCy** | `>=3.1.0,<3.2.0` |
| **Default Pipeline** | `tok2vec`, `ner` |
| **Components** | `tok2vec`, `ner` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources**... | {"language": ["de"], "license": "cc-by-4.0", "tags": ["spacy", "token-classification"]} | csae8092/de_MRP_NER | null | [
"spacy",
"token-classification",
"de",
"license:cc-by-4.0",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"de"
] | TAGS
#spacy #token-classification #de #license-cc-by-4.0 #model-index #region-us
| NER Model for 'Ministerratsprotokolle'
### Label Scheme
View label scheme (4 labels for 1 components)
### Accuracy
| [
"### Label Scheme\n\n\n\nView label scheme (4 labels for 1 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #de #license-cc-by-4.0 #model-index #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (4 labels for 1 components)",
"### Accuracy"
] |
token-classification | spacy | Regensburger Reichstag von 1576
| Feature | Description |
| --- | --- |
| **Name** | `de_RTA_NER` |
| **Version** | `0.0.0` |
| **spaCy** | `>=3.1.0,<3.2.0` |
| **Default Pipeline** | `tok2vec`, `ner` |
| **Components** | `tok2vec`, `ner` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | n/a ... | {"language": ["de"], "license": "cc-by-nc-4.0", "tags": ["spacy", "token-classification"]} | csae8092/de_RTA_NER | null | [
"spacy",
"token-classification",
"de",
"license:cc-by-nc-4.0",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"de"
] | TAGS
#spacy #token-classification #de #license-cc-by-nc-4.0 #model-index #region-us
| Regensburger Reichstag von 1576
### Label Scheme
View label scheme (4 labels for 1 components)
### Accuracy
| [
"### Label Scheme\n\n\n\nView label scheme (4 labels for 1 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #de #license-cc-by-nc-4.0 #model-index #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (4 labels for 1 components)",
"### Accuracy"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-bne-finetuned-amazon_reviews_multi
This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggin... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["amazon_reviews_multi"], "metrics": ["accuracy"], "model-index": [{"name": "roberta-base-bne-finetuned-amazon_reviews_multi", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "amazon_review... | csalamea/roberta-base-bne-finetuned-amazon_reviews_multi | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:amazon_reviews_multi",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| roberta-base-bne-finetuned-amazon\_reviews\_multi
=================================================
This model is a fine-tuned version of BSC-TeMU/roberta-base-bne on the amazon\_reviews\_multi dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2303
* Accuracy: 0.9325
Model description
--... | [
"### 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\... |
question-answering | transformers |
## BERT-base uncased model fine-tuned on SQuAD v1
This model was fine-tuned from the HuggingFace [BERT](https://www.aclweb.org/anthology/N19-1423/) base uncased checkpoint on [SQuAD1.1](https://rajpurkar.github.io/SQuAD-explorer).
This model is case-insensitive: it does not make a difference between english and Engli... | {"language": "en", "license": "mit", "tags": ["question-answering", "bert", "bert-base"], "datasets": ["squad"], "metrics": ["squad"], "widget": [{"text": "Which name is also used to describe the Amazon rainforest in English?", "context": "The Amazon rainforest (Portuguese: Floresta Amaz\u00f4nica or Amaz\u00f4nia; Spa... | csarron/bert-base-uncased-squad-v1 | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"bert",
"question-answering",
"bert-base",
"en",
"dataset:squad",
"license:mit",
"model-index",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #jax #safetensors #bert #question-answering #bert-base #en #dataset-squad #license-mit #model-index #endpoints_compatible #has_space #region-us
| BERT-base uncased model fine-tuned on SQuAD v1
----------------------------------------------
This model was fine-tuned from the HuggingFace BERT base uncased checkpoint on SQuAD1.1.
This model is case-insensitive: it does not make a difference between english and English.
Details
-------
Dataset: SQuAD1.1, Split... | [
"# samples: 90.6K\nDataset: SQuAD1.1, Split: eval, # samples: 11.1k",
"### Fine-tuning\n\n\n* Python: '3.7.5'\n* Machine specs:\n\n\n'CPU: Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz'\n\n\n'Memory: 32 GiB'\n\n\n'GPUs: 2 GeForce GTX 1070, each with 8GiB memory'\n\n\n'GPU driver: 418.87.01, CUDA: 10.1'\n* script:\n\n\... | [
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"### Fine-tuning\n\n\n* Python: '3.7.5'\n* Machine specs:\n\n\n... |
question-answering | transformers |
## MobileBERT fine-tuned on SQuAD v1
[MobileBERT](https://arxiv.org/abs/2004.02984) is a thin version of BERT_LARGE, while equipped with bottleneck structures and a carefully designed balance
between self-attentions and feed-forward networks.
This model was fine-tuned from the HuggingFace checkpoint `google/mobilebe... | {"language": "en", "license": "mit", "tags": ["question-answering", "mobilebert"], "datasets": ["squad"], "metrics": ["squad"], "widget": [{"text": "Which name is also used to describe the Amazon rainforest in English?", "context": "The Amazon rainforest (Portuguese: Floresta Amaz\u00f4nica or Amaz\u00f4nia; Spanish: S... | csarron/mobilebert-uncased-squad-v1 | null | [
"transformers",
"pytorch",
"safetensors",
"mobilebert",
"question-answering",
"en",
"dataset:squad",
"arxiv:2004.02984",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.02984"
] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #mobilebert #question-answering #en #dataset-squad #arxiv-2004.02984 #license-mit #endpoints_compatible #region-us
| MobileBERT fine-tuned on SQuAD v1
---------------------------------
MobileBERT is a thin version of BERT\_LARGE, while equipped with bottleneck structures and a carefully designed balance
between self-attentions and feed-forward networks.
This model was fine-tuned from the HuggingFace checkpoint 'google/mobilebert-... | [
"# samples: 90.6K\nDataset: SQuAD1.1, Split: eval, # samples: 11.1k",
"### Fine-tuning\n\n\n* Python: '3.7.5'\n* Machine specs:\n\n\n'CPU: Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz'\n\n\n'Memory: 32 GiB'\n\n\n'GPUs: 2 GeForce GTX 1070, each with 8GiB memory'\n\n\n'GPU driver: 418.87.01, CUDA: 10.1'\n* script:\n\n\... | [
"TAGS\n#transformers #pytorch #safetensors #mobilebert #question-answering #en #dataset-squad #arxiv-2004.02984 #license-mit #endpoints_compatible #region-us \n",
"# samples: 90.6K\nDataset: SQuAD1.1, Split: eval, # samples: 11.1k",
"### Fine-tuning\n\n\n* Python: '3.7.5'\n* Machine specs:\n\n\n'CPU: Intel(R) C... |
question-answering | transformers |
## MobileBERT fine-tuned on SQuAD v2
[MobileBERT](https://arxiv.org/abs/2004.02984) is a thin version of BERT_LARGE, while equipped with bottleneck structures and a carefully designed balance
between self-attentions and feed-forward networks.
This model was fine-tuned from the HuggingFace checkpoint `google/mobilebe... | {"language": "en", "license": "mit", "tags": ["question-answering", "mobilebert"], "datasets": ["squad_v2"], "metrics": ["squad_v2"], "widget": [{"text": "Which name is also used to describe the Amazon rainforest in English?", "context": "The Amazon rainforest (Portuguese: Floresta Amaz\u00f4nica or Amaz\u00f4nia; Span... | csarron/mobilebert-uncased-squad-v2 | null | [
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| MobileBERT fine-tuned on SQuAD v2
---------------------------------
MobileBERT is a thin version of BERT\_LARGE, while equipped with bottleneck structures and a carefully designed balance
between self-attentions and feed-forward networks.
This model was fine-tuned from the HuggingFace checkpoint 'google/mobilebert-... | [
"# samples: 130k\nDataset: SQuAD2.0, Split: eval, # samples: 12.3k",
"### Fine-tuning\n\n\n* Python: '3.7.5'\n* Machine specs:\n\n\n'CPU: Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz'\n\n\n'Memory: 32 GiB'\n\n\n'GPUs: 2 GeForce GTX 1070, each with 8GiB memory'\n\n\n'GPU driver: 418.87.01, CUDA: 10.1'\n* script:\n\n\n... | [
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"### Fine-tuning\n\n\n* Python: '3.7.5'\n* Machine specs:\n\n\n'CPU: In... |
question-answering | transformers |
## RoBERTa-base fine-tuned on SQuAD v1
This model was fine-tuned from the HuggingFace [RoBERTa](https://arxiv.org/abs/1907.11692) base checkpoint on [SQuAD1.1](https://rajpurkar.github.io/SQuAD-explorer).
This model is case-sensitive: it makes a difference between english and English.
## Details
| Dataset | Split ... | {"language": "en", "license": "mit", "tags": ["question-answering", "roberta", "roberta-base"], "datasets": ["squad"], "metrics": ["squad"], "widget": [{"text": "Which name is also used to describe the Amazon rainforest in English?", "context": "The Amazon rainforest (Portuguese: Floresta Amaz\u00f4nica or Amaz\u00f4ni... | csarron/roberta-base-squad-v1 | null | [
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] | [
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| RoBERTa-base fine-tuned on SQuAD v1
-----------------------------------
This model was fine-tuned from the HuggingFace RoBERTa base checkpoint on SQuAD1.1.
This model is case-sensitive: it makes a difference between english and English.
Details
-------
Dataset: SQuAD1.1, Split: train, # samples: 96.8K
Dataset: SQ... | [
"# samples: 96.8K\nDataset: SQuAD1.1, Split: eval, # samples: 11.8k",
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"### Fine-tuning\n\n\n* Python: '3.7.5'\n* Machine specs:\n\n\n... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion... | cscottp27/distilbert-base-uncased-finetuned-emotion | null | [
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| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2175
* Accuracy: 0.923
* F1: 0.9233
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: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
null | transformers |
# BanglaBERT
This repository contains the pretrained discriminator checkpoint of the model **BanglaBERT**. This is an [ELECTRA](https://openreview.net/pdf?id=r1xMH1BtvB) discriminator model pretrained with the Replaced Token Detection (RTD) objective. Finetuned models using this checkpoint achieve state-of-the-art re... | {"language": ["bn"], "licenses": ["cc-by-nc-sa-4.0"]} | csebuetnlp/banglabert | null | [
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"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"bn"
] | TAGS
#transformers #pytorch #electra #pretraining #bn #endpoints_compatible #has_space #region-us
| BanglaBERT
==========
This repository contains the pretrained discriminator checkpoint of the model BanglaBERT. This is an ELECTRA discriminator model pretrained with the Replaced Token Detection (RTD) objective. Finetuned models using this checkpoint achieve state-of-the-art results on many of the NLP tasks in benga... | [] | [
"TAGS\n#transformers #pytorch #electra #pretraining #bn #endpoints_compatible #has_space #region-us \n"
] |
summarization | transformers |
# mT5-m2o-english-CrossSum
This repository contains the many-to-one (m2o) mT5 checkpoint finetuned on all cross-lingual pairs of the [CrossSum](https://huggingface.co/datasets/csebuetnlp/CrossSum) dataset, where the target summary was in **english**, i.e. this model tries to **summarize text written in any language i... | {"language": ["am", "ar", "az", "bn", "my", "zh", "en", "fr", "gu", "ha", "hi", "ig", "id", "ja", "rn", "ko", "ky", "mr", "ne", "om", "ps", "fa", "pcm", "pt", "pa", "ru", "gd", "sr", "si", "so", "es", "sw", "ta", "te", "th", "ti", "tr", "uk", "ur", "uz", "vi", "cy", "yo"], "tags": ["summarization", "mT5"], "licenses": ... | csebuetnlp/mT5_m2o_english_crossSum | null | [
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# mT5-m2o-english-CrossSum
This repository contains the many-to-one (m2o) mT5 checkpoint finetuned on all cross-lingual pairs of the CrossSum dataset, where the target summary was in english, i.e. this model tries to summarize text written in any language in English. For finetuning details and scripts, see the paper ... | [
"# mT5-m2o-english-CrossSum\n\nThis repository contains the many-to-one (m2o) mT5 checkpoint finetuned on all cross-lingual pairs of the CrossSum dataset, where the target summary was in english, i.e. this model tries to summarize text written in any language in English. For finetuning details and scripts, see the ... | [
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summarization | transformers |
# mT5-multilingual-XLSum
This repository contains the mT5 checkpoint finetuned on the 45 languages of [XL-Sum](https://huggingface.co/datasets/csebuetnlp/xlsum) dataset. For finetuning details and scripts,
see the [paper](https://aclanthology.org/2021.findings-acl.413/) and the [official repository](https://github.co... | {"language": ["am", "ar", "az", "bn", "my", "zh", "en", "fr", "gu", "ha", "hi", "ig", "id", "ja", "rn", "ko", "ky", "mr", "ne", "om", "ps", "fa", "pcm", "pt", "pa", "ru", "gd", "sr", "si", "so", "es", "sw", "ta", "te", "th", "ti", "tr", "uk", "ur", "uz", "vi", "cy", "yo"], "tags": ["summarization", "mT5"], "datasets": ... | csebuetnlp/mT5_multilingual_XLSum | null | [
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======================
This repository contains the mT5 checkpoint finetuned on the 45 languages of XL-Sum dataset. For finetuning details and scripts,
see the paper and the official repository.
Using this model in 'transformers' (tested on 4.11.0.dev0)
---------------------------------------... | [] | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #summarization #mT5 #am #ar #az #bn #my #zh #en #fr #gu #ha #hi #ig #id #ja #rn #ko #ky #mr #ne #om #ps #fa #pcm #pt #pa #ru #gd #sr #si #so #es #sw #ta #te #th #ti #tr #uk #ur #uz #vi #cy #yo #dataset-csebuetnlp/xlsum #model-index #autotrain_compatible #endp... |
fill-mask | transformers |
# FrALBERT Base Cased
Pretrained model on French language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1909.11942) and first released in
[this repository](https://github.com/google-research/albert).
This model, unlike other ALBERT models, is cased: it does ... | {"language": "fr", "license": "apache-2.0", "datasets": ["wikipedia"]} | cservan/fralbert-base-cased | null | [
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"fr",
"dataset:wikipedia",
"arxiv:1909.11942",
"license:apache-2.0",
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"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1909.11942"
] | [
"fr"
] | TAGS
#transformers #pytorch #albert #fill-mask #fr #dataset-wikipedia #arxiv-1909.11942 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| FrALBERT Base Cased
===================
Pretrained model on French language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository.
This model, unlike other ALBERT models, is cased: it does make a difference between french and French.
Model descriptio... | [
"### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\n\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\n\nand in TensorFlow:\n\n\nTraining data\n-------------\n\n\nThe FrALBERT model was pretrained on 4go of French Wikipedia (excluding... | [
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"### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\n\n\nHere is how to use this model to get the... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-1b-bemba-fds
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/face... | {"license": "apache-2.0", "tags": ["generated_from_trainer", "bem", "robust-speech-event"], "model-index": [{"name": "wav2vec2-large-xls-r-1b-bemba-fds", "results": []}]} | csikasote/wav2vec2-large-xls-r-1b-bemba-fds | null | [
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"robust-speech-event",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #bem #robust-speech-event #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xls-r-1b-bemba-fds
=================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the BembaSpeech dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2898
* Wer: 0.3435
Model description
-----------------
More information needed
Int... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-bemba-fds
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/... | {"license": "apache-2.0", "tags": ["generated_from_trainer", "bem", "robust-speech-event"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-bemba-fds", "results": []}]} | csikasote/wav2vec2-large-xls-r-300m-bemba-fds | null | [
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"robust-speech-event",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
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#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #bem #robust-speech-event #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xls-r-300m-bemba-fds
===================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the BembaSpeech dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3594
* Wer: 0.3838
Model description
-----------------
More information needed... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Bemba
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Bemba language of Zambia using the [BembaSpeech](https://csikasote.github.io/BembaSpeech). When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The ... | {"language": "bem", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["BembaSpeech"], "metrics": ["wer"], "model-index": [{"name": "XLSR Wav2Vec2 Bemba by Claytone Sikasote", "results": [{"task": {"type": "automatic-speech-recognition", "name": "... | csikasote/wav2vec2-large-xlsr-bemba | null | [
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] | null | 2022-03-02T23:29:05+00:00 | [] | [
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] | TAGS
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|
# Wav2Vec2-Large-XLSR-53-Bemba
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Bemba language of Zambia using the BembaSpeech. When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
## Evaluation
The model can be ... | [
"# Wav2Vec2-Large-XLSR-53-Bemba\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Bemba language of Zambia using the BembaSpeech. When using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nT... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #bem #dataset-BembaSpeech #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Bemba\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Bemba language of Zambi... |
translation | transformers | ### marianmt-th-zh_cn
* source languages: th
* target languages: zh_cn
* dataset:
* model: transformer-align
* pre-processing: normalization + SentencePiece
* test set translations:
* test set scores:
## Training
Training scripts from [LalitaDeelert/NLP-ZH_TH-Project](https://github.com/LalitaDeelert/NLP-ZH_TH-Pro... | {"tags": ["translation", "torch==1.8.0"], "widget": [{"text": "Inference Unavailable"}]} | cstorm125/marianmt-th-zh_cn | null | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"translation",
"torch==1.8.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #marian #text2text-generation #translation #torch==1.8.0 #autotrain_compatible #endpoints_compatible #region-us
| ### marianmt-th-zh_cn
* source languages: th
* target languages: zh_cn
* dataset:
* model: transformer-align
* pre-processing: normalization + SentencePiece
* test set translations:
* test set scores:
## Training
Training scripts from LalitaDeelert/NLP-ZH_TH-Project. Experiments tracked at cstorm125/marianmt-th-zh... | [
"### marianmt-th-zh_cn\n* source languages: th\n* target languages: zh_cn\n* dataset: \n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* test set translations: \n* test set scores:",
"## Training\n\nTraining scripts from LalitaDeelert/NLP-ZH_TH-Project. Experiments tracked at cstorm1... | [
"TAGS\n#transformers #pytorch #marian #text2text-generation #translation #torch==1.8.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### marianmt-th-zh_cn\n* source languages: th\n* target languages: zh_cn\n* dataset: \n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* ... |
translation | transformers | ### marianmt-zh_cn-th
* source languages: zh_cn
* target languages: th
* dataset:
* model: transformer-align
* pre-processing: normalization + SentencePiece
* test set translations:
* test set scores:
## Training
Training scripts from [LalitaDeelert/NLP-ZH_TH-Project](https://github.com/LalitaDeelert/NLP-ZH_TH-Pro... | {"tags": ["translation", "torch==1.8.0"], "widget": [{"text": "Inference Unavailable"}]} | cstorm125/marianmt-zh_cn-th | null | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"translation",
"torch==1.8.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #marian #text2text-generation #translation #torch==1.8.0 #autotrain_compatible #endpoints_compatible #region-us
| ### marianmt-zh_cn-th
* source languages: zh_cn
* target languages: th
* dataset:
* model: transformer-align
* pre-processing: normalization + SentencePiece
* test set translations:
* test set scores:
## Training
Training scripts from LalitaDeelert/NLP-ZH_TH-Project. Experiments tracked at cstorm125/marianmt-zh_cn... | [
"### marianmt-zh_cn-th\n* source languages: zh_cn\n* target languages: th\n* dataset: \n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* test set translations: \n* test set scores:",
"## Training\n\nTraining scripts from LalitaDeelert/NLP-ZH_TH-Project. Experiments tracked at cstorm1... | [
"TAGS\n#transformers #pytorch #marian #text2text-generation #translation #torch==1.8.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### marianmt-zh_cn-th\n* source languages: zh_cn\n* target languages: th\n* dataset: \n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* ... |
question-answering | transformers | # wangchan-deberta_v1-base-wiki-20210520-news-spm-finetune-qa
Finetuning `airesearch/wangchan-deberta_v1-base-wiki-20210520-news-spm` with the training set of `iapp_wiki_qa_squad`, `thaiqa_squad`, and `nsc_qa` (removed examples which have cosine similarity with validation and test examples over 0.8; contexts of the la... | {"widget": [{"text": "\u0e2a\u0e27\u0e19\u0e01\u0e38\u0e2b\u0e25\u0e32\u0e1a\u0e40\u0e1b\u0e47\u0e19\u0e42\u0e23\u0e07\u0e40\u0e23\u0e35\u0e22\u0e19\u0e2d\u0e30\u0e44\u0e23", "context": "\u0e42\u0e23\u0e07\u0e40\u0e23\u0e35\u0e22\u0e19\u0e2a\u0e27\u0e19\u0e01\u0e38\u0e2b\u0e25\u0e32\u0e1a\u0e27\u0e34\u0e17\u0e22\u0e32\... | cstorm125/wangchan-deberta_v1-base-wiki-20210520-news-spm-finetune-qa | null | [
"transformers",
"pytorch",
"deberta",
"question-answering",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #deberta #question-answering #endpoints_compatible #region-us
| # wangchan-deberta_v1-base-wiki-20210520-news-spm-finetune-qa
Finetuning 'airesearch/wangchan-deberta_v1-base-wiki-20210520-news-spm' with the training set of 'iapp_wiki_qa_squad', 'thaiqa_squad', and 'nsc_qa' (removed examples which have cosine similarity with validation and test examples over 0.8; contexts of the la... | [
"# wangchan-deberta_v1-base-wiki-20210520-news-spm-finetune-qa\n\nFinetuning 'airesearch/wangchan-deberta_v1-base-wiki-20210520-news-spm' with the training set of 'iapp_wiki_qa_squad', 'thaiqa_squad', and 'nsc_qa' (removed examples which have cosine similarity with validation and test examples over 0.8; contexts of... | [
"TAGS\n#transformers #pytorch #deberta #question-answering #endpoints_compatible #region-us \n",
"# wangchan-deberta_v1-base-wiki-20210520-news-spm-finetune-qa\n\nFinetuning 'airesearch/wangchan-deberta_v1-base-wiki-20210520-news-spm' with the training set of 'iapp_wiki_qa_squad', 'thaiqa_squad', and 'nsc_qa' (re... |
question-answering | transformers | # airesearch/wangchanberta-base-att-spm-uncased
Finetuning `airesearch/wangchanberta-base-att-spm-uncased` with the training set of `iapp_wiki_qa_squad`, `thaiqa_squad`, and `nsc_qa` (removed examples which have cosine similarity with validation and test examples over 0.8; contexts of the latter two are trimmed to be... | {"widget": [{"text": "\u0e2a\u0e27\u0e19\u0e01\u0e38\u0e2b\u0e25\u0e32\u0e1a\u0e40\u0e1b\u0e47\u0e19\u0e42\u0e23\u0e07\u0e40\u0e23\u0e35\u0e22\u0e19\u0e2d\u0e30\u0e44\u0e23", "context": "\u0e42\u0e23\u0e07\u0e40\u0e23\u0e35\u0e22\u0e19\u0e2a\u0e27\u0e19\u0e01\u0e38\u0e2b\u0e25\u0e32\u0e1a\u0e27\u0e34\u0e17\u0e22\u0e32\... | cstorm125/wangchanberta-base-att-spm-uncased-finetune-qa | null | [
"transformers",
"pytorch",
"camembert",
"question-answering",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #camembert #question-answering #endpoints_compatible #region-us
| # airesearch/wangchanberta-base-att-spm-uncased
Finetuning 'airesearch/wangchanberta-base-att-spm-uncased' with the training set of 'iapp_wiki_qa_squad', 'thaiqa_squad', and 'nsc_qa' (removed examples which have cosine similarity with validation and test examples over 0.8; contexts of the latter two are trimmed to be... | [
"# airesearch/wangchanberta-base-att-spm-uncased\n\nFinetuning 'airesearch/wangchanberta-base-att-spm-uncased' with the training set of 'iapp_wiki_qa_squad', 'thaiqa_squad', and 'nsc_qa' (removed examples which have cosine similarity with validation and test examples over 0.8; contexts of the latter two are trimme... | [
"TAGS\n#transformers #pytorch #camembert #question-answering #endpoints_compatible #region-us \n",
"# airesearch/wangchanberta-base-att-spm-uncased\n\nFinetuning 'airesearch/wangchanberta-base-att-spm-uncased' with the training set of 'iapp_wiki_qa_squad', 'thaiqa_squad', and 'nsc_qa' (removed examples which hav... |
question-answering | transformers | # wangchanberta-base-wiki-20210520-news-spm-finetune-qa
Finetuning `airesearchth/wangchanberta-base-wiki-20210520-news-spm` with the training set of `iapp_wiki_qa_squad`, `thaiqa_squad`, and `nsc_qa` (removed examples which have cosine similarity with validation and test examples over 0.8; contexts of the latter two a... | {"widget": [{"text": "\u0e2a\u0e27\u0e19\u0e01\u0e38\u0e2b\u0e25\u0e32\u0e1a\u0e40\u0e1b\u0e47\u0e19\u0e42\u0e23\u0e07\u0e40\u0e23\u0e35\u0e22\u0e19\u0e2d\u0e30\u0e44\u0e23", "context": "\u0e42\u0e23\u0e07\u0e40\u0e23\u0e35\u0e22\u0e19\u0e2a\u0e27\u0e19\u0e01\u0e38\u0e2b\u0e25\u0e32\u0e1a\u0e27\u0e34\u0e17\u0e22\u0e32\... | cstorm125/wangchanberta-base-wiki-20210520-news-spm-finetune-qa | null | [
"transformers",
"pytorch",
"camembert",
"question-answering",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #camembert #question-answering #endpoints_compatible #region-us
| # wangchanberta-base-wiki-20210520-news-spm-finetune-qa
Finetuning 'airesearchth/wangchanberta-base-wiki-20210520-news-spm' with the training set of 'iapp_wiki_qa_squad', 'thaiqa_squad', and 'nsc_qa' (removed examples which have cosine similarity with validation and test examples over 0.8; contexts of the latter two a... | [
"# wangchanberta-base-wiki-20210520-news-spm-finetune-qa\n\nFinetuning 'airesearchth/wangchanberta-base-wiki-20210520-news-spm' with the training set of 'iapp_wiki_qa_squad', 'thaiqa_squad', and 'nsc_qa' (removed examples which have cosine similarity with validation and test examples over 0.8; contexts of the latte... | [
"TAGS\n#transformers #pytorch #camembert #question-answering #endpoints_compatible #region-us \n",
"# wangchanberta-base-wiki-20210520-news-spm-finetune-qa\n\nFinetuning 'airesearchth/wangchanberta-base-wiki-20210520-news-spm' with the training set of 'iapp_wiki_qa_squad', 'thaiqa_squad', and 'nsc_qa' (removed ex... |
question-answering | transformers | # wangchanberta-base-wiki-20210520-news-spm_span-mask-finetune-qa
Finetuning `airesearch/wangchanberta-base-wiki-20210520-news-spm_span-mask` with the training set of `iapp_wiki_qa_squad`, `thaiqa_squad`, and `nsc_qa` (removed examples which have cosine similarity with validation and test examples over 0.8; contexts o... | {"widget": [{"text": "\u0e2a\u0e27\u0e19\u0e01\u0e38\u0e2b\u0e25\u0e32\u0e1a\u0e40\u0e1b\u0e47\u0e19\u0e42\u0e23\u0e07\u0e40\u0e23\u0e35\u0e22\u0e19\u0e2d\u0e30\u0e44\u0e23", "context": "\u0e42\u0e23\u0e07\u0e40\u0e23\u0e35\u0e22\u0e19\u0e2a\u0e27\u0e19\u0e01\u0e38\u0e2b\u0e25\u0e32\u0e1a\u0e27\u0e34\u0e17\u0e22\u0e32\... | cstorm125/wangchanberta-base-wiki-20210520-news-spm_span-mask-finetune-qa | null | [
"transformers",
"pytorch",
"camembert",
"question-answering",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #camembert #question-answering #endpoints_compatible #region-us
| # wangchanberta-base-wiki-20210520-news-spm_span-mask-finetune-qa
Finetuning 'airesearch/wangchanberta-base-wiki-20210520-news-spm_span-mask' with the training set of 'iapp_wiki_qa_squad', 'thaiqa_squad', and 'nsc_qa' (removed examples which have cosine similarity with validation and test examples over 0.8; contexts o... | [
"# wangchanberta-base-wiki-20210520-news-spm_span-mask-finetune-qa\n\nFinetuning 'airesearch/wangchanberta-base-wiki-20210520-news-spm_span-mask' with the training set of 'iapp_wiki_qa_squad', 'thaiqa_squad', and 'nsc_qa' (removed examples which have cosine similarity with validation and test examples over 0.8; con... | [
"TAGS\n#transformers #pytorch #camembert #question-answering #endpoints_compatible #region-us \n",
"# wangchanberta-base-wiki-20210520-news-spm_span-mask-finetune-qa\n\nFinetuning 'airesearch/wangchanberta-base-wiki-20210520-news-spm_span-mask' with the training set of 'iapp_wiki_qa_squad', 'thaiqa_squad', and 'n... |
null | k2 |
# Introduction
This repo contains pre-trained model using
<https://github.com/k2-fsa/icefall/pull/219>.
It is trained on [AIShell](https://www.openslr.org/33/) dataset
using modified transducer from [optimized_transducer](https://github.com/csukuangfj/optimized_transducer).
Also, it uses [aidatatang_200zh](http://ww... | {"language": "en", "license": "apache-2.0", "tags": ["icefall", "k2", "transducer", "aishell", "ASR", "stateless transducer", "PyTorch"], "datasets": ["aishell", "aidatatang_200zh"], "metrics": ["WER"]} | csukuangfj/icefall-aishell-transducer-stateless-modified-2-2022-03-01 | null | [
"k2",
"icefall",
"transducer",
"aishell",
"ASR",
"stateless transducer",
"PyTorch",
"en",
"dataset:aishell",
"dataset:aidatatang_200zh",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#k2 #icefall #transducer #aishell #ASR #stateless transducer #PyTorch #en #dataset-aishell #dataset-aidatatang_200zh #license-apache-2.0 #region-us
| Introduction
============
This repo contains pre-trained model using
<URL
It is trained on AIShell dataset
using modified transducer from optimized\_transducer.
Also, it uses aidatatang\_200zh as extra training data.
How to clone this repo
----------------------
Catuion: You have to run 'git lfs pull'. Otherwis... | [] | [
"TAGS\n#k2 #icefall #transducer #aishell #ASR #stateless transducer #PyTorch #en #dataset-aishell #dataset-aidatatang_200zh #license-apache-2.0 #region-us \n"
] |
null | k2 |
# Introduction
This repo contains pre-trained model using
<https://github.com/k2-fsa/icefall/pull/219>.
It is trained on [AIShell](https://www.openslr.org/33/) dataset
using modified transducer from [optimized_transducer](https://github.com/csukuangfj/optimized_transducer).
## How to clone this repo
```
sudo apt-ge... | {"language": "en", "license": "apache-2.0", "tags": ["icefall", "k2", "transducer", "aishell", "ASR", "stateless transducer", "PyTorch"], "datasets": ["aishell"], "metrics": ["WER"]} | csukuangfj/icefall-aishell-transducer-stateless-modified-2022-03-01 | null | [
"k2",
"icefall",
"transducer",
"aishell",
"ASR",
"stateless transducer",
"PyTorch",
"en",
"dataset:aishell",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#k2 #icefall #transducer #aishell #ASR #stateless transducer #PyTorch #en #dataset-aishell #license-apache-2.0 #region-us
| Introduction
============
This repo contains pre-trained model using
<URL
It is trained on AIShell dataset
using modified transducer from optimized\_transducer.
How to clone this repo
----------------------
Catuion: You have to run 'git lfs pull'. Otherwise, you will be SAD later.
The model in this repo is tr... | [] | [
"TAGS\n#k2 #icefall #transducer #aishell #ASR #stateless transducer #PyTorch #en #dataset-aishell #license-apache-2.0 #region-us \n"
] |
null | null | # Introduction
This repo contains pre-trained model using
<https://github.com/k2-fsa/icefall/pull/213>.
It is trained on train-clean-100 subset of the LibriSpeech dataset.
Also, it uses the `S` subset from GigaSpeech as extra training data.
## How to clone this repo
```
sudo apt-get install git-lfs
git clone https:/... | {} | csukuangfj/icefall-asr-librispeech-100h-transducer-stateless-multi-datasets-bpe-500-2022-02-21 | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| Introduction
============
This repo contains pre-trained model using
<URL
It is trained on train-clean-100 subset of the LibriSpeech dataset.
Also, it uses the 'S' subset from GigaSpeech as extra training data.
How to clone this repo
----------------------
Catuion: You have to run 'git lfs pull'. Otherwise, you... | [] | [
"TAGS\n#region-us \n"
] |
null | null | # Introduction
## How to clone this repo
```
sudo apt-get install git-lfs
git clone https://huggingface.co/csukuangfj/icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09
cd icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09
git lfs pull
```
**Catuion**: You have to run `git lfs pull`. Otherwise, yo... | {} | csukuangfj/icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09 | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| Introduction
============
How to clone this repo
----------------------
Catuion: You have to run 'git lfs pull'. Otherwise, you will be SAD later.
---
Description
-----------
This repo provides pre-trained conformer CTC model for the librispeech dataset
using [icefall](URL).
The commands for training are:... | [] | [
"TAGS\n#region-us \n"
] |
null | null | # Introduction
## How to clone this repo
```
sudo apt-get install git-lfs
git clone https://huggingface.co/csukuangfj/icefall-asr-librispeech-transducer-bpe-500-2021-12-17
cd icefall-asr-librispeech-transducer-bpe-500-2021-12-17
git lfs pull
```
**Catuion**: You have to run `git lfs pull`. Otherwise, you will be SAD... | {} | csukuangfj/icefall-asr-librispeech-transducer-bpe-500-2021-12-17 | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| Introduction
============
How to clone this repo
----------------------
Catuion: You have to run 'git lfs pull'. Otherwise, you will be SAD later.
The model in this repo is trained using the commit 'cb04c8a7509425ab45fae888b0ca71bbbd23f0de'.
You can use
to download 'icefall'.
You can find the model informat... | [] | [
"TAGS\n#region-us \n"
] |
null | null | # Introduction
## How to clone this repo
```
sudo apt-get install git-lfs
git clone https://huggingface.co/csukuangfj/icefall-asr-librispeech-transducer-bpe-500-2021-12-23
cd icefall-asr-librispeech-transducer-bpe-500-2021-12-23
git lfs pull
```
**Catuion**: You have to run `git lfs pull`. Otherwise, you will be SAD... | {} | csukuangfj/icefall-asr-librispeech-transducer-bpe-500-2021-12-23 | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| Introduction
============
How to clone this repo
----------------------
Catuion: You have to run 'git lfs pull'. Otherwise, you will be SAD later.
The model in this repo is trained using the commit '5b6699a8354b70b23b252b371c612a35ed186ec2'.
You can use
to download 'icefall'.
You can find the model informat... | [] | [
"TAGS\n#region-us \n"
] |
null | null | # Introduction
## How to clone this repo
```
sudo apt-get install git-lfs
git clone https://huggingface.co/csukuangfj/icefall-asr-librispeech-transducer-stateless-bpe-500-2021-12-22
cd icefall-asr-librispeech-transducer-stateless-bpe-500-2021-12-22
git lfs pull
```
**Catuion**: You have to run `git lfs pull`. Other... | {} | csukuangfj/icefall-asr-librispeech-transducer-stateless-bpe-500-2021-12-22 | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| Introduction
============
How to clone this repo
----------------------
Catuion: You have to run 'git lfs pull'. Otherwise, you will be SAD later.
The model in this repo is trained using the commit 'fb6a57e9e01dd8aae2af2a6b4568daad8bc8ab32'.
You can use
to download 'icefall'.
You can find the model informat... | [] | [
"TAGS\n#region-us \n"
] |
null | null | # Introduction
## How to clone this repo
```
sudo apt-get install git-lfs
git clone https://huggingface.co/csukuangfj/icefall-asr-librispeech-transducer-stateless-bpe-500-2021-12-27
cd icefall-asr-librispeech-transducer-stateless-bpe-500-2021-12-27
git lfs pull
```
**Catuion**: You have to run `git lfs pull`. Other... | {} | csukuangfj/icefall-asr-librispeech-transducer-stateless-bpe-500-2021-12-27 | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| Introduction
============
How to clone this repo
----------------------
Catuion: You have to run 'git lfs pull'. Otherwise, you will be SAD later.
The model in this repo is trained using the commit '14c93add507982306f5a478cd144e0e32e0f970d'.
You can use
to download 'icefall'.
You can find the model informat... | [] | [
"TAGS\n#region-us \n"
] |
null | null | # Introduction
## How to clone this repo
```
sudo apt-get install git-lfs
git clone https://huggingface.co/csukuangfj/icefall-asr-librispeech-transducer-stateless-bpe-500-2022-01-10
cd icefall-asr-librispeech-transducer-stateless-bpe-500-2022-01-10
git lfs pull
```
**Catuion**: You have to run `git lfs pull`. Other... | {} | csukuangfj/icefall-asr-librispeech-transducer-stateless-bpe-500-2022-01-10 | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| Introduction
============
How to clone this repo
----------------------
Catuion: You have to run 'git lfs pull'. Otherwise, you will be SAD later.
The model in this repo is trained using the commit '4c1b3665ee6efb935f4dd93a80ff0e154b13efb6'.
You can use
to download 'icefall'.
You can find the model informat... | [] | [
"TAGS\n#region-us \n"
] |
null | null | # Introduction
## How to clone this repo
```
sudo apt-get install git-lfs
git clone https://huggingface.co/csukuangfj/icefall-asr-librispeech-transducer-stateless-bpe-500-2022-02-07
cd icefall-asr-librispeech-transducer-stateless-bpe-500-2022-02-07
git lfs pull
```
**Catuion**: You have to run `git lfs pull`. Other... | {} | csukuangfj/icefall-asr-librispeech-transducer-stateless-bpe-500-2022-02-07 | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| Introduction
============
How to clone this repo
----------------------
Catuion: You have to run 'git lfs pull'. Otherwise, you will be SAD later.
The model in this repo is trained using the commit 'a8150021e01d34ecbd6198fe03a57eacf47a16f2'.
You can use
to download 'icefall'.
You can find the model informat... | [] | [
"TAGS\n#region-us \n"
] |
null | k2 |
# Introduction
This repo contains pre-trained model using
<https://github.com/k2-fsa/icefall/pull/213>.
It is trained on full LibriSpeech dataset.
Also, it uses the `L` subset from [GigaSpeech](https://github.com/SpeechColab/GigaSpeech)
as extra training data.
## How to clone this repo
```
sudo apt-get install git... | {"language": "en", "license": "apache-2.0", "tags": ["icefall", "k2", "transducer", "librispeech", "ASR", "stateless transducer", "PyTorch", "RNN-T", "speech recognition"], "datasets": ["librispeech"], "metrics": ["WER"]} | csukuangfj/icefall-asr-librispeech-transducer-stateless-multi-datasets-bpe-500-2022-03-01 | null | [
"k2",
"icefall",
"transducer",
"librispeech",
"ASR",
"stateless transducer",
"PyTorch",
"RNN-T",
"speech recognition",
"en",
"dataset:librispeech",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#k2 #icefall #transducer #librispeech #ASR #stateless transducer #PyTorch #RNN-T #speech recognition #en #dataset-librispeech #license-apache-2.0 #region-us
| Introduction
============
This repo contains pre-trained model using
<URL
It is trained on full LibriSpeech dataset.
Also, it uses the 'L' subset from GigaSpeech
as extra training data.
How to clone this repo
----------------------
Catuion: You have to run 'git lfs pull'. Otherwise, you will be SAD later.
The... | [] | [
"TAGS\n#k2 #icefall #transducer #librispeech #ASR #stateless transducer #PyTorch #RNN-T #speech recognition #en #dataset-librispeech #license-apache-2.0 #region-us \n"
] |
null | null |
## Pre-trained TDNN models for the yesno dataset with icefall.
Refer to <https://github.com/k2-fsa/icefall/tree/master/egs/yesno/ASR>
for more information about this pre-trained model.
You can find usage instructions there.
## Sound files for testing the pre-trained model
The folder `test_waves` contains test sou... | {} | csukuangfj/icefall_asr_yesno_tdnn | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
|
## Pre-trained TDNN models for the yesno dataset with icefall.
Refer to <URL
for more information about this pre-trained model.
You can find usage instructions there.
## Sound files for testing the pre-trained model
The folder 'test_waves' contains test sound files. They
are downloaded from <URL
There are 60 fil... | [
"## Pre-trained TDNN models for the yesno dataset with icefall.\n\nRefer to <URL\nfor more information about this pre-trained model.\n\nYou can find usage instructions there.",
"## Sound files for testing the pre-trained model\n\nThe folder 'test_waves' contains test sound files. They\nare downloaded from <URL\n\... | [
"TAGS\n#region-us \n",
"## Pre-trained TDNN models for the yesno dataset with icefall.\n\nRefer to <URL\nfor more information about this pre-trained model.\n\nYou can find usage instructions there.",
"## Sound files for testing the pre-trained model\n\nThe folder 'test_waves' contains test sound files. They\nar... |
null | null | See
https://colab.research.google.com/drive/14MozS-9jWD3XQ0o-dZ-meqnblgHs70P2?usp=sharing
| {} | csukuangfj/test-data-for-optimized-transducer | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| See
URL
| [] | [
"TAGS\n#region-us \n"
] |
null | null | # Introduction
This repo contains the benchmark results for <https://github.com/csukuangfj/transducer-loss-benchmarking>
## Usage
First, install `git-lfs`.
Second, use the following command to clone this repo:
```bash
git lfs install
git clone https://huggingface.co/csukuangfj/transducer-loss-benchmarking
```
**C... | {} | csukuangfj/transducer-loss-benchmarking | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| # Introduction
This repo contains the benchmark results for <URL
## Usage
First, install 'git-lfs'.
Second, use the following command to clone this repo:
Caution: You have to run 'git lfs install' first. Otherwise, you will be SAD later.
Third,
Fourth, open your browser and go to
- <http://localhost:6006/#py... | [
"# Introduction\n\nThis repo contains the benchmark results for <URL",
"## Usage\n\nFirst, install 'git-lfs'.\n\nSecond, use the following command to clone this repo:\n\n\n\nCaution: You have to run 'git lfs install' first. Otherwise, you will be SAD later.\n\nThird,\n\n\nFourth, open your browser and go to\n\n- ... | [
"TAGS\n#region-us \n",
"# Introduction\n\nThis repo contains the benchmark results for <URL",
"## Usage\n\nFirst, install 'git-lfs'.\n\nSecond, use the following command to clone this repo:\n\n\n\nCaution: You have to run 'git lfs install' first. Otherwise, you will be SAD later.\n\nThird,\n\n\nFourth, open you... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Cantonese
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Cantonese using the [Common Voice](https://huggingface.co/datasets/common_voice).
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model... | {"language": ["yue"], "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["cer"], "language_bcp47": ["zh-HK"], "model-index": [{"name": "wav2vec2-large-xlsr-cantonese", "results": [{"task": {"type": "automatic-speech-re... | ctl/wav2vec2-large-xlsr-cantonese | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"yue",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"yue"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #yue #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us
|
# Wav2Vec2-Large-XLSR-53-Cantonese
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Cantonese using the Common Voice.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
## Evaluation
The model can be evaluated... | [
"# Wav2Vec2-Large-XLSR-53-Cantonese\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Cantonese using the Common Voice.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model ... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #yue #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Cantonese\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Cantone... |
text-generation | transformers |
# My Awesome Model
| {"tags": ["conversational"]} | cumtowndiscord/DialoGPT-small-joshua | 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
|
# My Awesome Model
| [
"# My Awesome Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# My Awesome Model"
] |
token-classification | transformers | Fine tuning LayoutLMv2 model on Vietnamese bill dataset
```python
from transformers import LayoutLMv2ForTokenClassification
model = LayoutLMv2ForTokenClassification.from_pretrained('cuongngm/layoutlm-bill', num_labels=len(labels))
```
labels = ['price',
'storename',
'total_cost',
'phone',
'address',
'unitprice',... | {} | cuongngm/layoutlm-bill | null | [
"transformers",
"pytorch",
"layoutlmv2",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #layoutlmv2 #token-classification #autotrain_compatible #endpoints_compatible #region-us
| Fine tuning LayoutLMv2 model on Vietnamese bill dataset
labels = ['price',
'storename',
'total_cost',
'phone',
'address',
'unitprice',
'item',
'subitem',
'other',
'time',
'unit',
'total refunds',
'total_qty',
'seller',
'total_received'] | [] | [
"TAGS\n#transformers #pytorch #layoutlmv2 #token-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-generation | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | cutiebunny639/DialoGPT-small-harry | 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 DialoGPT Model | [
"# Harry Potter DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Harry Potter DialoGPT Model"
] |
text-classification | transformers |
**Disclaimer**: *This model is still under testing and may change in the future, we will try to keep backwards compatibility. For any questions reach us at info@cvcio.org*
# MediaWatch News Topics (Greek)
Fine-tuned model for multi-label text-classification (SequenceClassification), based on [roberta-el-news](https:... | {"language": "el", "license": "gpl-3.0", "tags": ["roberta", "Greek", "news", "transformers", "text-classification"], "pipeline_tag": "text-classification", "widget": [{"text": "\u03a0\u03b1\u03c1\u2019 \u03bf\u03bb\u03af\u03b3\u03bf\u03bd \u00ab\u03b8\u03b5\u03c1\u03bc\u03cc\u00bb \u03b5\u03c0\u03b5\u03b9\u03c3\u03cc\... | cvcio/mediawatch-el-topics | null | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"text-classification",
"Greek",
"news",
"el",
"doi:10.57967/hf/0711",
"license:gpl-3.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"el"
] | TAGS
#transformers #pytorch #safetensors #roberta #text-classification #Greek #news #el #doi-10.57967/hf/0711 #license-gpl-3.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| Disclaimer: *This model is still under testing and may change in the future, we will try to keep backwards compatibility. For any questions reach us at info@URL*
MediaWatch News Topics (Greek)
==============================
Fine-tuned model for multi-label text-classification (SequenceClassification), based on robe... | [
"### Framework versions\n\n\n* Transformers 4.13.0\n* Pytorch 1.9.0+cu111\n* Datasets 1.16.1\n* Tokenizers 0.10.3\n\n\nAuthors\n-------\n\n\nDimitris Papaevagelou - @andefined\n\n\nAbout Us\n--------\n\n\nCivic Information Office is a Non Profit Organization based in Athens, Greece focusing on creating technology a... | [
"TAGS\n#transformers #pytorch #safetensors #roberta #text-classification #Greek #news #el #doi-10.57967/hf/0711 #license-gpl-3.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Framework versions\n\n\n* Transformers 4.13.0\n* Pytorch 1.9.0+cu111\n* Datasets 1.16.1\n* Tokenizers 0.10.... |
fill-mask | transformers |
# RoBERTa Greek base model
Pretrained model on Greek language with the Masked Language Modeling (MLM) objective using [Hugging Face's](https://huggingface.co/) [Transformers](https://github.com/huggingface/transformers) library. This model is *NOT* case-sensitive and all Greek diacritics retained.
### How to use
Yo... | {"language": "el", "license": "gpl-3.0", "tags": ["generated_from_trainer", "roberta", "Greek", "news", "transformers"], "widget": [{"text": "\u0397 \u03ba\u03c5\u03b2\u03ad\u03c1\u03bd\u03b7\u03c3\u03b7 \u03bc\u03bf\u03c5\u03b4\u03b9\u03b1\u03c3\u03bc\u03ad\u03bd\u03b7 \u03b1\u03c0\u03cc \u03c4\u03b7 <mask> \u03c4\u03... | cvcio/roberta-el-news | null | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"fill-mask",
"generated_from_trainer",
"Greek",
"news",
"el",
"doi:10.57967/hf/0712",
"license:gpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"el"
] | TAGS
#transformers #pytorch #safetensors #roberta #fill-mask #generated_from_trainer #Greek #news #el #doi-10.57967/hf/0712 #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us
| RoBERTa Greek base model
========================
Pretrained model on Greek language with the Masked Language Modeling (MLM) objective using Hugging Face's Transformers library. This model is *NOT* case-sensitive and all Greek diacritics retained.
### How to use
You can use this model directly with a pipeline for... | [
"### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\n\n\nTraining data\n-------------\n\n\nThe model was pretrained on 8 millon unique news articles (~ approx 160M sentences, 33GB of text), collected with MediaWatch, from October 2016 upto December 2021.\n\n\nPreproces... | [
"TAGS\n#transformers #pytorch #safetensors #roberta #fill-mask #generated_from_trainer #Greek #news #el #doi-10.57967/hf/0712 #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\n\n\nTrainin... |
fill-mask | transformers |
# Greek RoBERTa Uncased (v1)
Pretrained model on Greek language using a masked language modeling (MLM) objective using [Hugging Face's](https://huggingface.co/) [Transformers](https://github.com/huggingface/transformers) library. This model is case-sensitive and has no Greek diacritics (uncased, no-accents).
### Tra... | {"language": "el", "tags": ["roberta", "twitter", "Greek"], "widget": [{"text": "<mask>: \u03bc\u03b5\u03b3\u03b1\u03bb\u03b7 \u03c5\u03c0\u03bf\u03c7\u03c9\u03c1\u03b7\u03c3\u03b7 \u03c4\u03bf\u03c5 \u03b9\u03b9\u03ba\u03bf\u03c5 \u03c6\u03bf\u03c1\u03c4\u03b9\u03bf\u03c5 \u03c3\u03b5 \u03b1\u03c4\u03c4\u03b9\u03ba\u0... | cvcio/roberta-el-uncased-twitter-v1 | null | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"fill-mask",
"twitter",
"Greek",
"el",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"el"
] | TAGS
#transformers #pytorch #safetensors #roberta #fill-mask #twitter #Greek #el #autotrain_compatible #endpoints_compatible #region-us
|
# Greek RoBERTa Uncased (v1)
Pretrained model on Greek language using a masked language modeling (MLM) objective using Hugging Face's Transformers library. This model is case-sensitive and has no Greek diacritics (uncased, no-accents).
### Training data
This model was pretrained on almost 18M unique tweets, all Gre... | [
"# Greek RoBERTa Uncased (v1)\n\nPretrained model on Greek language using a masked language modeling (MLM) objective using Hugging Face's Transformers library. This model is case-sensitive and has no Greek diacritics (uncased, no-accents).",
"### Training data\n\nThis model was pretrained on almost 18M unique twe... | [
"TAGS\n#transformers #pytorch #safetensors #roberta #fill-mask #twitter #Greek #el #autotrain_compatible #endpoints_compatible #region-us \n",
"# Greek RoBERTa Uncased (v1)\n\nPretrained model on Greek language using a masked language modeling (MLM) objective using Hugging Face's Transformers library. This model ... |
token-classification | transformers | ## Hello World | {} | cwtpc/wangchanberta-ner-8989 | null | [
"transformers",
"pytorch",
"camembert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #camembert #token-classification #autotrain_compatible #endpoints_compatible #region-us
| ## Hello World | [
"## Hello World"
] | [
"TAGS\n#transformers #pytorch #camembert #token-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"## Hello World"
] |
null | transformers | ## Cyclone Chinese NER
This model provides simplified Chinese NER model based on pretrained model BERT (specifically BERT + CRF)
Currently, we only support 8 general type of entities ("address", "company", "government", "name", "organization", "position", "scene", "time")
### Usage
from transformers import ... | {} | cyclone/cyclone-ner | null | [
"transformers",
"pytorch",
"bert",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #endpoints_compatible #region-us
| ## Cyclone Chinese NER
This model provides simplified Chinese NER model based on pretrained model BERT (specifically BERT + CRF)
Currently, we only support 8 general type of entities ("address", "company", "government", "name", "organization", "position", "scene", "time")
### Usage
from transformers import ... | [
"## Cyclone Chinese NER\r\n\r\nThis model provides simplified Chinese NER model based on pretrained model BERT (specifically BERT + CRF)\r\nCurrently, we only support 8 general type of entities (\"address\", \"company\", \"government\", \"name\", \"organization\", \"position\", \"scene\", \"time\")",
"### Usage\r... | [
"TAGS\n#transformers #pytorch #bert #endpoints_compatible #region-us \n",
"## Cyclone Chinese NER\r\n\r\nThis model provides simplified Chinese NER model based on pretrained model BERT (specifically BERT + CRF)\r\nCurrently, we only support 8 general type of entities (\"address\", \"company\", \"government\", \"n... |
feature-extraction | transformers | ## Cyclone SIMCSE RoBERTa WWM Ext Chinese
This model provides simplified Chinese sentence embeddings encoding based on [Simple Contrastive Learning](https://arxiv.org/abs/2104.08821).
The pretrained model(Chinese RoBERTa WWM Ext) is used for token encoding.
### Usage
Please use [SentenceTransformer](https://git... | {} | cyclone/simcse-chinese-roberta-wwm-ext | null | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"arxiv:2104.08821",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2104.08821"
] | [] | TAGS
#transformers #pytorch #bert #feature-extraction #arxiv-2104.08821 #endpoints_compatible #has_space #region-us
| ## Cyclone SIMCSE RoBERTa WWM Ext Chinese
This model provides simplified Chinese sentence embeddings encoding based on Simple Contrastive Learning.
The pretrained model(Chinese RoBERTa WWM Ext) is used for token encoding.
### Usage
Please use SentenceTransformer to load the model.
from sentence_transform... | [
"## Cyclone SIMCSE RoBERTa WWM Ext Chinese\r\n\r\nThis model provides simplified Chinese sentence embeddings encoding based on Simple Contrastive Learning.\r\nThe pretrained model(Chinese RoBERTa WWM Ext) is used for token encoding.",
"### Usage\r\nPlease use SentenceTransformer to load the model.\r\n\r\n from... | [
"TAGS\n#transformers #pytorch #bert #feature-extraction #arxiv-2104.08821 #endpoints_compatible #has_space #region-us \n",
"## Cyclone SIMCSE RoBERTa WWM Ext Chinese\r\n\r\nThis model provides simplified Chinese sentence embeddings encoding based on Simple Contrastive Learning.\r\nThe pretrained model(Chinese RoB... |
fill-mask | transformers | # About
This is a sample repo. | {} | cylee/tutorial | null | [
"transformers",
"tf",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #tf #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| # About
This is a sample repo. | [
"# About\n\nThis is a sample repo."
] | [
"TAGS\n#transformers #tf #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"# About\n\nThis is a sample repo."
] |
fill-mask | transformers | # Description:
This is a smaller per-trained model on Sinhalese Language using Masked Language Modeling(MLM). And the model is trained on Oscar Sinhala dataset.
# How to Use:
The model can be used directly with a pipeline for masked language modeling:
```python
>>> from transformers import AutoTokenizer, AutoModelFor... | {} | d42kw01f/Sinhala-RoBERTa | 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
| # Description:
This is a smaller per-trained model on Sinhalese Language using Masked Language Modeling(MLM). And the model is trained on Oscar Sinhala dataset.
# How to Use:
The model can be used directly with a pipeline for masked language modeling:
| [
"# Description:\n\nThis is a smaller per-trained model on Sinhalese Language using Masked Language Modeling(MLM). And the model is trained on Oscar Sinhala dataset.",
"# How to Use:\nThe model can be used directly with a pipeline for masked language modeling:"
] | [
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"# Description:\n\nThis is a smaller per-trained model on Sinhalese Language using Masked Language Modeling(MLM). And the model is trained on Oscar Sinhala dataset.",
"# How to Use:\nThe model can be us... |
fill-mask | transformers | # Description:
This is a smaller per-trained model on Tamil Language using Masked Language Modeling(MLM). And the model is trained on Oscar Tamil dataset.
# How to Use:
The model can be used directly with a pipeline for masked language modeling:
```python
>>> from transformers import AutoTokenizer, AutoModelForMasked... | {} | d42kw01f/Tamil-RoBERTa | 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
| # Description:
This is a smaller per-trained model on Tamil Language using Masked Language Modeling(MLM). And the model is trained on Oscar Tamil dataset.
# How to Use:
The model can be used directly with a pipeline for masked language modeling:
| [
"# Description:\n\nThis is a smaller per-trained model on Tamil Language using Masked Language Modeling(MLM). And the model is trained on Oscar Tamil dataset.",
"# How to Use:\nThe model can be used directly with a pipeline for masked language modeling:"
] | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"# Description:\n\nThis is a smaller per-trained model on Tamil Language using Masked Language Modeling(MLM). And the model is trained on Oscar Tamil dataset.",
"# How to Use:\nThe model can be used dir... |
text-classification | transformers |
## About the Model
An English sequence classification model, trained on MBAD Dataset to detect bias and fairness in sentences (news articles). This model was built on top of distilbert-base-uncased model and trained for 30 epochs with a batch size of 16, a learning rate of 5e-5, and a maximum sequence length of 512.
... | {"language": ["en"], "tags": ["Text Classification"], "co2_eq_emissions": 0.319355, "widget": [{"text": "Nevertheless, Trump and other Republicans have tarred the protests as havens for terrorists intent on destroying property.", "example_title": "Biased example 1"}, {"text": "Billie Eilish issues apology for mouthing ... | d4data/bias-detection-model | null | [
"transformers",
"tf",
"distilbert",
"text-classification",
"Text Classification",
"en",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #tf #distilbert #text-classification #Text Classification #en #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us
| About the Model
---------------
An English sequence classification model, trained on MBAD Dataset to detect bias and fairness in sentences (news articles). This model was built on top of distilbert-base-uncased model and trained for 30 epochs with a batch size of 16, a learning rate of 5e-5, and a maximum sequence le... | [] | [
"TAGS\n#transformers #tf #distilbert #text-classification #Text Classification #en #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
token-classification | spacy | ## About the Model
This model is trained on MBAD Dataset to recognize the biased word/phrases in a sentence. This model was built on top of roberta-base offered by Spacy transformers.
This model is in association with https://huggingface.co/d4data/bias-detection-model
| Feature | Description |
| --- | --- |
| **Name... | {"language": ["en"], "tags": ["spacy", "token-classification"], "widget": [{"text": "Billie Eilish issues apology for mouthing an anti-Asian derogatory term in a resurfaced video.", "example_title": "Biased example 1"}, {"text": "Christians should make clear that the perpetuation of objectionable vaccines and the lack ... | d4data/en_pipeline | null | [
"spacy",
"token-classification",
"en",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#spacy #token-classification #en #model-index #region-us
| About the Model
---------------
This model is trained on MBAD Dataset to recognize the biased word/phrases in a sentence. This model was built on top of roberta-base offered by Spacy transformers.
This model is in association with URL
Author
------
This model is part of the Research topic "Bias and Fairness in... | [] | [
"TAGS\n#spacy #token-classification #en #model-index #region-us \n"
] |
text-classification | transformers |
## About the Model
An Environmental due diligence classification model, trained on customized environmental Dataset to detect contamination and remediation activities (both prevailing as well as planned) as a part of site assessment process. This model can identify the source of contamination, the extent of contamina... | {"language": ["en"], "tags": ["Text Classification"], "co2_eq_emissions": 0.1069, "widget": [{"text": "At the every month post-injection monitoring event, TCE, carbon tetrachloride, and chloroform concentrations were above CBSGs in three of the wells", "example_title": "Remediation Standards"}, {"text": "TRPH exceedanc... | d4data/environmental-due-diligence-model | null | [
"transformers",
"tf",
"distilbert",
"text-classification",
"Text Classification",
"en",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #tf #distilbert #text-classification #Text Classification #en #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us
|
## About the Model
An Environmental due diligence classification model, trained on customized environmental Dataset to detect contamination and remediation activities (both prevailing as well as planned) as a part of site assessment process. This model can identify the source of contamination, the extent of contamina... | [
"## About the Model\nAn Environmental due diligence classification model, trained on customized environmental Dataset to detect contamination and remediation activities (both prevailing as well as planned) as a part of site assessment process. This model can identify the source of contamination, the extent of cont... | [
"TAGS\n#transformers #tf #distilbert #text-classification #Text Classification #en #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## About the Model\nAn Environmental due diligence classification model, trained on customized environmental Dataset to detect contamination ... |
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"], "base_model": "xlm-roberta-base", "model-index": [{"name": "xlm-roberta-base-finetuned-marc-en", "results": []}]} | d4niel92/xlm-roberta-base-finetuned-marc-en | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"dataset:amazon_reviews_multi",
"base_model:xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #base_model-xlm-roberta-base #license-mit #autotrain_compatible #endpoints_compatible #region-us
| 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: 0.8976
* Mae: 0.4268
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... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #base_model-xlm-roberta-base #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during... |
text-generation | transformers |
# Harry | {"tags": ["conversational"]} | d4rk/harry | 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 | [
"# Harry"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Harry"
] |
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-zh-en-ep1-renri-zh-to-en
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-zh-en](https://huggingface.co/Helsi... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["bleu"], "model_index": [{"name": "opus-mt-zh-en-ep1-renri-zh-to-en", "results": [{"task": {"name": "Sequence-to-sequence Language Modeling", "type": "text2text-generation"}, "metric": {"name": "Bleu", "type": "bleu", "value": 18.2579}}]}]} | dadada/opus-mt-zh-en-ep1-renri-zh-to-en | null | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"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 #marian #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| opus-mt-zh-en-ep1-renri-zh-to-en
================================
This model is a fine-tuned version of Helsinki-NLP/opus-mt-zh-en on an unkown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.2192
* Bleu: 18.2579
* Gen Len: 28.4817
Model description
-----------------
More information... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\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... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batc... |
sentence-similarity | transformers |
# Similarity between two sentences (fine-tuning with KoELECTRA-Small-v3 model and KorSTS dataset)
## Usage (Amazon SageMaker inference applicable)
It uses the interface of the SageMaker Inference Toolkit as is, so it can be easily deployed to SageMaker Endpoint.
### inference_korsts.py
```python
import json
import... | {"language": ["ko"], "license": "cc-by-4.0", "tags": ["sentence-similarity", "transformers"], "datasets": ["korsts"], "metrics": ["accuracy", "f1", "precision", "recall"], "pipeline_tag": "sentence-similarity"} | daekeun-ml/koelectra-small-v3-korsts | null | [
"transformers",
"pytorch",
"electra",
"text-classification",
"sentence-similarity",
"ko",
"dataset:korsts",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ko"
] | TAGS
#transformers #pytorch #electra #text-classification #sentence-similarity #ko #dataset-korsts #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Similarity between two sentences (fine-tuning with KoELECTRA-Small-v3 model and KorSTS dataset)
## Usage (Amazon SageMaker inference applicable)
It uses the interface of the SageMaker Inference Toolkit as is, so it can be easily deployed to SageMaker Endpoint.
### inference_korsts.py
### URL
### Sample ... | [
"# Similarity between two sentences (fine-tuning with KoELECTRA-Small-v3 model and KorSTS dataset)",
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"### inference_korsts.py",
"### URL",
... | [
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"# Similarity between two sentences (fine-tuning with KoELECTRA-Small-v3 model and KorSTS dataset)",
"## Usage (Amazon SageMaker inf... |
text-classification | transformers |
# Sentiment Binary Classification (fine-tuning with KoELECTRA-Small-v3 model and Naver Sentiment Movie Corpus dataset)
## Usage (Amazon SageMaker inference applicable)
It uses the interface of the SageMaker Inference Toolkit as is, so it can be easily deployed to SageMaker Endpoint.
### inference_nsmc.py
```python
... | {"language": ["ko"], "license": "mit", "tags": ["classification"], "datasets": ["nsmc"], "metrics": ["accuracy", "f1", "precision", "recall- accuracy"], "widget": [{"text": "\ubd88\ud6c4\uc758 \uba85\uc791\uc785\ub2c8\ub2e4! \uc774\ub807\uac8c \uac10\ub3d9\uc801\uc778 \ub0b4\uc6a9\uc740 \ucc98\uc74c\uc774\uc5d0\uc694",... | daekeun-ml/koelectra-small-v3-nsmc | null | [
"transformers",
"pytorch",
"electra",
"text-classification",
"classification",
"ko",
"dataset:nsmc",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ko"
] | TAGS
#transformers #pytorch #electra #text-classification #classification #ko #dataset-nsmc #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# Sentiment Binary Classification (fine-tuning with KoELECTRA-Small-v3 model and Naver Sentiment Movie Corpus dataset)
## Usage (Amazon SageMaker inference applicable)
It uses the interface of the SageMaker Inference Toolkit as is, so it can be easily deployed to SageMaker Endpoint.
### inference_nsmc.py
### ... | [
"# Sentiment Binary Classification (fine-tuning with KoELECTRA-Small-v3 model and Naver Sentiment Movie Corpus dataset)",
"## Usage (Amazon SageMaker inference applicable)\nIt uses the interface of the SageMaker Inference Toolkit as is, so it can be easily deployed to SageMaker Endpoint.",
"### inference_nsmc.p... | [
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"# Sentiment Binary Classification (fine-tuning with KoELECTRA-Small-v3 model and Naver Sentiment Movie Corpus dataset)",
"## Usage (Amazon SageM... |
text-to-image | transformers |
# DALL·E Mini Model Card
This model card focuses on the model associated with the DALL·E mini space on Hugging Face, available [here](https://huggingface.co/spaces/dalle-mini/dalle-mini). The app is called “dalle-mini”, but incorporates “[DALL·E Mini](https://wandb.ai/dalle-mini/dalle-mini/reports/DALL-E-mini-Genera... | {"language": "en", "license": "apache-2.0", "tags": ["text-to-image"], "inference": false, "co2_eq_emissions": {"emissions": 7540, "source": "MLCo2 Machine Learning Impact calculator", "geographical_location": "East USA", "hardware_used": "TPU v3-8"}, "model-index": [{"name": "dalle-mini", "results": []}]} | dalle-mini/dalle-mini | null | [
"transformers",
"jax",
"dallebart",
"text-to-image",
"en",
"arxiv:2102.08981",
"arxiv:2012.09841",
"arxiv:1910.13461",
"arxiv:1910.09700",
"license:apache-2.0",
"co2_eq_emissions",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2102.08981",
"2012.09841",
"1910.13461",
"1910.09700"
] | [
"en"
] | TAGS
#transformers #jax #dallebart #text-to-image #en #arxiv-2102.08981 #arxiv-2012.09841 #arxiv-1910.13461 #arxiv-1910.09700 #license-apache-2.0 #co2_eq_emissions #has_space #region-us
|
# DALL·E Mini Model Card
This model card focuses on the model associated with the DALL·E mini space on Hugging Face, available here. The app is called “dalle-mini”, but incorporates “DALL·E Mini’’ and “DALL·E Mega” models (further details on this distinction forthcoming).
The DALL·E Mega model is the largest versio... | [
"# DALL·E Mini Model Card\n\nThis model card focuses on the model associated with the DALL·E mini space on Hugging Face, available here. The app is called “dalle-mini”, but incorporates “DALL·E Mini’’ and “DALL·E Mega” models (further details on this distinction forthcoming).\n\nThe DALL·E Mega model is the larges... | [
"TAGS\n#transformers #jax #dallebart #text-to-image #en #arxiv-2102.08981 #arxiv-2012.09841 #arxiv-1910.13461 #arxiv-1910.09700 #license-apache-2.0 #co2_eq_emissions #has_space #region-us \n",
"# DALL·E Mini Model Card\n\nThis model card focuses on the model associated with the DALL·E mini space on Hugging Face, ... |
null | transformers | ## VQGAN-f16-16384
### Model Description
This is a Flax/JAX implementation of VQGAN, which learns a codebook of context-rich visual parts by leveraging both the use of convolutional methods and transformers. It was introduced in [Taming Transformers for High-Resolution Image Synthesis](https://compvis.github.io/tamin... | {} | dalle-mini/vqgan_imagenet_f16_16384 | null | [
"transformers",
"jax",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #jax #endpoints_compatible #has_space #region-us
| ## VQGAN-f16-16384
### Model Description
This is a Flax/JAX implementation of VQGAN, which learns a codebook of context-rich visual parts by leveraging both the use of convolutional methods and transformers. It was introduced in Taming Transformers for High-Resolution Image Synthesis (CVPR paper).
The model allows t... | [
"## VQGAN-f16-16384",
"### Model Description\n\nThis is a Flax/JAX implementation of VQGAN, which learns a codebook of context-rich visual parts by leveraging both the use of convolutional methods and transformers. It was introduced in Taming Transformers for High-Resolution Image Synthesis (CVPR paper).\n\nThe m... | [
"TAGS\n#transformers #jax #endpoints_compatible #has_space #region-us \n",
"## VQGAN-f16-16384",
"### Model Description\n\nThis is a Flax/JAX implementation of VQGAN, which learns a codebook of context-rich visual parts by leveraging both the use of convolutional methods and transformers. It was introduced in T... |
fill-mask | transformers |
# HIV_BERT model
## Table of Contents
- [Summary](#model-summary)
- [Model Description](#model-description)
- [Intended Uses & Limitations](#intended-uses-&-limitations)
- [How to Use](#how-to-use)
- [Training Data](#training-data)
- [Training Procedure](#training-procedure)
- [Preprocessing](#preprocessi... | {"license": "mit", "datasets": ["damlab/HIV_FLT"], "metrics": ["accuracy"], "widget": [{"text": "C T R P N N N T R K S I R I Q R G P G R A F V T I G K I G N M R Q A H C", "example_title": "V3"}, {"text": "M E P V D P R L E P W K H P G S Q P K T A C T N C Y C K K C C F H C Q V C F I T K A L G I S Y G R K K R R Q R R R A... | damlab/HIV_BERT | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"dataset:damlab/HIV_FLT",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #dataset-damlab/HIV_FLT #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# HIV_BERT model
## Table of Contents
- Summary
- Model Description
- Intended Uses & Limitations
- How to Use
- Training Data
- Training Procedure
- Preprocessing
- Training
- Evaluation Results
- BibTeX Entry and Citation Info
## Summary
The HIV-BERT model was trained as a refinement of the P... | [
"# HIV_BERT model",
"## Table of Contents\r\n- Summary\r\n- Model Description\r\n- Intended Uses & Limitations\r\n- How to Use\r\n- Training Data\r\n- Training Procedure\r\n - Preprocessing\r\n - Training\r\n- Evaluation Results\r\n- BibTeX Entry and Citation Info",
"## Summary\r\n\r\nThe HIV-BERT model was t... | [
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"# HIV_BERT model",
"## Table of Contents\r\n- Summary\r\n- Model Description\r\n- Intended Uses & Limitations\r\n- How to Use\r\n- Training Data\r\n- Training Procedur... |
text-classification | transformers |
# HIV_PR_resist model
## Table of Contents
- [Summary](#model-summary)
- [Model Description](#model-description)
- [Intended Uses & Limitations](#intended-uses-&-limitations)
- [How to Use](#how-to-use)
- [Training Data](#training-data)
- [Training Procedure](#training-procedure)
- [Preprocessing](#prepro... | {"license": "mit"} | damlab/HIV_PR_resist | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# HIV_PR_resist model
## Table of Contents
- Summary
- Model Description
- Intended Uses & Limitations
- How to Use
- Training Data
- Training Procedure
- Preprocessing
- Training
- Evaluation Results
- BibTeX Entry and Citation Info
## Summary
The HIV-BERT-Protease-Resistance model was trained... | [
"# HIV_PR_resist model",
"## Table of Contents\r\n- Summary\r\n- Model Description\r\n- Intended Uses & Limitations\r\n- How to Use\r\n- Training Data\r\n- Training Procedure\r\n - Preprocessing\r\n - Training\r\n- Evaluation Results\r\n- BibTeX Entry and Citation Info",
"## Summary\r\n\r\nThe HIV-BERT-Protea... | [
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"# HIV_PR_resist model",
"## Table of Contents\r\n- Summary\r\n- Model Description\r\n- Intended Uses & Limitations\r\n- How to Use\r\n- Training Data\r\n- Training Procedure\r\n - ... |
text-classification | transformers |
# HIV_V3_coreceptor model
## Table of Contents
- [Summary](#model-summary)
- [Model Description](#model-description)
- [Intended Uses & Limitations](#intended-uses-&-limitations)
- [How to Use](#how-to-use)
- [Training Data](#training-data)
- [Training Procedure](#training-procedure)
- [Preprocessing](#pr... | {"license": "mit", "widget": [{"text": "C T R P N N N T R K S I R I Q R G P G R A F V T I G K I G N M R Q A H C"}, {"text": "C T R P N N N T R K S I H I G P G R A F Y T T G Q I I G D I R Q A Y C"}, {"text": "C T R P N N N T R R S I R I G P G Q A F Y A T G D I I G D I R Q A H C"}, {"text": "C G R P N N H R I K G L R I G... | damlab/HIV_V3_Coreceptor | null | [
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"bert",
"text-classification",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# HIV_V3_coreceptor model
## Table of Contents
- Summary
- Model Description
- Intended Uses & Limitations
- How to Use
- Training Data
- Training Procedure
- Preprocessing
- Training
- Evaluation Results
- BibTeX Entry and Citation Info
## Summary
The HIV-BERT-Coreceptor model was trained as a... | [
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text-classification | transformers |
# Model Card for [HIV_V3_bodysite]
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Summary](#model-summary)
- [Model Description](#model-description)
- [Intended Uses & Limitations](#intended-uses-&-limitations)
- [How to Use](#how-to-use)
- [Training Data](#training-data)
- [Training Proc... | {"datasets": ["damlab/HIV_V3_bodysite"], "metrics": ["accuracy"], "licence": "mit", "widget": [{"text": "T R P N N N T R K S I R I Q R G P G R A F V T I G K I G N M R Q A H C", "example_title": "V3 Macrophage"}, {"text": "C T R P N N N T R K S I H I G P G R A F Y T T G Q I I G D I R Q A Y C", "example_title": "V3 T-cel... | damlab/HIV_V3_bodysite | null | [
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] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #dataset-damlab/HIV_V3_bodysite #autotrain_compatible #endpoints_compatible #region-us
|
# Model Card for [HIV_V3_bodysite]
## Table of Contents
- Table of Contents
- Summary
- Model Description
- Intended Uses & Limitations
- How to Use
- Training Data
- Training Procedure
- Preprocessing
- Training
- Evaluation Results
- BibTeX Entry and Citation Info
## Summary
The HIV-BERT-Bod... | [
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"#... | [
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"## Table of Contents\r\n- Table of Contents\r\n- Summary\r\n- Model Description\r\n- Intended Uses & Limitations\r\n- How to... |
text-generation | transformers |
#dialogue | {"tags": ["text-generation"]} | danchang11/GPT2-TraditionalChat | null | [
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"pytorch",
"gpt2",
"text-generation",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #endpoints_compatible #text-generation-inference #region-us
|
#dialogue | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #endpoints_compatible #text-generation-inference #region-us \n"
] |
null | transformers |
# Vision-and-Language Transformer (ViLT), fine-tuned on COCO
Vision-and-Language Transformer (ViLT) model fine-tuned on [COCO](https://cocodataset.org/#home). It was introduced in the paper [ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision](https://arxiv.org/abs/2102.03334) by Kim et al... | {"license": "apache-2.0"} | dandelin/vilt-b32-finetuned-coco | null | [
"transformers",
"pytorch",
"vilt",
"arxiv:2102.03334",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2102.03334"
] | [] | TAGS
#transformers #pytorch #vilt #arxiv-2102.03334 #license-apache-2.0 #endpoints_compatible #region-us
|
# Vision-and-Language Transformer (ViLT), fine-tuned on COCO
Vision-and-Language Transformer (ViLT) model fine-tuned on COCO. It was introduced in the paper ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision by Kim et al. and first released in this repository.
Disclaimer: The team relea... | [
"# Vision-and-Language Transformer (ViLT), fine-tuned on COCO\n\nVision-and-Language Transformer (ViLT) model fine-tuned on COCO. It was introduced in the paper ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision by Kim et al. and first released in this repository. \n\nDisclaimer: The te... | [
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"# Vision-and-Language Transformer (ViLT), fine-tuned on COCO\n\nVision-and-Language Transformer (ViLT) model fine-tuned on COCO. It was introduced in the paper ViLT: Vision-and-Language Transformer Wit... |
null | transformers |
# Vision-and-Language Transformer (ViLT), fine-tuned on Flickr30k
Vision-and-Language Transformer (ViLT) model fine-tuned on [Flickr30k](https://arxiv.org/abs/1505.04870#:~:text=The%20Flickr30k%20dataset%20has%20become,for%20sentence%2Dbased%20image%20description.&text=Such%20annotations%20are%20essential%20for,entit... | {"license": "apache-2.0"} | dandelin/vilt-b32-finetuned-flickr30k | null | [
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"pytorch",
"vilt",
"arxiv:1505.04870",
"arxiv:2102.03334",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1505.04870",
"2102.03334"
] | [] | TAGS
#transformers #pytorch #vilt #arxiv-1505.04870 #arxiv-2102.03334 #license-apache-2.0 #endpoints_compatible #region-us
|
# Vision-and-Language Transformer (ViLT), fine-tuned on Flickr30k
Vision-and-Language Transformer (ViLT) model fine-tuned on Flickr30k. It was introduced in the paper ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision by Kim et al. and first released in this repository.
Disclaimer: The ... | [
"# Vision-and-Language Transformer (ViLT), fine-tuned on Flickr30k\n\nVision-and-Language Transformer (ViLT) model fine-tuned on Flickr30k. It was introduced in the paper ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision by Kim et al. and first released in this repository. \n\nDisclaim... | [
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null | transformers |
# Vision-and-Language Transformer (ViLT), fine-tuned on NLVR2
Vision-and-Language Transformer (ViLT) model fine-tuned on [NLVR2](https://lil.nlp.cornell.edu/nlvr/). It was introduced in the paper [ViLT: Vision-and-Language Transformer
Without Convolution or Region Supervision](https://arxiv.org/abs/2102.03334) by Kim... | {"license": "apache-2.0"} | dandelin/vilt-b32-finetuned-nlvr2 | null | [
"transformers",
"pytorch",
"vilt",
"arxiv:2102.03334",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2102.03334"
] | [] | TAGS
#transformers #pytorch #vilt #arxiv-2102.03334 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# Vision-and-Language Transformer (ViLT), fine-tuned on NLVR2
Vision-and-Language Transformer (ViLT) model fine-tuned on NLVR2. It was introduced in the paper ViLT: Vision-and-Language Transformer
Without Convolution or Region Supervision by Kim et al. and first released in this repository.
Disclaimer: The team rel... | [
"# Vision-and-Language Transformer (ViLT), fine-tuned on NLVR2\n\nVision-and-Language Transformer (ViLT) model fine-tuned on NLVR2. It was introduced in the paper ViLT: Vision-and-Language Transformer\nWithout Convolution or Region Supervision by Kim et al. and first released in this repository. \n\nDisclaimer: The... | [
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visual-question-answering | transformers |
# Vision-and-Language Transformer (ViLT), fine-tuned on VQAv2
Vision-and-Language Transformer (ViLT) model fine-tuned on [VQAv2](https://visualqa.org/). It was introduced in the paper [ViLT: Vision-and-Language Transformer
Without Convolution or Region Supervision](https://arxiv.org/abs/2102.03334) by Kim et al. and ... | {"license": "apache-2.0", "tags": ["visual-question-answering"], "widget": [{"text": "What's the animal doing?", "src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg"}, {"text": "What is on top of the building?", "src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/pa... | dandelin/vilt-b32-finetuned-vqa | null | [
"transformers",
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"vilt",
"visual-question-answering",
"arxiv:2102.03334",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2102.03334"
] | [] | TAGS
#transformers #pytorch #vilt #visual-question-answering #arxiv-2102.03334 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# Vision-and-Language Transformer (ViLT), fine-tuned on VQAv2
Vision-and-Language Transformer (ViLT) model fine-tuned on VQAv2. It was introduced in the paper ViLT: Vision-and-Language Transformer
Without Convolution or Region Supervision by Kim et al. and first released in this repository.
Disclaimer: The team rel... | [
"# Vision-and-Language Transformer (ViLT), fine-tuned on VQAv2\n\nVision-and-Language Transformer (ViLT) model fine-tuned on VQAv2. It was introduced in the paper ViLT: Vision-and-Language Transformer\nWithout Convolution or Region Supervision by Kim et al. and first released in this repository. \n\nDisclaimer: The... | [
"TAGS\n#transformers #pytorch #vilt #visual-question-answering #arxiv-2102.03334 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# Vision-and-Language Transformer (ViLT), fine-tuned on VQAv2\n\nVision-and-Language Transformer (ViLT) model fine-tuned on VQAv2. It was introduced in the paper V... |
null | transformers |
# Vision-and-Language Transformer (ViLT), pre-trained only
Vision-and-Language Transformer (ViLT) model pre-trained on GCC+SBU+COCO+VG (200k steps). It was introduced in the paper [ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision](https://arxiv.org/abs/2102.03334) by Kim et al. and firs... | {"license": "apache-2.0"} | dandelin/vilt-b32-mlm-itm | null | [
"transformers",
"pytorch",
"vilt",
"arxiv:2102.03334",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2102.03334"
] | [] | TAGS
#transformers #pytorch #vilt #arxiv-2102.03334 #license-apache-2.0 #endpoints_compatible #region-us
|
# Vision-and-Language Transformer (ViLT), pre-trained only
Vision-and-Language Transformer (ViLT) model pre-trained on GCC+SBU+COCO+VG (200k steps). It was introduced in the paper ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision by Kim et al. and first released in this repository.
Dis... | [
"# Vision-and-Language Transformer (ViLT), pre-trained only\n\nVision-and-Language Transformer (ViLT) model pre-trained on GCC+SBU+COCO+VG (200k steps). It was introduced in the paper ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision by Kim et al. and first released in this repository.... | [
"TAGS\n#transformers #pytorch #vilt #arxiv-2102.03334 #license-apache-2.0 #endpoints_compatible #region-us \n",
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fill-mask | transformers |
# Vision-and-Language Transformer (ViLT), pre-trained only
Vision-and-Language Transformer (ViLT) model pre-trained on GCC+SBU+COCO+VG (200k steps). It was introduced in the paper [ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision](https://arxiv.org/abs/2102.03334) by Kim et al. and firs... | {"license": "apache-2.0"} | dandelin/vilt-b32-mlm | null | [
"transformers",
"pytorch",
"vilt",
"fill-mask",
"arxiv:2102.03334",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2102.03334"
] | [] | TAGS
#transformers #pytorch #vilt #fill-mask #arxiv-2102.03334 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Vision-and-Language Transformer (ViLT), pre-trained only
Vision-and-Language Transformer (ViLT) model pre-trained on GCC+SBU+COCO+VG (200k steps). It was introduced in the paper ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision by Kim et al. and first released in this repository. Note:... | [
"# Vision-and-Language Transformer (ViLT), pre-trained only\n\nVision-and-Language Transformer (ViLT) model pre-trained on GCC+SBU+COCO+VG (200k steps). It was introduced in the paper ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision by Kim et al. and first released in this repository.... | [
"TAGS\n#transformers #pytorch #vilt #fill-mask #arxiv-2102.03334 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Vision-and-Language Transformer (ViLT), pre-trained only\n\nVision-and-Language Transformer (ViLT) model pre-trained on GCC+SBU+COCO+VG (200k steps). It wa... |
null | transformers |
# GPT-2 Fine-tuning in Vietnamese Wikipedia
## Model description
This is a Vietnamese GPT-2 model which is finetuned on the [Latest pages articles of Vietnamese Wikipedia](https://dumps.wikimedia.org/viwiki/latest/viwiki-latest-pages-articles.xml.bz2).
## Dataset
The dataset is about 800MB, includes many articles ... | {"language": "vi", "license": "mit", "tags": ["gpt2-viwiki"]} | danghuy1999/gpt2-viwiki | null | [
"transformers",
"pytorch",
"tf",
"gpt2",
"gpt2-viwiki",
"vi",
"license:mit",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"vi"
] | TAGS
#transformers #pytorch #tf #gpt2 #gpt2-viwiki #vi #license-mit #endpoints_compatible #text-generation-inference #region-us
|
# GPT-2 Fine-tuning in Vietnamese Wikipedia
## Model description
This is a Vietnamese GPT-2 model which is finetuned on the Latest pages articles of Vietnamese Wikipedia.
## Dataset
The dataset is about 800MB, includes many articles from Wikipedia.
## How to use
You can use this model to:
- Tokenize Vietnamese ... | [
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"## Model description\n\nThis is a Vietnamese GPT-2 model which is finetuned on the Latest pages articles of Vietnamese Wikipedia.",
"## Dataset\n\nThe dataset is about 800MB, includes many articles from Wikipedia.",
"## How to use\n\nYou can use this model to:\n... | [
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"# GPT-2 Fine-tuning in Vietnamese Wikipedia",
"## Model description\n\nThis is a Vietnamese GPT-2 model which is finetuned on the Latest pages articles of Vietnamese Wikipedia.... |
sentence-similarity | transformers | ## Description:
[**Sentence-CamemBERT-Large**](https://huggingface.co/dangvantuan/sentence-camembert-large) is the Embedding Model for French developed by [La Javaness](https://www.lajavaness.com/). The purpose of this embedding model is to represent the content and semantics of a French sentence in a mathematical vect... | {"language": "fr", "license": "apache-2.0", "tags": ["Text", "Sentence Similarity", "Sentence-Embedding", "camembert-large"], "datasets": ["stsb_multi_mt"], "pipeline_tag": "sentence-similarity", "model-index": [{"name": "sentence-camembert-large by Van Tuan DANG", "results": [{"task": {"type": "Text Similarity", "name... | dangvantuan/sentence-camembert-large | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"camembert",
"feature-extraction",
"Text",
"Sentence Similarity",
"Sentence-Embedding",
"camembert-large",
"sentence-similarity",
"fr",
"dataset:stsb_multi_mt",
"arxiv:1908.10084",
"license:apache-2.0",
"model-index",
"endpoints_compati... | null | 2022-03-02T23:29:05+00:00 | [
"1908.10084"
] | [
"fr"
] | TAGS
#transformers #pytorch #tf #safetensors #camembert #feature-extraction #Text #Sentence Similarity #Sentence-Embedding #camembert-large #sentence-similarity #fr #dataset-stsb_multi_mt #arxiv-1908.10084 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us
| Description:
------------
Sentence-CamemBERT-Large is the Embedding Model for French developed by La Javaness. The purpose of this embedding model is to represent the content and semantics of a French sentence in a mathematical vector which allows it to understand the meaning of the text-beyond individual words in qu... | [] | [
"TAGS\n#transformers #pytorch #tf #safetensors #camembert #feature-extraction #Text #Sentence Similarity #Sentence-Embedding #camembert-large #sentence-similarity #fr #dataset-stsb_multi_mt #arxiv-1908.10084 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us \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-en-to-pt
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None datase... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["bleu"], "model-index": [{"name": "t5-small-finetuned-en-to-pt", "results": []}]} | danhsf/t5-small-finetuned-en-to-pt | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-en-to-pt
===========================
This model is a fine-tuned version of t5-small on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3295
* Bleu: 5.6807
* Gen Len: 18.6772
Model description
-----------------
More information needed
Intended uses & limi... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.005\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: 10",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-en-to-ro-lr_2e-3-fp_false
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) o... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "metrics": ["bleu"], "model-index": [{"name": "t5-small-finetuned-en-to-ro-lr_2e-3-fp_false", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "wmt16", "type":... | danhsf/t5-small-finetuned-en-to-ro-lr_2e-3-fp_false | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
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"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-lr\_2e-3-fp\_false
==============================================
This model is a fine-tuned version of t5-small on the wmt16 dataset.
It achieves the following results on the evaluation set:
* Loss: 1.4239
* Bleu: 7.1921
* Gen Len: 18.2611
Model description
-----------------
More in... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.002\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 |
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 457311749
- CO2 Emissions (in grams): 10.148805588432941
## Validation Metrics
- Loss: 1.647747278213501
- Rouge1: 32.4854
- Rouge2: 19.8974
- RougeL: 30.0602
- RougeLsum: 29.9377
- Gen Len: 46.6556
## Usage
You can use cURL to access this mo... | {"language": "unk", "tags": "autonlp", "datasets": ["danicodes/autonlp-data-legal-text-summary"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 10.148805588432941} | danicodes/autonlp-legal-text-summary-457311749 | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autonlp",
"unk",
"dataset:danicodes/autonlp-data-legal-text-summary",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #pegasus #text2text-generation #autonlp #unk #dataset-danicodes/autonlp-data-legal-text-summary #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 457311749
- CO2 Emissions (in grams): 10.148805588432941
## Validation Metrics
- Loss: 1.647747278213501
- Rouge1: 32.4854
- Rouge2: 19.8974
- RougeL: 30.0602
- RougeLsum: 29.9377
- Gen Len: 46.6556
## Usage
You can use cURL to access this mo... | [
"# Model Trained Using AutoNLP\n\n- Problem type: Summarization\n- Model ID: 457311749\n- CO2 Emissions (in grams): 10.148805588432941",
"## Validation Metrics\n\n- Loss: 1.647747278213501\n- Rouge1: 32.4854\n- Rouge2: 19.8974\n- RougeL: 30.0602\n- RougeLsum: 29.9377\n- Gen Len: 46.6556",
"## Usage\n\nYou can u... | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #autonlp #unk #dataset-danicodes/autonlp-data-legal-text-summary #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Summarization\n- Model ID: 457311749\n- CO2 Emissions (in gr... |
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-fi-to-en
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wmt19 datas... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt19"], "metrics": ["bleu"], "model-index": [{"name": "t5-small-finetuned-fi-to-en", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "wmt19", "type": "wmt19", "args":... | danielbispov/t5-small-finetuned-fi-to-en | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:wmt19",
"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-wmt19 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-fi-to-en
===========================
This model is a fine-tuned version of t5-small on the wmt19 dataset.
It achieves the following results on the evaluation set:
* Loss: 3.5235
* Bleu: 1.129
* Gen Len: 17.088
Model description
-----------------
More information needed
Intended uses & limit... | [
"### 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-wmt19 #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... |
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. -->
# bangla_asr
This model is a fine-tuned version of [Harveenchadha/vakyansh-wav2vec2-bengali-bnm-200](https://huggingface.co/Harvee... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "bangla_asr", "results": []}]} | danielbubiola/bangla_asr | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #endpoints_compatible #region-us
| bangla\_asr
===========
This model is a fine-tuned version of Harveenchadha/vakyansh-wav2vec2-bengali-bnm-200 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 157.8652
* Wer: 0.4507
Model description
-----------------
More information needed
Intended uses & limitations
---... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_... |
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. -->
# daniel_asr
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "daniel_asr", "results": []}]} | danielbubiola/daniel_asr | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| daniel\_asr
===========
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4565
* Wer: 0.3423
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 3... |
token-classification | spacy | | Feature | Description |
| --- | --- |
| **Name** | `en_acnl_electra_pipeline` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.1.3,<3.2.0` |
| **Default Pipeline** | `transformer`, `tagger`, `parser` |
| **Components** | `transformer`, `tagger`, `parser` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **S... | {"language": ["en"], "tags": ["spacy", "token-classification"]} | danielvasic/en_acnl_electra_pipeline | null | [
"spacy",
"token-classification",
"en",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#spacy #token-classification #en #model-index #region-us
|
### Label Scheme
View label scheme (87 labels for 2 components)
### Accuracy
| [
"### Label Scheme\n\n\n\nView label scheme (87 labels for 2 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #en #model-index #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (87 labels for 2 components)",
"### Accuracy"
] |
text-classification | spacy | | Feature | Description |
| --- | --- |
| **Name** | `en_acnl_roberta_pipeline` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.1.3,<3.2.0` |
| **Default Pipeline** | `transformer`, `tagger`, `parser` |
| **Components** | `transformer`, `tagger`, `parser` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **S... | {"language": ["en"], "license": "cc-by-4.0", "library_name": "spacy", "tags": ["spacy", "token-classification"], "datasets": ["conll2012_ontonotesv5"], "metrics": ["f1"], "pipeline_tag": "text-classification"} | danielvasic/en_acnl_roberta_pipeline | null | [
"spacy",
"token-classification",
"text-classification",
"en",
"dataset:conll2012_ontonotesv5",
"license:cc-by-4.0",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#spacy #token-classification #text-classification #en #dataset-conll2012_ontonotesv5 #license-cc-by-4.0 #model-index #region-us
|
### Label Scheme
View label scheme (87 labels for 2 components)
### Accuracy
| [
"### Label Scheme\n\n\n\nView label scheme (87 labels for 2 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #text-classification #en #dataset-conll2012_ontonotesv5 #license-cc-by-4.0 #model-index #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (87 labels for 2 components)",
"### Accuracy"
] |
token-classification | spacy | | Feature | Description |
| --- | --- |
| **Name** | `hr_bertic_pipeline` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.1.3,<3.2.0` |
| **Default Pipeline** | `transformer`, `morphologizer`, `tagger`, `parser` |
| **Components** | `transformer`, `morphologizer`, `tagger`, `parser` |
| **Vectors** | 0 keys, 0 unique ve... | {"language": ["hr"], "tags": ["spacy", "token-classification"]} | danielvasic/hr_bertic_pipeline | null | [
"spacy",
"token-classification",
"hr",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"hr"
] | TAGS
#spacy #token-classification #hr #model-index #region-us
|
### Label Scheme
View label scheme (1392 labels for 3 components)
### Accuracy
| [
"### Label Scheme\n\n\n\nView label scheme (1392 labels for 3 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #hr #model-index #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (1392 labels for 3 components)",
"### Accuracy"
] |
token-classification | spacy | | Feature | Description |
| --- | --- |
| **Name** | `hr_hroberta_pipeline` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.1.3,<3.2.0` |
| **Default Pipeline** | `transformer`, `morphologizer`, `tagger`, `parser` |
| **Components** | `transformer`, `morphologizer`, `tagger`, `parser` |
| **Vectors** | 0 keys, 0 unique ... | {"language": ["hr"], "license": "cc", "library_name": "spacy", "tags": ["spacy", "token-classification"], "datasets": ["classla/hr500k"], "metrics": ["f1", "accuracy"], "pipeline_tag": "token-classification"} | danielvasic/hr_hroberta_pipeline | null | [
"spacy",
"token-classification",
"hr",
"dataset:classla/hr500k",
"license:cc",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"hr"
] | TAGS
#spacy #token-classification #hr #dataset-classla/hr500k #license-cc #model-index #region-us
|
### Label Scheme
View label scheme (1392 labels for 3 components)
### Accuracy
| [
"### Label Scheme\n\n\n\nView label scheme (1392 labels for 3 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #hr #dataset-classla/hr500k #license-cc #model-index #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (1392 labels for 3 components)",
"### Accuracy"
] |
text-generation | transformers |
# Michael Scott DialoGPT Model | {"tags": ["conversational"]} | danildany/DialoGPT-small-MichaelScott | 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
|
# Michael Scott DialoGPT Model | [
"# Michael Scott DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Michael Scott DialoGPT Model"
] |
multiple-choice | 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. -->
# albert-xxlarge-v2-finetuned-csqa-ih
This model is a fine-tuned version of [albert-xxlarge-v2](https://huggingface.co/albert-xxla... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model_index": {"name": "albert-xxlarge-v2-finetuned-csqa-ih"}} | danlou/albert-xxlarge-v2-finetuned-csqa-ih | null | [
"transformers",
"pytorch",
"albert",
"multiple-choice",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #albert #multiple-choice #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| albert-xxlarge-v2-finetuned-csqa-ih
===================================
This model is a fine-tuned version of albert-xxlarge-v2 on an unkown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.5694
* Accuracy: 0.8026
Model description
-----------------
More information needed
Intended ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-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... | [
"TAGS\n#transformers #pytorch #albert #multiple-choice #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: 1e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\... |
multiple-choice | 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. -->
# albert-xxlarge-v2-finetuned-csqa
This model is a fine-tuned version of [albert-xxlarge-v2](https://huggingface.co/albert-xxlarge... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["commonsense_qa"], "metrics": ["accuracy"], "model_index": [{"name": "albert-xxlarge-v2-finetuned-csqa", "results": [{"dataset": {"name": "commonsense_qa", "type": "commonsense_qa", "args": "default"}, "metric": {"name": "Accuracy", "type": "acc... | danlou/albert-xxlarge-v2-finetuned-csqa | null | [
"transformers",
"pytorch",
"albert",
"multiple-choice",
"generated_from_trainer",
"dataset:commonsense_qa",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #albert #multiple-choice #generated_from_trainer #dataset-commonsense_qa #license-apache-2.0 #endpoints_compatible #region-us
| albert-xxlarge-v2-finetuned-csqa
================================
This model is a fine-tuned version of albert-xxlarge-v2 on the commonsense\_qa dataset.
It achieves the following results on the evaluation set:
* Loss: 1.6177
* Accuracy: 0.7871
Model description
-----------------
More information needed
Inten... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-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... | [
"TAGS\n#transformers #pytorch #albert #multiple-choice #generated_from_trainer #dataset-commonsense_qa #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: 1e-05\n* train\\_batch\\_size: 16\n* ... |
multiple-choice | 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. -->
# aristo-roberta-finetuned-csqa
This model is a fine-tuned version of [LIAMF-USP/aristo-roberta](https://huggingface.co/LIAMF-USP/... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["commonsense_qa"], "metrics": ["accuracy"], "model_index": [{"name": "aristo-roberta-finetuned-csqa", "results": [{"dataset": {"name": "commonsense_qa", "type": "commonsense_qa", "args": "default"}, "metric": {"name": "Accuracy", "type": "accuracy", "v... | danlou/aristo-roberta-finetuned-csqa | null | [
"transformers",
"pytorch",
"roberta",
"multiple-choice",
"generated_from_trainer",
"dataset:commonsense_qa",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #multiple-choice #generated_from_trainer #dataset-commonsense_qa #license-mit #endpoints_compatible #region-us
| aristo-roberta-finetuned-csqa
=============================
This model is a fine-tuned version of LIAMF-USP/aristo-roberta on the commonsense\_qa dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2187
* Accuracy: 0.7305
Model description
-----------------
More information needed
Inte... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-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... | [
"TAGS\n#transformers #pytorch #roberta #multiple-choice #generated_from_trainer #dataset-commonsense_qa #license-mit #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 16\n* eval\\... |
text-classification | transformers | Testing | {} | danlou/distilbert-base-uncased-finetuned-rte | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| Testing | [] | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
multiple-choice | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-large-finetuned-csqa
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the ... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["commonsense_qa"], "metrics": ["accuracy"], "model_index": [{"name": "roberta-large-finetuned-csqa", "results": [{"dataset": {"name": "commonsense_qa", "type": "commonsense_qa", "args": "default"}, "metric": {"name": "Accuracy", "type": "accuracy", "va... | danlou/roberta-large-finetuned-csqa | null | [
"transformers",
"pytorch",
"roberta",
"multiple-choice",
"generated_from_trainer",
"dataset:commonsense_qa",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #multiple-choice #generated_from_trainer #dataset-commonsense_qa #license-mit #endpoints_compatible #region-us
| roberta-large-finetuned-csqa
============================
This model is a fine-tuned version of roberta-large on the commonsense\_qa dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9146
* Accuracy: 0.7330
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-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... | [
"TAGS\n#transformers #pytorch #roberta #multiple-choice #generated_from_trainer #dataset-commonsense_qa #license-mit #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 16\n* eval\\... |
text-generation | transformers | #datnguyen | {"tags": ["conversational"]} | danny481/DialoGPT-small-datnguyenchatbot | null | [
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] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| #datnguyen | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers | #Harry Potter DialoGPT
| {"tags": ["conversational"]} | danny481/DialoGPT-small-harrypotter | null | [
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] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| #Harry Potter DialoGPT
| [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers | #ChatBot updated by datng | {"tags": ["conversational"]} | danny481/Final_ChatBot | null | [
"transformers",
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"autotrain_compatible",
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] | 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 updated by datng | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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