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sentence-similarity | sentence-transformers |
# aditeyabaral/sentencetransformer-bert-hinglish-big
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | aditeyabaral/sentencetransformer-bert-hinglish-big | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# aditeyabaral/sentencetransformer-bert-hinglish-big
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-tra... | [
"# aditeyabaral/sentencetransformer-bert-hinglish-big\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have se... | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# aditeyabaral/sentencetransformer-bert-hinglish-big\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can b... |
sentence-similarity | sentence-transformers |
# aditeyabaral/sentencetransformer-bert-hinglish-small
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transforme... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | aditeyabaral/sentencetransformer-bert-hinglish-small | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# aditeyabaral/sentencetransformer-bert-hinglish-small
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-t... | [
"# aditeyabaral/sentencetransformer-bert-hinglish-small\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have ... | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# aditeyabaral/sentencetransformer-bert-hinglish-small\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can... |
sentence-similarity | sentence-transformers |
# aditeyabaral/sentencetransformer-contrastive-roberta-base
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Trans... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | aditeyabaral/sentencetransformer-contrastive-roberta-base | null | [
"sentence-transformers",
"pytorch",
"roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #roberta #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# aditeyabaral/sentencetransformer-contrastive-roberta-base
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sente... | [
"# aditeyabaral/sentencetransformer-contrastive-roberta-base\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you ... | [
"TAGS\n#sentence-transformers #pytorch #roberta #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# aditeyabaral/sentencetransformer-contrastive-roberta-base\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space... |
sentence-similarity | sentence-transformers |
# aditeyabaral/sentencetransformer-distilbert-base-cased
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transfor... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | aditeyabaral/sentencetransformer-distilbert-base-cased | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# aditeyabaral/sentencetransformer-distilbert-base-cased
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence... | [
"# aditeyabaral/sentencetransformer-distilbert-base-cased\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you hav... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# aditeyabaral/sentencetransformer-distilbert-base-cased\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space... |
sentence-similarity | sentence-transformers |
# aditeyabaral/sentencetransformer-distilbert-hinglish-big
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transf... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | aditeyabaral/sentencetransformer-distilbert-hinglish-big | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# aditeyabaral/sentencetransformer-distilbert-hinglish-big
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have senten... | [
"# aditeyabaral/sentencetransformer-distilbert-hinglish-big\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you h... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# aditeyabaral/sentencetransformer-distilbert-hinglish-big\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector spa... |
sentence-similarity | sentence-transformers |
# aditeyabaral/sentencetransformer-distilbert-hinglish-small
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Tran... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | aditeyabaral/sentencetransformer-distilbert-hinglish-small | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# aditeyabaral/sentencetransformer-distilbert-hinglish-small
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sent... | [
"# aditeyabaral/sentencetransformer-distilbert-hinglish-small\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# aditeyabaral/sentencetransformer-distilbert-hinglish-small\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector s... |
sentence-similarity | sentence-transformers |
# aditeyabaral/sentencetransformer-indic-bert
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Usin... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | aditeyabaral/sentencetransformer-indic-bert | null | [
"sentence-transformers",
"pytorch",
"albert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #albert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# aditeyabaral/sentencetransformer-indic-bert
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transforme... | [
"# aditeyabaral/sentencetransformer-indic-bert\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-... | [
"TAGS\n#sentence-transformers #pytorch #albert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# aditeyabaral/sentencetransformer-indic-bert\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be use... |
sentence-similarity | sentence-transformers |
# aditeyabaral/sentencetransformer-roberta-base
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Us... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | aditeyabaral/sentencetransformer-roberta-base | null | [
"sentence-transformers",
"pytorch",
"roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #roberta #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# aditeyabaral/sentencetransformer-roberta-base
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transfor... | [
"# aditeyabaral/sentencetransformer-roberta-base\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentenc... | [
"TAGS\n#sentence-transformers #pytorch #roberta #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# aditeyabaral/sentencetransformer-roberta-base\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be ... |
sentence-similarity | sentence-transformers |
# aditeyabaral/sentencetransformer-roberta-hinglish-big
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transform... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | aditeyabaral/sentencetransformer-roberta-hinglish-big | null | [
"sentence-transformers",
"pytorch",
"roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #roberta #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# aditeyabaral/sentencetransformer-roberta-hinglish-big
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-... | [
"# aditeyabaral/sentencetransformer-roberta-hinglish-big\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have... | [
"TAGS\n#sentence-transformers #pytorch #roberta #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# aditeyabaral/sentencetransformer-roberta-hinglish-big\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and... |
sentence-similarity | sentence-transformers |
# aditeyabaral/sentencetransformer-roberta-hinglish-small
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transfo... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | aditeyabaral/sentencetransformer-roberta-hinglish-small | null | [
"sentence-transformers",
"pytorch",
"roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #roberta #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# aditeyabaral/sentencetransformer-roberta-hinglish-small
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentenc... | [
"# aditeyabaral/sentencetransformer-roberta-hinglish-small\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you ha... | [
"TAGS\n#sentence-transformers #pytorch #roberta #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# aditeyabaral/sentencetransformer-roberta-hinglish-small\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space a... |
sentence-similarity | sentence-transformers |
# aditeyabaral/sentencetransformer-xlm-roberta-base
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | aditeyabaral/sentencetransformer-xlm-roberta-base | null | [
"sentence-transformers",
"pytorch",
"xlm-roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #xlm-roberta #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# aditeyabaral/sentencetransformer-xlm-roberta-base
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-tran... | [
"# aditeyabaral/sentencetransformer-xlm-roberta-base\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sen... | [
"TAGS\n#sentence-transformers #pytorch #xlm-roberta #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# aditeyabaral/sentencetransformer-xlm-roberta-base\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and... |
text2text-generation | transformers | T5 model
This is a sentence-transformers mode | {} | aditi2222/t5-paraphrase | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| T5 model
This is a sentence-transformers mode | [] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/dis... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"]} | adityavithaldas/distilbert-base-uncased-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of distilbert-base-uncased on the conll2003 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
##... | [
"# distilbert-base-uncased-finetuned-ner\n\nThis model is a fine-tuned version of distilbert-base-uncased on the conll2003 dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# distilbert-base-uncased-finetuned-ner\n\nThis model is a fine-tuned version of distilbert-base-uncased on the c... |
automatic-speech-recognition | transformers | ```python
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
processor = Wav2Vec2Processor.from_pretrained("adresgezgini/Wav2Vec-tr-AG-v1")
model = Wav2Vec2ForCTC.from_pretrained("adresgezgini/Wav2Vec-tr-AG-v1")
```
Dosyalar bölümünde paylaşılan ses1.mp3[1], ses1.mp3[2] ve ses1.mp3[3] ses dosyaları açık kaynak... | {} | adresgezgini/Wav2Vec2-tr-AG-v1 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us
| Dosyalar bölümünde paylaşılan ses1.mp3[1], ses1.mp3[2] ve ses1.mp3[3] ses dosyaları açık kaynaklı canlı kitap ses kayıtları üzerinden 1 - 1.5 dakika arasında belli bir kısmın alınması ile oluşturulmuştur. Oluşturulan sesler ile model test edilmiş ve WER değerleri kaydedilmiştir.
[1]Sabahattin Ali - Çaydanlık | YT:... | [] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us \n"
] |
text-generation | transformers | AdresGezgini Inc. R&D Center Turkish GPT-2 Model Trained with Turkish Wiki Corpus for 10 Epochs
| {} | adresgezgini/turkish-gpt-2 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| AdresGezgini Inc. R&D Center Turkish GPT-2 Model Trained with Turkish Wiki Corpus for 10 Epochs
| [] | [
"TAGS\n#transformers #pytorch #tf #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
automatic-speech-recognition | transformers |
# wav2vec-tr-lite-AG
## Usage
The model can be used directly (without a language model) as follows:
```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
test_dataset = load_dataset("common_voice", "tr", split="test[:2%]")
processor ... | {"language": "tr", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech"], "datasets": ["common_voice"], "metrics": ["wer"]} | adresgezgini/wav2vec-tr-lite-AG | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"tr",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #tr #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec-tr-lite-AG
## Usage
The model can be used directly (without a language model) as follows:
'''python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
test_dataset = load_dataset("common_voice", "tr", split="test[:2%]")
processor ... | [
"# wav2vec-tr-lite-AG",
"## Usage\n\nThe model can be used directly (without a language model) as follows:\n\n'''python\nimport torch\nimport torchaudio\nfrom datasets import load_dataset\nfrom transformers import Wav2Vec2ForCTC, Wav2Vec2Processor\n\ntest_dataset = load_dataset(\"common_voice\", \"tr\", split=\"t... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #tr #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec-tr-lite-AG",
"## Usage\n\nThe model can be used directly (without a language model) as follows:\n\n'''python\nimport torch\nimp... |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 19333717
- CO2 Emissions (in grams): 88.89388195672073
## Validation Metrics
- Loss: 1.0499154329299927
- Accuracy: 0.6207088513638894
- Macro F1: 0.46250803661544765
- Micro F1: 0.6207088513638894
- Weighted F1: 0.5850362079928957... | {"language": "en", "tags": "autonlp", "datasets": ["adrianmoses/autonlp-data-auto-nlp-lyrics-classification"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 88.89388195672073} | adrianmoses/autonlp-auto-nlp-lyrics-classification-19333717 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autonlp",
"en",
"dataset:adrianmoses/autonlp-data-auto-nlp-lyrics-classification",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #autonlp #en #dataset-adrianmoses/autonlp-data-auto-nlp-lyrics-classification #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 19333717
- CO2 Emissions (in grams): 88.89388195672073
## Validation Metrics
- Loss: 1.0499154329299927
- Accuracy: 0.6207088513638894
- Macro F1: 0.46250803661544765
- Micro F1: 0.6207088513638894
- Weighted F1: 0.5850362079928957... | [
"# Model Trained Using AutoNLP\n\n- Problem type: Multi-class Classification\n- Model ID: 19333717\n- CO2 Emissions (in grams): 88.89388195672073",
"## Validation Metrics\n\n- Loss: 1.0499154329299927\n- Accuracy: 0.6207088513638894\n- Macro F1: 0.46250803661544765\n- Micro F1: 0.6207088513638894\n- Weighted F1: ... | [
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"# Model Trained Using AutoNLP\n\n- Problem type: Multi-class Classification\n- Model ID: ... |
null | null | # Hate Speech Detection Model
Created from dataset provided by ROHAN KHILNANI | {} | adrianmoses/hate-speech-detection | null | [
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#has_space #region-us
| # Hate Speech Detection Model
Created from dataset provided by ROHAN KHILNANI | [
"# Hate Speech Detection Model\n\n\nCreated from dataset provided by ROHAN KHILNANI"
] | [
"TAGS\n#has_space #region-us \n",
"# Hate Speech Detection Model\n\n\nCreated from dataset provided by ROHAN KHILNANI"
] |
text-generation | transformers |
# Rick DialoGPT medium model | {"tags": ["conversational"]} | adviksinghania/DialoGPT-medium-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 medium model | [
"# Rick DialoGPT medium model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Rick DialoGPT medium model"
] |
token-classification | transformers | distilbert-base-uncased finetuned on the conll2003 dataset for NER. | {} | adzcodez/TokenClassificationTest | null | [
"transformers",
"pytorch",
"distilbert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #token-classification #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased finetuned on the conll2003 dataset for NER. | [] | [
"TAGS\n#transformers #pytorch #distilbert #token-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-generation | transformers | ## A conversational agent with many personalities (PersonaGPT)
PersonaGPT is an open-domain conversational agent designed to do 2 tasks:
1. decoding _personalized_ responses based on input personality facts (the "persona" profile of the bot).
2. incorporating _turn-level goals_ into its responses through "action code... | {"license": "gpl-3.0", "tags": ["conversational"]} | af1tang/personaGPT | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"arxiv:1801.07243",
"license:gpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1801.07243"
] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #arxiv-1801.07243 #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| A conversational agent with many personalities (PersonaGPT)
-----------------------------------------------------------
PersonaGPT is an open-domain conversational agent designed to do 2 tasks:
1. decoding *personalized* responses based on input personality facts (the "persona" profile of the bot).
2. incorporating... | [
"### How to Use\n\n\n1. Load the model and define some helper functions.\n2. Give your chatbot partner a set of personalities.\n3. The first use of PersonaGPT is to do *personalized* dialog generation. Use the following loop to interact with the model.\n\n\nExample of personalized decoding:\n\n\n\n\n4. The second u... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #arxiv-1801.07243 #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"### How to Use\n\n\n1. Load the model and define some helper functions.\n2. Give your chatbot partner a set of ... |
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-en-de-finetuned-en-to-de
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-de](https://huggingface.co/Helsi... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "metrics": ["bleu"], "model-index": [{"name": "opus-mt-en-de-finetuned-en-to-de", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "wmt16", "type": "wmt16", "a... | afreireosorio/opus-mt-en-de-finetuned-en-to-de | null | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"generated_from_trainer",
"dataset:wmt16",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #marian #text2text-generation #generated_from_trainer #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| opus-mt-en-de-finetuned-en-to-de
================================
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-de on the wmt16 dataset.
It achieves the following results on the evaluation set:
* Loss: 1.6798
* Bleu: 26.4396
* Gen Len: 24.8156
Model description
-----------------
More information... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_preci... | [
"TAGS\n#transformers #pytorch #tensorboard #marian #text2text-generation #generated_from_trainer #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\... |
text-generation | transformers |
# aggb DialogGPT spanish model | {"tags": ["conversational"]} | aggb/DialogGPT-small-AGGB-B | 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
|
# aggb DialogGPT spanish model | [
"# aggb DialogGPT spanish model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# aggb DialogGPT spanish model"
] |
text-classification | transformers | bert-base-uncased model trained on the tobacco800 dataset for the task of page-stream-segmentation.
[Link](https://github.com/agiagoulas/page-stream-segmentation) to the GitHub Repo with the model implementation. | {} | agiagoulas/bert-pss | null | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased model trained on the tobacco800 dataset for the task of page-stream-segmentation.
Link to the GitHub Repo with the model implementation. | [] | [
"TAGS\n#transformers #pytorch #jax #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
null | null |
# Text to Speech Model
## Being used for the `Audio Labeler` effect in Audacity
metadata:
```
{
metadata = {
'sample_rate': 16000,
'domain_tags': ['speech'],
'short_description': 'I will label your speech into text :]',
'long_description':
'This is an Audacity wrapper for the model, '... | {"tags": ["audacity"], "inference": false} | aguilara42/audacity-Wav2Vec2-Base | null | [
"audacity",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#audacity #region-us
|
# Text to Speech Model
## Being used for the 'Audio Labeler' effect in Audacity
metadata:
| [
"# Text to Speech Model",
"## Being used for the 'Audio Labeler' effect in Audacity\n\nmetadata:"
] | [
"TAGS\n#audacity #region-us \n",
"# Text to Speech Model",
"## Being used for the 'Audio Labeler' effect in Audacity\n\nmetadata:"
] |
null | null |
# Labeler With Timestamps
## Being used for the `Audio Labeler` effect in Audacity
This is a audio labeler model which is used in Audacity's labeler effect.
metadata:
```
{
"sample_rate": 48000,
"domain_tags": ["Music"],
"tags": ["Audio Labeler"],
"eff... | {"tags": ["audacity"], "inference": false} | aguilara42/openl3-labeler-w-timestamps | null | [
"audacity",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#audacity #region-us
|
# Labeler With Timestamps
## Being used for the 'Audio Labeler' effect in Audacity
This is a audio labeler model which is used in Audacity's labeler effect.
metadata:
| [
"# Labeler With Timestamps",
"## Being used for the 'Audio Labeler' effect in Audacity\n\nThis is a audio labeler model which is used in Audacity's labeler effect. \n\nmetadata:"
] | [
"TAGS\n#audacity #region-us \n",
"# Labeler With Timestamps",
"## Being used for the 'Audio Labeler' effect in Audacity\n\nThis is a audio labeler model which is used in Audacity's labeler effect. \n\nmetadata:"
] |
null | transformers | Hello World! | {} | ahanadeb/wav2vec2-large-indian-instrument-classification-v1 | null | [
"transformers",
"pytorch",
"wav2vec2",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #endpoints_compatible #region-us
| Hello World! | [] | [
"TAGS\n#transformers #pytorch #wav2vec2 #endpoints_compatible #region-us \n"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-colab", "results": []}]} | ahazeemi/wav2vec2-base-timit-demo-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-base-timit-demo-colab
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hy... | [
"# wav2vec2-base-timit-demo-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training ... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-base-timit-demo-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.",
"## Model description\n\nM... |
null | speechbrain |
<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe>
<br/><br/>
# Speaker Verification with ECAPA-TDNN embeddings on Voxceleb
This repository provides all the necessary too... | {"language": "en", "license": "apache-2.0", "tags": ["speechbrain", "embeddings", "Speaker", "Verification", "Identification", "pytorch", "ECAPA", "TDNN"], "datasets": ["voxceleb"], "metrics": ["EER"], "widget": [{"example_title": "VoxCeleb Speaker id10003", "src": "https://cdn-media.huggingface.co/speech_samples/VoxCe... | aheba31/test-predictor | null | [
"speechbrain",
"embeddings",
"Speaker",
"Verification",
"Identification",
"pytorch",
"ECAPA",
"TDNN",
"en",
"dataset:voxceleb",
"arxiv:2106.04624",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.04624"
] | [
"en"
] | TAGS
#speechbrain #embeddings #Speaker #Verification #Identification #pytorch #ECAPA #TDNN #en #dataset-voxceleb #arxiv-2106.04624 #license-apache-2.0 #region-us
|
Speaker Verification with ECAPA-TDNN embeddings on Voxceleb
===========================================================
This repository provides all the necessary tools to perform speaker verification with a pretrained ECAPA-TDNN model using SpeechBrain.
The system can be used to extract speaker embeddings as... | [
"### Compute your speaker embeddings",
"### Perform Speaker Verification\n\n\nThe prediction is 1 if the two signals in input are from the same speaker and 0 otherwise.",
"### Inference on GPU\n\n\nTo perform inference on the GPU, add 'run\\_opts={\"device\":\"cuda\"}' when calling the 'from\\_hparams' method."... | [
"TAGS\n#speechbrain #embeddings #Speaker #Verification #Identification #pytorch #ECAPA #TDNN #en #dataset-voxceleb #arxiv-2106.04624 #license-apache-2.0 #region-us \n",
"### Compute your speaker embeddings",
"### Perform Speaker Verification\n\n\nThe prediction is 1 if the two signals in input are from the same... |
null | transformers |
<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe>
<br/><br/>
# Speaker Verification with ECAPA-TDNN embeddings on Zaion
This repository provides all the necessary tools ... | {"language": "en", "license": "apache-2.0", "tags": ["speechbrain", "embeddings", "Speaker", "Verification", "Identification", "pytorch", "ECAPA", "TDNN"], "datasets": ["Zaion corpus"], "metrics": ["EER"], "widget": [{"example_title": "VoxCeleb Speaker id10003", "src": "https://cdn-media.huggingface.co/speech_samples/V... | aheba31/zaion-speaker-ident | null | [
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #endpoints_compatible #region-us
|
<iframe src="URL frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe>
<br/><br/>
# Speaker Verification with ECAPA-TDNN embeddings on Zaion
This repository provides all the necessary tools to perform speaker verification with a pretrained ECAPA-TDNN model using SpeechBrain.
The system can ... | [
"# Speaker Verification with ECAPA-TDNN embeddings on Zaion\n\nThis repository provides all the necessary tools to perform speaker verification with a pretrained ECAPA-TDNN model using SpeechBrain. \nThe system can be used to extract speaker embeddings as well. \nIt is trained on Voxceleb 1+ Voxceleb2 training data... | [
"TAGS\n#transformers #endpoints_compatible #region-us \n",
"# Speaker Verification with ECAPA-TDNN embeddings on Zaion\n\nThis repository provides all the necessary tools to perform speaker verification with a pretrained ECAPA-TDNN model using SpeechBrain. \nThe system can be used to extract speaker embeddings as... |
text-classification | transformers | ### FinancialBERT for Sentiment Analysis
[*FinancialBERT*](https://huggingface.co/ahmedrachid/FinancialBERT) is a BERT model pre-trained on a large corpora of financial texts. The purpose is to enhance financial NLP research and practice in financial domain, hoping that financial practitioners and researchers can bene... | {"language": "en", "tags": ["financial-sentiment-analysis", "sentiment-analysis"], "datasets": ["financial_phrasebank"], "widget": [{"text": "Operating profit rose to EUR 13.1 mn from EUR 8.7 mn in the corresponding period in 2007 representing 7.7 % of net sales."}, {"text": "Bids or offers include at least 1,000 share... | ahmedrachid/FinancialBERT-Sentiment-Analysis | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"financial-sentiment-analysis",
"sentiment-analysis",
"en",
"dataset:financial_phrasebank",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #financial-sentiment-analysis #sentiment-analysis #en #dataset-financial_phrasebank #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### FinancialBERT for Sentiment Analysis
*FinancialBERT* is a BERT model pre-trained on a large corpora of financial texts. The purpose is to enhance financial NLP research and practice in financial domain, hoping that financial practitioners and researchers can benefit from this model without the necessity of the si... | [
"### FinancialBERT for Sentiment Analysis\n\n\n*FinancialBERT* is a BERT model pre-trained on a large corpora of financial texts. The purpose is to enhance financial NLP research and practice in financial domain, hoping that financial practitioners and researchers can benefit from this model without the necessity o... | [
"TAGS\n#transformers #pytorch #bert #text-classification #financial-sentiment-analysis #sentiment-analysis #en #dataset-financial_phrasebank #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### FinancialBERT for Sentiment Analysis\n\n\n*FinancialBERT* is a BERT model pre-trained on a large ... |
fill-mask | transformers | **FinancialBERT** is a BERT model pre-trained on a large corpora of financial texts. The purpose is to enhance financial NLP research and practice in financial domain, hoping that financial practitioners and researchers can benefit from it without the necessity of the significant computational resources required to tra... | {"language": "en", "tags": ["fill-mask"], "widget": [{"text": "Tesla remains one of the highest [MASK] stocks on the market. Meanwhile, Aurora Innovation is a pre-revenue upstart that shows promise."}, {"text": "Asian stocks [MASK] from a one-year low on Wednesday as U.S. share futures and oil recovered from the previo... | ahmedrachid/FinancialBERT | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"en",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #fill-mask #en #autotrain_compatible #endpoints_compatible #has_space #region-us
| FinancialBERT is a BERT model pre-trained on a large corpora of financial texts. The purpose is to enhance financial NLP research and practice in financial domain, hoping that financial practitioners and researchers can benefit from it without the necessity of the significant computational resources required to train t... | [] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #en #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
text2text-generation | transformers |
#Bert2Bert Turkish Paraphrase Generation
#INISTA 2021
#Comparison of Turkish Paraphrase Generation Models
#Dataset
The dataset used in model training was created with the combination of the translation of the QQP dataset and manually generated dataset.
Dataset [Link](https://drive.google.com/file/d/1-2l9EwIzXZ7fUk... | {"language": ["tr"], "tags": ["paraphrasing", "encoder-decoder", "seq2seq", "bert"]} | ahmetbagci/bert2bert-turkish-paraphrase-generation | null | [
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"bert",
"tr",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #encoder-decoder #text2text-generation #paraphrasing #seq2seq #bert #tr #autotrain_compatible #endpoints_compatible #region-us
|
#Bert2Bert Turkish Paraphrase Generation
#INISTA 2021
#Comparison of Turkish Paraphrase Generation Models
#Dataset
The dataset used in model training was created with the combination of the translation of the QQP dataset and manually generated dataset.
Dataset Link
#How To Use
#Cite
| [] | [
"TAGS\n#transformers #pytorch #encoder-decoder #text2text-generation #paraphrasing #seq2seq #bert #tr #autotrain_compatible #endpoints_compatible #region-us \n"
] |
question-answering | transformers | ## Albert xxlarge version 1 language model fine-tuned on SQuAD2.0
### (updated 30Sept2020) with the following results:
```
exact: 86.11134506864315
f1: 89.35371214945009
total': 11873
HasAns_exact': 83.56950067476383
HasAns_f1': 90.06353312254078
HasAns_total': 5928
NoAns_exact': 88.64592094196804
NoAns_f1': 88.6459... | {} | ahotrod/albert_xxlargev1_squad2_512 | null | [
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] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #albert #question-answering #endpoints_compatible #has_space #region-us
| ## Albert xxlarge version 1 language model fine-tuned on SQuAD2.0
### (updated 30Sept2020) with the following results:
### from script:
### using the following software & system:
| [
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"### (updated 30Sept2020) with the following results:",
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] |
question-answering | transformers | ## ELECTRA_large_discriminator language model fine-tuned on SQuAD2.0
### with the following results:
```
"exact": 87.09677419354838,
"f1": 89.98343832723452,
"total": 11873,
"HasAns_exact": 84.66599190283401,
"HasAns_f1": 90.44759839056285,
"HasAns_total": 5928,
"NoAns_exact": 89.52060555088309,
"NoAn... | {} | ahotrod/electra_large_discriminator_squad2_512 | null | [
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"tf",
"electra",
"question-answering",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #electra #question-answering #endpoints_compatible #has_space #region-us
| ## ELECTRA_large_discriminator language model fine-tuned on SQuAD2.0
### with the following results:
### from script:
### using the following system & software:
| [
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"### with the following results:",
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] |
text2text-generation | transformers |
IndicBART is a multilingual, sequence-to-sequence pre-trained model focusing on Indic languages and English. It currently supports 11 Indian languages and is based on the mBART architecture. You can use IndicBART model to build natural language generation applications for Indian languages by finetuning the model with ... | {"language": ["as", "bn", "gu", "hi", "kn", "ml", "mr", "or", "pa", "ta", "te"], "tags": ["multilingual", "nlp", "indicnlp"]} | ai4bharat/IndicBART | null | [
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|
IndicBART is a multilingual, sequence-to-sequence pre-trained model focusing on Indic languages and English. It currently supports 11 Indian languages and is based on the mBART architecture. You can use IndicBART model to build natural language generation applications for Indian languages by finetuning the model with ... | [
"# Pre-training corpus\n\nWe used the <a href=\"URL data spanning 12 languages with 452 million sentences (9 billion tokens). The model was trained using the text-infilling objective used in mBART.",
"# Usage:",
"# Notes:\n1. This is compatible with the latest version of transformers but was developed with vers... | [
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"# Pre-training corpus\n\nWe used the <a href=\"URL data spanning 12 languages with 452 million sentence... |
null | transformers |
# IndicBERT
IndicBERT is a multilingual ALBERT model pretrained exclusively on 12 major Indian languages. It is pre-trained on our novel monolingual corpus of around 9 billion tokens and subsequently evaluated on a set of diverse tasks. IndicBERT has much fewer parameters than other multilingual models (mBERT, XLM-R ... | {"language": ["as", "bn", "en", "gu", "hi", "kn", "ml", "mr", "or", "pa", "ta", "te"], "license": "mit", "datasets": ["AI4Bharat IndicNLP Corpora"]} | ai4bharat/indic-bert | null | [
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| IndicBERT
=========
IndicBERT is a multilingual ALBERT model pretrained exclusively on 12 major Indian languages. It is pre-trained on our novel monolingual corpus of around 9 billion tokens and subsequently evaluated on a set of diverse tasks. IndicBERT has much fewer parameters than other multilingual models (mBERT... | [
"#### IndicGLUE",
"#### Additional Tasks\n\n\n\n\\* Note: all models have been restricted to a max\\_seq\\_length of 128.\n\n\nDownloads\n---------\n\n\nThe model can be downloaded here. Both tf checkpoints and pytorch binaries are included in the archive. Alternatively, you can also download it from Huggingface.... | [
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"#### Additional Tasks\n\n\n\n\\* Note: all models have been restricted to a max\\_seq\\_length of 128.\n\n\nDownloads\n---------\n\n\nThe model c... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# recipe-improver
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on an unknown dataset.
I... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "recipe-improver", "results": []}]} | aidan-o-brien/recipe-improver | null | [
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"question-answering",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #tf #albert #question-answering #generated_from_keras_callback #license-apache-2.0 #endpoints_compatible #has_space #region-us
| recipe-improver
===============
This model is a fine-tuned version of albert-base-v2 on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 2.5570
* Epoch: 0
Model description
-----------------
More information needed
Intended uses & limitations
-------------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 5e-05, 'decay\\_steps': 5539, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'nam... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': {'class\\_name'... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/dis... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "con... | aidj/distilbert-base-uncased-finetuned-ner | null | [
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"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
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| distilbert-base-uncased-finetuned-ner
=====================================
This model is a fine-tuned version of distilbert-base-uncased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0607
* Precision: 0.9260
* Recall: 0.9384
* F1: 0.9322
* Accuracy: 0.9834
Model des... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* le... |
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. -->
# vivos_prj1tha
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["vivos_dataset"], "model-index": [{"name": "vivos_prj1tha", "results": []}]} | aiface/vivos_prj1tha | null | [
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"tensorboard",
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"automatic-speech-recognition",
"generated_from_trainer",
"dataset:vivos_dataset",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-vivos_dataset #license-apache-2.0 #endpoints_compatible #region-us
| vivos\_prj1tha
==============
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the vivos\_dataset dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7737
* Wer: 0.5128
Model description
-----------------
More information needed
Intended uses & limitations
--------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* ... |
text-generation | transformers |
# My Awesome Model | {"tags": ["conversational"]} | aimiekhe/yummv1 | null | [
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"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
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] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
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] |
text-generation | transformers |
# My Awesome Model | {"tags": ["conversational"]} | aimiekhe/yummv2 | null | [
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] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
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] |
summarization | transformers | # BART base model fine-tuned on CNN Dailymail
- This model is a [bart-base model](https://huggingface.co/facebook/bart-base) fine-tuned on the [CNN/Dailymail summarization dataset](https://huggingface.co/datasets/cnn_dailymail) using [Ainize Teachable-NLP](https://ainize.ai/teachable-nlp).
The Bart model was proposed... | {"language": "en", "license": "apache-2.0", "tags": ["summarization", "bart"], "datasets": ["cnn_dailymail"]} | ainize/bart-base-cnn | null | [
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"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bart #feature-extraction #summarization #en #dataset-cnn_dailymail #license-apache-2.0 #endpoints_compatible #has_space #region-us
| # BART base model fine-tuned on CNN Dailymail
- This model is a bart-base model fine-tuned on the CNN/Dailymail summarization dataset using Ainize Teachable-NLP.
The Bart model was proposed by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov and Luke Zettlemoyer ... | [
"# BART base model fine-tuned on CNN Dailymail\n\n- This model is a bart-base model fine-tuned on the CNN/Dailymail summarization dataset using Ainize Teachable-NLP.\n\nThe Bart model was proposed by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov and Luke Zet... | [
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feature-extraction | transformers |
Original repository : <https://huggingface.co/EleutherAI/gpt-j-6B> | {"license": "apache-2.0"} | ainize/gpt-j-6B-float16 | null | [
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"feature-extraction",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
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|
Original repository : <URL | [] | [
"TAGS\n#transformers #pytorch #gptj #feature-extraction #license-apache-2.0 #endpoints_compatible #has_space #region-us \n"
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text-generation | transformers | ### Model information
Fine tuning data 1: https://www.kaggle.com/andradaolteanu/rickmorty-scripts
Base model: e-tony/gpt2-rnm
Epoch: 1
Train runtime: 3.4982 secs
Loss: 3.0894
Training notebook: [Colab](https://colab.research.google.com/drive/1RawVxulLETFicWMY0YANUdP-H-e7Eeyc)
### ===Teachabl... | {} | ainize/gpt2-rnm-with-only-rick | null | [
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"gpt2",
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"endpoints_compatible",
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] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| ### Model information
Fine tuning data 1: URL
Base model: e-tony/gpt2-rnm
Epoch: 1
Train runtime: 3.4982 secs
Loss: 3.0894
Training notebook: Colab
### ===Teachable NLP=== ###
To train a GPT-2 model, write code and require GPU resources, but can easily fine-tune and get an API to use the mo... | [
"### Model information\n \n Fine tuning data 1: URL\n Base model: e-tony/gpt2-rnm\n Epoch: 1\n Train runtime: 3.4982 secs\n Loss: 3.0894\n\n\nTraining notebook: Colab",
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text-generation | transformers | ### Model information
Fine tuning data 1: https://www.kaggle.com/andradaolteanu/rickmorty-scripts
Base model: e-tony/gpt2-rnm
Epoch: 3
Train runtime: 7.1779 secs
Loss: 2.5694
Training notebook: [Colab](https://colab.research.google.com/drive/12NvO1SIZevF8ybJqfN9O21I3i9bU1dOO#scrollTo=KUs... | {} | ainize/gpt2-rnm-with-season-1 | null | [
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#transformers #pytorch #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| ### Model information
Fine tuning data 1: URL
Base model: e-tony/gpt2-rnm
Epoch: 3
Train runtime: 7.1779 secs
Loss: 2.5694
Training notebook: Colab
### ===Teachable NLP=== ###
To train a GPT-2 model, write code and require GPU resources, but can easily fine-tune and get an API to use ... | [
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text-generation | transformers | ### Model information
Fine tuning data 1: https://www.kaggle.com/andradaolteanu/rickmorty-scripts
Fine tuning data 2: https://www.kaggle.com/mikhailgaerlan/spongebob-squarepants-completed-transcripts
Base model: e-tony/gpt2-rnm
Epoch: 2
Train runtime: 790.0612 secs
Loss: 2.8569
API page: [... | {} | ainize/gpt2-rnm-with-spongebob | null | [
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"jax",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
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] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| ### Model information
Fine tuning data 1: URL
Fine tuning data 2: URL
Base model: e-tony/gpt2-rnm
Epoch: 2
Train runtime: 790.0612 secs
Loss: 2.8569
API page: Ainize
Demo page: End-point
### ===Teachable NLP=== ###
To train a GPT-2 model, write code and require GPU resources, but can ea... | [
"### Model information\n \n Fine tuning data 1: URL\n Fine tuning data 2: URL\n Base model: e-tony/gpt2-rnm\n Epoch: 2\n Train runtime: 790.0612 secs\n Loss: 2.8569\n\nAPI page: Ainize\n\nDemo page: End-point",
"### ===Teachable NLP=== ###\n\nTo train a GPT-2 model, write code and require GPU... | [
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Model information\n \n Fine tuning data 1: URL\n Fine tuning data 2: URL\n Base model: e-tony/gpt2-rnm\n Epoch: 2\n Train runtime: 790.0612 secs\n... |
text-generation | transformers | ### Model information
Fine tuning data: https://www.kaggle.com/mikhailgaerlan/spongebob-squarepants-completed-transcripts
License: CC-BY-SA
Base model: gpt-2 large
Epoch: 50
Train runtime: 14723.0716 secs
Loss: 0.0268
API page: [Ainize](https://ainize.ai/fpem123/GPT2-Spongebob?bra... | {} | ainize/gpt2-spongebob-script-large | null | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| ### Model information
Fine tuning data: URL
License: CC-BY-SA
Base model: gpt-2 large
Epoch: 50
Train runtime: 14723.0716 secs
Loss: 0.0268
API page: Ainize
Demo page: End-point
### ===Teachable NLP=== ###
To train a GPT-2 model, write code and require GPU resources, but can ea... | [
"### Model information\n \n Fine tuning data: URL\n License: CC-BY-SA\n Base model: gpt-2 large \n Epoch: 50\n Train runtime: 14723.0716 secs\n Loss: 0.0268\n \n\nAPI page: Ainize\n\nDemo page: End-point",
"### ===Teachable NLP=== ###\n\nTo train a GPT-2 model, write code and require GP... | [
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"### Model information\n \n Fine tuning data: URL\n License: CC-BY-SA\n Base model: gpt-2 large \n Epoch: 50\n Train runtime: 14723.0716 ... |
question-answering | transformers |
# bert-base for QA
**Code:** See [Ainize Workspace](https://link.ainize.ai/3FjvBVn)
**klue-bert-base-mrc DEMO**: [Ainize DEMO](https://main-klue-mrc-bert-scy6500.endpoint.ainize.ai/)
**klue-bert-base-mrc API**: [Ainize API](https://ainize.ai/scy6500/KLUE-MRC-BERT?branch=main)
## Overview
**Language model:** klu... | {"language": "ko", "license": "cc-by-sa-4.0", "tags": ["bert", "mrc"], "datasets": ["klue"]} | ainize/klue-bert-base-mrc | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"mrc",
"ko",
"dataset:klue",
"license:cc-by-sa-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ko"
] | TAGS
#transformers #pytorch #bert #question-answering #mrc #ko #dataset-klue #license-cc-by-sa-4.0 #endpoints_compatible #region-us
|
# bert-base for QA
Code: See Ainize Workspace
klue-bert-base-mrc DEMO: Ainize DEMO
klue-bert-base-mrc API: Ainize API
## Overview
Language model: klue/bert-base
Language: Korean
Downstream-task: Extractive QA
Training data: KLUE-MRC
Eval data: KLUE-MRC
## Usage
### In Transformers
## About us
... | [
"# bert-base for QA \n\nCode: See Ainize Workspace \n\nklue-bert-base-mrc DEMO: Ainize DEMO\n\nklue-bert-base-mrc API: Ainize API",
"## Overview\nLanguage model: klue/bert-base \nLanguage: Korean \nDownstream-task: Extractive QA \nTraining data: KLUE-MRC \nEval data: KLUE-MRC",
"## Usage",
"### In Tran... | [
"TAGS\n#transformers #pytorch #bert #question-answering #mrc #ko #dataset-klue #license-cc-by-sa-4.0 #endpoints_compatible #region-us \n",
"# bert-base for QA \n\nCode: See Ainize Workspace \n\nklue-bert-base-mrc DEMO: Ainize DEMO\n\nklue-bert-base-mrc API: Ainize API",
"## Overview\nLanguage model: klue/bert-... |
text-classification | transformers | # bert-base for KLUE Relation Extraction task.
Fine-tuned klue/bert-base using KLUE RE dataset.
- <a href="https://klue-benchmark.com/">KLUE Benchmark Official Webpage</a>
- <a href="https://github.com/KLUE-benchmark/KLUE">KLUE Official Github</a>
- <a href="https://github.com/ainize-team/klue-re-workspace">KLUE RE Gi... | {} | ainize/klue-bert-base-re | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| # bert-base for KLUE Relation Extraction task.
Fine-tuned klue/bert-base using KLUE RE dataset.
- <a href="URL Benchmark Official Webpage</a>
- <a href="URL Official Github</a>
- <a href="URL RE Github</a>
- Run KLUE RE on free GPU : <a href="URL/URL Workspace</a>
<br>
# Usage
<pre><code>
from transformers import Au... | [
"# bert-base for KLUE Relation Extraction task.\nFine-tuned klue/bert-base using KLUE RE dataset.\n- <a href=\"URL Benchmark Official Webpage</a>\n- <a href=\"URL Official Github</a> \n- <a href=\"URL RE Github</a>\n- Run KLUE RE on free GPU : <a href=\"URL/URL Workspace</a>\n\n<br>",
"# Usage\n<pre><code>\nfrom ... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# bert-base for KLUE Relation Extraction task.\nFine-tuned klue/bert-base using KLUE RE dataset.\n- <a href=\"URL Benchmark Official Webpage</a>\n- <a href=\"URL Official Github</a> \n- <a href=\"... |
summarization | transformers | # kobart-news
- This model is a [kobart](https://huggingface.co/hyunwoongko/kobart) fine-tuned on the [문서요약 텍스트/신문기사](https://aihub.or.kr/aidata/8054) using [Ainize Teachable-NLP](https://ainize.ai/teachable-nlp).
## Usage
### Python Code
```python
from transformers import PreTrainedTokenizerFast, BartForConditionalGe... | {"language": "ko", "license": "mit", "tags": ["summarization", "bart"]} | ainize/kobart-news | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"summarization",
"ko",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ko"
] | TAGS
#transformers #pytorch #bart #text2text-generation #summarization #ko #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
| # kobart-news
- This model is a kobart fine-tuned on the 문서요약 텍스트/신문기사 using Ainize Teachable-NLP.
## Usage
### Python Code
### API and Demo
You can experience this model through ainize-api and ainize-demo.
| [
"# kobart-news\n- This model is a kobart fine-tuned on the 문서요약 텍스트/신문기사 using Ainize Teachable-NLP.",
"## Usage",
"### Python Code",
"### API and Demo\nYou can experience this model through ainize-api and ainize-demo."
] | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #summarization #ko #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# kobart-news\n- This model is a kobart fine-tuned on the 문서요약 텍스트/신문기사 using Ainize Teachable-NLP.",
"## Usage",
"### Python Code",
"### API and ... |
summarization | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-base-wikinewssum-all-languages
This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-bas... | {"license": "apache-2.0", "tags": ["summarization", "generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "mt5-base-wikinewssum-all-languages", "results": []}]} | airKlizz/mt5-base-wikinewssum-all-languages | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"summarization",
"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 #mt5 #text2text-generation #summarization #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| mt5-base-wikinewssum-all-languages
==================================
This model is a fine-tuned version of google/mt5-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.2454
* Rouge1: 8.3826
* Rouge2: 3.5524
* Rougel: 6.8656
* Rougelsum: 7.8362
Model description
------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5.6e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\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=... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #summarization #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\\_r... |
summarization | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-base-wikinewssum-english-100
This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base)... | {"license": "apache-2.0", "tags": ["summarization", "generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "mt5-base-wikinewssum-english-100", "results": []}]} | airKlizz/mt5-base-wikinewssum-english-100 | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"summarization",
"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 #mt5 #text2text-generation #summarization #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| mt5-base-wikinewssum-english-100
================================
This model is a fine-tuned version of google/mt5-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 6.6225
* Rouge1: 3.909
* Rouge2: 0.9312
* Rougel: 3.3835
* Rougelsum: 3.7786
Model description
-----------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5.6e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\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=... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #summarization #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\\_r... |
summarization | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-base-wikinewssum-english-1000
This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base... | {"license": "apache-2.0", "tags": ["summarization", "generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "mt5-base-wikinewssum-english-1000", "results": []}]} | airKlizz/mt5-base-wikinewssum-english-1000 | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"summarization",
"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 #mt5 #text2text-generation #summarization #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| mt5-base-wikinewssum-english-1000
=================================
This model is a fine-tuned version of google/mt5-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.4724
* Rouge1: 7.7389
* Rouge2: 3.1606
* Rougel: 6.3317
* Rougelsum: 7.2487
Model description
--------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5.6e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\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=... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #summarization #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\\_r... |
summarization | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-base-wikinewssum-english
This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on ... | {"license": "apache-2.0", "tags": ["summarization", "generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "mt5-base-wikinewssum-english", "results": []}]} | airKlizz/mt5-base-wikinewssum-english | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"summarization",
"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 #mt5 #text2text-generation #summarization #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| mt5-base-wikinewssum-english
============================
This model is a fine-tuned version of google/mt5-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.3040
* Rouge1: 8.9565
* Rouge2: 3.6563
* Rougel: 7.1346
* Rougelsum: 8.3802
Model description
-----------------
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5.6e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\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=... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #summarization #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\\_r... |
summarization | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-base-wikinewssum-french
This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on a... | {"license": "apache-2.0", "tags": ["summarization", "generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "mt5-base-wikinewssum-french", "results": []}]} | airKlizz/mt5-base-wikinewssum-french | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"summarization",
"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 #mt5 #text2text-generation #summarization #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| mt5-base-wikinewssum-french
===========================
This model is a fine-tuned version of google/mt5-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.0917
* Rouge1: 12.0984
* Rouge2: 5.7289
* Rougel: 9.9245
* Rougelsum: 11.0697
Model description
-----------------
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5.6e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\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=... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #summarization #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\\_r... |
summarization | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-base-wikinewssum-german
This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on a... | {"license": "apache-2.0", "tags": ["summarization", "generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "mt5-base-wikinewssum-german", "results": []}]} | airKlizz/mt5-base-wikinewssum-german | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"summarization",
"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 #mt5 #text2text-generation #summarization #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| mt5-base-wikinewssum-german
===========================
This model is a fine-tuned version of google/mt5-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.5135
* Rouge1: 8.0553
* Rouge2: 2.7846
* Rougel: 6.2182
* Rougelsum: 7.6203
Model description
-----------------
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5.6e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\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=... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #summarization #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\\_r... |
summarization | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-base-wikinewssum-italian
This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on ... | {"license": "apache-2.0", "tags": ["summarization", "generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "mt5-base-wikinewssum-italian", "results": []}]} | airKlizz/mt5-base-wikinewssum-italian | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"summarization",
"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 #mt5 #text2text-generation #summarization #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| mt5-base-wikinewssum-italian
============================
This model is a fine-tuned version of google/mt5-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 10.5739
* Rouge1: 2.1728
* Rouge2: 0.1516
* Rougel: 2.0846
* Rougelsum: 2.0515
Model description
-----------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5.6e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\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=... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #summarization #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\\_r... |
summarization | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-base-wikinewssum-polish
This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on a... | {"license": "apache-2.0", "tags": ["summarization", "generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "mt5-base-wikinewssum-polish", "results": []}]} | airKlizz/mt5-base-wikinewssum-polish | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"summarization",
"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 #mt5 #text2text-generation #summarization #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| mt5-base-wikinewssum-polish
===========================
This model is a fine-tuned version of google/mt5-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.3179
* Rouge1: 7.911
* Rouge2: 3.2189
* Rougel: 6.7856
* Rougelsum: 7.4485
Model description
-----------------
M... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5.6e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\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=... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #summarization #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\\_r... |
summarization | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-base-wikinewssum-portuguese
This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) ... | {"license": "apache-2.0", "tags": ["summarization", "generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "mt5-base-wikinewssum-portuguese", "results": []}]} | airKlizz/mt5-base-wikinewssum-portuguese | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"summarization",
"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 #mt5 #text2text-generation #summarization #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| mt5-base-wikinewssum-portuguese
===============================
This model is a fine-tuned version of google/mt5-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.0428
* Rouge1: 9.4966
* Rouge2: 4.2224
* Rougel: 7.9845
* Rougelsum: 8.8641
Model description
------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5.6e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\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=... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #summarization #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\\_r... |
summarization | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-base-wikinewssum-spanish
This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on ... | {"license": "apache-2.0", "tags": ["summarization", "generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "mt5-base-wikinewssum-spanish", "results": []}]} | airKlizz/mt5-base-wikinewssum-spanish | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"summarization",
"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 #mt5 #text2text-generation #summarization #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| mt5-base-wikinewssum-spanish
============================
This model is a fine-tuned version of google/mt5-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.2394
* Rouge1: 7.9732
* Rouge2: 3.5041
* Rougel: 6.6713
* Rougelsum: 7.5229
Model description
-----------------
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5.6e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\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=... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #summarization #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\\_r... |
summarization | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-small-wikinewssum-test
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on ... | {"license": "apache-2.0", "tags": ["summarization", "generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "mt5-small-wikinewssum-test", "results": []}]} | airKlizz/mt5-small-wikinewssum-test | null | [
"transformers",
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"mt5",
"text2text-generation",
"summarization",
"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 #mt5 #text2text-generation #summarization #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| mt5-small-wikinewssum-test
==========================
This model is a fine-tuned version of google/mt5-small on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.9354
* Rouge1: 6.8433
* Rouge2: 2.5498
* Rougel: 5.6114
* Rougelsum: 6.353
Model description
-----------------
Mo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5.6e-05\n* train\\_batch\\_size: 12\n* eval\\_batch\\_size: 12\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 8",
"### Trai... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #summarization #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\\_r... |
question-answering | transformers |
# bert-base-multilingual-cased
Finetuning `bert-base-multilingual-cased` 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 around 300 `newmm` words). Bench... | {"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\... | airesearch/bert-base-multilingual-cased-finetune-qa | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #question-answering #endpoints_compatible #region-us
|
# bert-base-multilingual-cased
Finetuning 'bert-base-multilingual-cased' 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 around 300 'newmm' words). Bench... | [
"# bert-base-multilingual-cased\n\nFinetuning 'bert-base-multilingual-cased' 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 around 300 'newmm' words).... | [
"TAGS\n#transformers #pytorch #bert #question-answering #endpoints_compatible #region-us \n",
"# bert-base-multilingual-cased\n\nFinetuning 'bert-base-multilingual-cased' with the training set of 'iapp_wiki_qa_squad', 'thaiqa_squad', and 'nsc_qa' (removed examples which have cosine similarity with validation and... |
fill-mask | transformers | # Finetuend `bert-base-multilignual-cased` model on Thai sequence and token classification datasets
<br>
Finetuned XLM Roberta BASE model on Thai sequence and token classification datasets
The script and documentation can be found at [this repository](https://github.com/vistec-AI/thai2transformers).
<br>
## Model d... | {} | airesearch/bert-base-multilingual-cased-finetuned | null | [
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"bert",
"fill-mask",
"arxiv:1810.04805",
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"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1810.04805",
"2101.09635"
] | [] | TAGS
#transformers #bert #fill-mask #arxiv-1810.04805 #arxiv-2101.09635 #autotrain_compatible #endpoints_compatible #region-us
| # Finetuend 'bert-base-multilignual-cased' model on Thai sequence and token classification datasets
<br>
Finetuned XLM Roberta BASE model on Thai sequence and token classification datasets
The script and documentation can be found at this repository.
<br>
## Model description
<br>
We use the pretrained cross-ling... | [
"# Finetuend 'bert-base-multilignual-cased' model on Thai sequence and token classification datasets\n\n<br>\n\nFinetuned XLM Roberta BASE model on Thai sequence and token classification datasets\nThe script and documentation can be found at this repository.\n\n<br>",
"## Model description\n\n<br>\n\nWe use the p... | [
"TAGS\n#transformers #bert #fill-mask #arxiv-1810.04805 #arxiv-2101.09635 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Finetuend 'bert-base-multilignual-cased' model on Thai sequence and token classification datasets\n\n<br>\n\nFinetuned XLM Roberta BASE model on Thai sequence and token classifi... |
fill-mask | transformers |
# WangchanBERTa base model: `wangchanberta-base-att-spm-uncased`
<br>
Pretrained RoBERTa BASE model on assorted Thai texts (78.5 GB).
The script and documentation can be found at [this repository](https://github.com/vistec-AI/thai2transformers).
<br>
## Model description
<br>
The architecture of the pretrained mo... | {"language": "th", "widget": [{"text": "\u0e1c\u0e39\u0e49\u0e43\u0e0a\u0e49\u0e07\u0e32\u0e19\u0e17\u0e48\u0e32\u0e2d\u0e32\u0e01\u0e32\u0e28\u0e22\u0e32\u0e19\u0e19\u0e32\u0e19\u0e32\u0e0a\u0e32\u0e15\u0e34<mask>\u0e21\u0e35\u0e01\u0e27\u0e48\u0e32\u0e2a\u0e32\u0e21\u0e25\u0e49\u0e32\u0e19\u0e04\u0e19<pad>"}]} | airesearch/wangchanberta-base-att-spm-uncased | null | [
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"arxiv:2101.09635",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1907.11692",
"1801.06146",
"1808.06226",
"2101.09635"
] | [
"th"
] | TAGS
#transformers #pytorch #safetensors #camembert #fill-mask #th #arxiv-1907.11692 #arxiv-1801.06146 #arxiv-1808.06226 #arxiv-2101.09635 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# WangchanBERTa base model: 'wangchanberta-base-att-spm-uncased'
<br>
Pretrained RoBERTa BASE model on assorted Thai texts (78.5 GB).
The script and documentation can be found at this repository.
<br>
## Model description
<br>
The architecture of the pretrained model is based on RoBERTa [[Liu et al., 2019]](URL
... | [
"# WangchanBERTa base model: 'wangchanberta-base-att-spm-uncased'\n\n<br>\n\nPretrained RoBERTa BASE model on assorted Thai texts (78.5 GB).\nThe script and documentation can be found at this repository.\n<br>",
"## Model description\n\n<br>\n\nThe architecture of the pretrained model is based on RoBERTa [[Liu et... | [
"TAGS\n#transformers #pytorch #safetensors #camembert #fill-mask #th #arxiv-1907.11692 #arxiv-1801.06146 #arxiv-1808.06226 #arxiv-2101.09635 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# WangchanBERTa base model: 'wangchanberta-base-att-spm-uncased'\n\n<br>\n\nPretrained RoBERTa BASE m... |
question-answering | transformers | # wangchanberta-base-wiki-20210520-spm-finetune-qa
Finetuning `airesearchth/wangchanberta-base-wiki-20210520-spmd` 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... | {"language": "th", "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\... | airesearch/wangchanberta-base-wiki-20210520-spm-finetune-qa | null | [
"transformers",
"pytorch",
"camembert",
"question-answering",
"th",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"th"
] | TAGS
#transformers #pytorch #camembert #question-answering #th #endpoints_compatible #region-us
| # wangchanberta-base-wiki-20210520-spm-finetune-qa
Finetuning 'airesearchth/wangchanberta-base-wiki-20210520-spmd' 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... | [
"# wangchanberta-base-wiki-20210520-spm-finetune-qa\n\nFinetuning 'airesearchth/wangchanberta-base-wiki-20210520-spmd' 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... | [
"TAGS\n#transformers #pytorch #camembert #question-answering #th #endpoints_compatible #region-us \n",
"# wangchanberta-base-wiki-20210520-spm-finetune-qa\n\nFinetuning 'airesearchth/wangchanberta-base-wiki-20210520-spmd' with the training set of 'iapp_wiki_qa_squad', 'thaiqa_squad', and 'nsc_qa' (removed example... |
fill-mask | transformers |
# WangchanBERTa base model: `wangchanberta-base-wiki-newmm`
<br>
Pretrained RoBERTa BASE model on Thai Wikipedia corpus.
The script and documentation can be found at [this reposiryory](https://github.com/vistec-AI/thai2transformers).
<br>
## Model description
<br>
The architecture of the pretrained model is based... | {"language": "th"} | airesearch/wangchanberta-base-wiki-newmm | null | [
"transformers",
"pytorch",
"jax",
"roberta",
"fill-mask",
"th",
"arxiv:1907.11692",
"arxiv:2101.09635",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1907.11692",
"2101.09635"
] | [
"th"
] | TAGS
#transformers #pytorch #jax #roberta #fill-mask #th #arxiv-1907.11692 #arxiv-2101.09635 #autotrain_compatible #endpoints_compatible #region-us
|
# WangchanBERTa base model: 'wangchanberta-base-wiki-newmm'
<br>
Pretrained RoBERTa BASE model on Thai Wikipedia corpus.
The script and documentation can be found at this reposiryory.
<br>
## Model description
<br>
The architecture of the pretrained model is based on RoBERTa [[Liu et al., 2019]](URL
<br>
## ... | [
"# WangchanBERTa base model: 'wangchanberta-base-wiki-newmm'\n\n<br>\n\nPretrained RoBERTa BASE model on Thai Wikipedia corpus.\nThe script and documentation can be found at this reposiryory.\n<br>",
"## Model description\n\n<br>\n\nThe architecture of the pretrained model is based on RoBERTa [[Liu et al., 2019]]... | [
"TAGS\n#transformers #pytorch #jax #roberta #fill-mask #th #arxiv-1907.11692 #arxiv-2101.09635 #autotrain_compatible #endpoints_compatible #region-us \n",
"# WangchanBERTa base model: 'wangchanberta-base-wiki-newmm'\n\n<br>\n\nPretrained RoBERTa BASE model on Thai Wikipedia corpus.\nThe script and documentation c... |
fill-mask | transformers |
# WangchanBERTa base model: `wangchanberta-base-wiki-sefr`
<br>
Pretrained RoBERTa BASE model on Thai Wikipedia corpus.
The script and documentation can be found at [this reposiryory](https://github.com/vistec-AI/thai2transformers).
<br>
## Model description
<br>
The architecture of the pretrained model is based ... | {"language": "th"} | airesearch/wangchanberta-base-wiki-sefr | null | [
"transformers",
"pytorch",
"jax",
"roberta",
"fill-mask",
"th",
"arxiv:1907.11692",
"arxiv:2101.09635",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1907.11692",
"2101.09635"
] | [
"th"
] | TAGS
#transformers #pytorch #jax #roberta #fill-mask #th #arxiv-1907.11692 #arxiv-2101.09635 #autotrain_compatible #endpoints_compatible #region-us
|
# WangchanBERTa base model: 'wangchanberta-base-wiki-sefr'
<br>
Pretrained RoBERTa BASE model on Thai Wikipedia corpus.
The script and documentation can be found at this reposiryory.
<br>
## Model description
<br>
The architecture of the pretrained model is based on RoBERTa [[Liu et al., 2019]](URL
<br>
## I... | [
"# WangchanBERTa base model: 'wangchanberta-base-wiki-sefr'\n\n<br>\n\nPretrained RoBERTa BASE model on Thai Wikipedia corpus.\nThe script and documentation can be found at this reposiryory.\n<br>",
"## Model description\n\n<br>\n\nThe architecture of the pretrained model is based on RoBERTa [[Liu et al., 2019]](... | [
"TAGS\n#transformers #pytorch #jax #roberta #fill-mask #th #arxiv-1907.11692 #arxiv-2101.09635 #autotrain_compatible #endpoints_compatible #region-us \n",
"# WangchanBERTa base model: 'wangchanberta-base-wiki-sefr'\n\n<br>\n\nPretrained RoBERTa BASE model on Thai Wikipedia corpus.\nThe script and documentation ca... |
fill-mask | transformers |
# WangchanBERTa base model: `wangchanberta-base-wiki-spm`
<br>
Pretrained RoBERTa BASE model on Thai Wikipedia corpus.
The script and documentation can be found at [this reposiryory](https://github.com/vistec-AI/thai2transformers).
<br>
## Model description
<br>
The architecture of the pretrained model is based o... | {"language": "th"} | airesearch/wangchanberta-base-wiki-spm | null | [
"transformers",
"pytorch",
"jax",
"roberta",
"fill-mask",
"th",
"arxiv:1907.11692",
"arxiv:2101.09635",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1907.11692",
"2101.09635"
] | [
"th"
] | TAGS
#transformers #pytorch #jax #roberta #fill-mask #th #arxiv-1907.11692 #arxiv-2101.09635 #autotrain_compatible #endpoints_compatible #region-us
|
# WangchanBERTa base model: 'wangchanberta-base-wiki-spm'
<br>
Pretrained RoBERTa BASE model on Thai Wikipedia corpus.
The script and documentation can be found at this reposiryory.
<br>
## Model description
<br>
The architecture of the pretrained model is based on RoBERTa [[Liu et al., 2019]](URL
<br>
## In... | [
"# WangchanBERTa base model: 'wangchanberta-base-wiki-spm'\n\n<br>\n\nPretrained RoBERTa BASE model on Thai Wikipedia corpus.\nThe script and documentation can be found at this reposiryory.\n<br>",
"## Model description\n\n<br>\n\nThe architecture of the pretrained model is based on RoBERTa [[Liu et al., 2019]](U... | [
"TAGS\n#transformers #pytorch #jax #roberta #fill-mask #th #arxiv-1907.11692 #arxiv-2101.09635 #autotrain_compatible #endpoints_compatible #region-us \n",
"# WangchanBERTa base model: 'wangchanberta-base-wiki-spm'\n\n<br>\n\nPretrained RoBERTa BASE model on Thai Wikipedia corpus.\nThe script and documentation can... |
fill-mask | transformers |
# WangchanBERTa base model: `wangchanberta-base-wiki-syllable`
<br>
Pretrained RoBERTa BASE model on Thai Wikipedia corpus.
The script and documentation can be found at [this reposiryory](https://github.com/vistec-AI/thai2transformers).
<br>
## Model description
<br>
The architecture of the pretrained model is ba... | {"language": "th"} | airesearch/wangchanberta-base-wiki-syllable | null | [
"transformers",
"pytorch",
"jax",
"roberta",
"fill-mask",
"th",
"arxiv:1907.11692",
"arxiv:2101.09635",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1907.11692",
"2101.09635"
] | [
"th"
] | TAGS
#transformers #pytorch #jax #roberta #fill-mask #th #arxiv-1907.11692 #arxiv-2101.09635 #autotrain_compatible #endpoints_compatible #region-us
|
# WangchanBERTa base model: 'wangchanberta-base-wiki-syllable'
<br>
Pretrained RoBERTa BASE model on Thai Wikipedia corpus.
The script and documentation can be found at this reposiryory.
<br>
## Model description
<br>
The architecture of the pretrained model is based on RoBERTa [[Liu et al., 2019]](URL
<br>
... | [
"# WangchanBERTa base model: 'wangchanberta-base-wiki-syllable'\n\n<br>\n\nPretrained RoBERTa BASE model on Thai Wikipedia corpus.\nThe script and documentation can be found at this reposiryory.\n<br>",
"## Model description\n\n<br>\n\nThe architecture of the pretrained model is based on RoBERTa [[Liu et al., 201... | [
"TAGS\n#transformers #pytorch #jax #roberta #fill-mask #th #arxiv-1907.11692 #arxiv-2101.09635 #autotrain_compatible #endpoints_compatible #region-us \n",
"# WangchanBERTa base model: 'wangchanberta-base-wiki-syllable'\n\n<br>\n\nPretrained RoBERTa BASE model on Thai Wikipedia corpus.\nThe script and documentatio... |
automatic-speech-recognition | transformers |
# `wav2vec2-large-xlsr-53-th`
Finetuning `wav2vec2-large-xlsr-53` on Thai [Common Voice 7.0](https://commonvoice.mozilla.org/en/datasets)
[Read more on our blog](https://medium.com/airesearch-in-th/airesearch-in-th-3c1019a99cd)
We finetune [wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-... | {"language": "th", "license": "cc-by-sa-4.0", "tags": ["audio", "automatic-speech-recognition", "hf-asr-leaderboard", "robust-speech-event", "speech", "xlsr-fine-tuning"], "datasets": ["common_voice"], "model-index": [{"name": "XLS-R-53 - Thai", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Aut... | airesearch/wav2vec2-large-xlsr-53-th | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"hf-asr-leaderboard",
"robust-speech-event",
"speech",
"xlsr-fine-tuning",
"th",
"dataset:common_voice",
"doi:10.57967/hf/0404",
"license:cc-by-sa-4.0",
"model-index",
"endpoints_compatible",
"has_space",
... | null | 2022-03-02T23:29:05+00:00 | [] | [
"th"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #hf-asr-leaderboard #robust-speech-event #speech #xlsr-fine-tuning #th #dataset-common_voice #doi-10.57967/hf/0404 #license-cc-by-sa-4.0 #model-index #endpoints_compatible #has_space #region-us
| 'wav2vec2-large-xlsr-53-th'
===========================
Finetuning 'wav2vec2-large-xlsr-53' on Thai Common Voice 7.0
Read more on our blog
We finetune wav2vec2-large-xlsr-53 based on Fine-tuning Wav2Vec2 for English ASR using Thai examples of Common Voice Corpus 7.0. The notebooks and scripts can be found in vist... | [
"### Eval results on Common Voice 7 \"test\":\n\n\n\nUsage\n-----\n\n\nDatasets\n--------\n\n\nCommon Voice Corpus 7.0](URL contains 133 validated hours of Thai (255 total hours) at 5GB. We pre-tokenize with 'pythainlp.tokenize.word\\_tokenize'. We preprocess the dataset using cleaning rules described in 'notebooks... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #hf-asr-leaderboard #robust-speech-event #speech #xlsr-fine-tuning #th #dataset-common_voice #doi-10.57967/hf/0404 #license-cc-by-sa-4.0 #model-index #endpoints_compatible #has_space #region-us \n",
"### Eval results on Common Voice 7 \"... |
question-answering | transformers | # xlm-roberta-base-finetune-qa
Finetuning `xlm-roberta-base` 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 around 300 `newmm` words). Benchmarks shared 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\... | airesearch/xlm-roberta-base-finetune-qa | null | [
"transformers",
"pytorch",
"xlm-roberta",
"question-answering",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #question-answering #endpoints_compatible #region-us
| # xlm-roberta-base-finetune-qa
Finetuning 'xlm-roberta-base' 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 around 300 'newmm' words). Benchmarks shared o... | [
"# xlm-roberta-base-finetune-qa\n\nFinetuning 'xlm-roberta-base' 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 around 300 'newmm' words). Benchmarks s... | [
"TAGS\n#transformers #pytorch #xlm-roberta #question-answering #endpoints_compatible #region-us \n",
"# xlm-roberta-base-finetune-qa\n\nFinetuning 'xlm-roberta-base' with the training set of 'iapp_wiki_qa_squad', 'thaiqa_squad', and 'nsc_qa' (removed examples which have cosine similarity with validation and test ... |
fill-mask | transformers | # Finetuend `xlm-roberta-base` model on Thai sequence and token classification datasets
<br>
Finetuned XLM Roberta BASE model on Thai sequence and token classification datasets
The script and documentation can be found at [this repository](https://github.com/vistec-AI/thai2transformers).
<br>
## Model description
... | {} | airesearch/xlm-roberta-base-finetuned | null | [
"transformers",
"xlm-roberta",
"fill-mask",
"arxiv:1911.02116",
"arxiv:2101.09635",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1911.02116",
"2101.09635"
] | [] | TAGS
#transformers #xlm-roberta #fill-mask #arxiv-1911.02116 #arxiv-2101.09635 #autotrain_compatible #endpoints_compatible #region-us
| # Finetuend 'xlm-roberta-base' model on Thai sequence and token classification datasets
<br>
Finetuned XLM Roberta BASE model on Thai sequence and token classification datasets
The script and documentation can be found at this repository.
<br>
## Model description
<br>
We use the pretrained cross-lingual RoBERTa ... | [
"# Finetuend 'xlm-roberta-base' model on Thai sequence and token classification datasets\n\n<br>\n\nFinetuned XLM Roberta BASE model on Thai sequence and token classification datasets\nThe script and documentation can be found at this repository.\n\n<br>",
"## Model description\n\n<br>\n\nWe use the pretrained cr... | [
"TAGS\n#transformers #xlm-roberta #fill-mask #arxiv-1911.02116 #arxiv-2101.09635 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Finetuend 'xlm-roberta-base' model on Thai sequence and token classification datasets\n\n<br>\n\nFinetuned XLM Roberta BASE model on Thai sequence and token classificatio... |
text-generation | transformers |
# Michael Scott DialoGPT Model | {"tags": ["conversational"]} | aishanisingh/DiagloGPT-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"
] |
text-generation | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | aishanisingh/DialoGPT-small-harrypotter | 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"
] |
null | null | pip install vaderSentiment
from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
analyser = SentimentIntensityAnalyzer()
analyser.polarity_scores("I hate watching movies")
import nltk
from nltk.tokenize import word_tokenize, RegexpTokenizer
from nltk.sentiment.vader import SentimentIntensityAnalyzer... | {} | aishoo1612/VADER-With-heatmaps | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| pip install vaderSentiment
from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
analyser = SentimentIntensityAnalyzer()
analyser.polarity_scores("I hate watching movies")
import nltk
from nltk.tokenize import word_tokenize, RegexpTokenizer
from URL import SentimentIntensityAnalyzer
nltk.download('... | [] | [
"TAGS\n#region-us \n"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-hi-colab_new
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-hi-colab_new", "results": []}]} | ajaiswal1008/wav2vec2-large-xls-r-300m-hi-colab_new | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-large-xls-r-300m-hi-colab_new
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training proc... | [
"# wav2vec2-large-xls-r-300m-hi-colab_new\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information ne... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-large-xls-r-300m-hi-colab_new\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_v... |
image-classification | transformers |
# greens
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | ajanco/greens | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# greens
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images
#### cucumber
!cucumber
#### green beans
!green beans
#### okra
!okra
#### pickle
!pickle
#### zucinni
!zucinn... | [
"# greens\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### cucumber\n\n!cucumber",
"#### green beans\n\n!green beans",
"#### okra\n\n!okra",
"#### pic... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# greens\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with t... |
fill-mask | transformers |
This **cased model** was pretrained from scratch using a custom vocabulary on the following corpora
- Pubmed
- Clinical trials corpus
- and a small subset of Bookcorpus
The pretrained model was used to do NER **as is, with no fine-tuning**. The approach is described [in this post](https://ajitrajasekharan.github.io... | {"language": [{}], "license": "mit", "tags": [{}, "exbert"], "widget": [{"text": "Lou Gehrig who works for XCorp and lives in New York suffers from [MASK]", "example_title": "Test for entity type: Disease"}, {"text": "Overexpression of [MASK] occurs across a wide range of cancers", "example_title": "Test for entity typ... | ajitrajasekharan/biomedical | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
|
This cased model was pretrained from scratch using a custom vocabulary on the following corpora
- Pubmed
- Clinical trials corpus
- and a small subset of Bookcorpus
The pretrained model was used to do NER as is, with no fine-tuning. The approach is described in this post. Towards Data Science review
App in Space... | [
"### Ensemble model performance\n\n <img src=\"URL width=\"600\">",
"### Additional notes\n\n- The model predictions on the right do not include [CLS] predictions. Hosted inference API only returns the masked position predictions. In practice, the [CLS] predictions are just as useful as the model predictions for ... | [
"TAGS\n#transformers #pytorch #bert #fill-mask #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Ensemble model performance\n\n <img src=\"URL width=\"600\">",
"### Additional notes\n\n- The model predictions on the right do not include [CLS] predictions. Hosted inference ... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-finetuned-cola
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-unca... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "bert-base-uncased-finetuned-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "... | ajrae/bert-base-uncased-finetuned-cola | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased-finetuned-cola
================================
This model is a fine-tuned version of bert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8385
* Matthews Correlation: 0.5865
Model description
-----------------
More information needed
Inte... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rat... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-finetuned-mrpc
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-unca... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "bert-base-uncased-finetuned-mrpc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "mrpc"}... | ajrae/bert-base-uncased-finetuned-mrpc | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased-finetuned-mrpc
================================
This model is a fine-tuned version of bert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4520
* Accuracy: 0.8578
* F1: 0.9003
Model description
-----------------
More information needed
Int... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rat... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xlsr-53-Total
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/face... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-large-xlsr-53-Total", "results": []}]} | akadriu/wav2vec2-large-xlsr-53-Total | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xlsr-53-Total
============================
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2814
* Wer: 0.2260
Model description
-----------------
More information needed
Intended uses &... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\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=... | [
"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: 8... |
text-generation | transformers |
## how to use
```python
from transformers import pipeline, set_seed
path = "akahana/gpt2-indonesia"
generator = pipeline('text-generation',
model=path)
set_seed(42)
kalimat = "dahulu kala ada sebuah"
preds = generator(kalimat,
max_length=64,
num_return_sequ... | {"language": "id", "widget": [{"text": "dahulu kala ada sebuah"}]} | akahana/gpt2-indonesia | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"gpt2",
"text-generation",
"id",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"id"
] | TAGS
#transformers #pytorch #tf #safetensors #gpt2 #text-generation #id #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
## how to use
| [
"## how to use"
] | [
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"## how to use"
] |
text-classification | transformers |
## how to use
```python
from transformers import pipeline, set_seed
path = "akahana/indonesia-emotion-roberta"
emotion = pipeline('text-classification',
model=path,device=0)
set_seed(42)
kalimat = "dia orang yang baik ya bunds."
preds = emotion(kalimat)
preds
[{'label': 'BAHAGI... | {"language": "id", "widget": [{"text": "dia orang yang baik ya bunds."}]} | akahana/indonesia-emotion-roberta | null | [
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"tensorboard",
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"id",
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"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"id"
] | TAGS
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|
## how to use
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"## how to use"
] |
text-classification | transformers |
## how to use
```python
from transformers import pipeline, set_seed
path = "akahana/indonesia-sentiment-roberta"
emotion = pipeline('text-classification',
model=path,device=0)
set_seed(42)
kalimat = "dia orang yang baik ya bunds."
preds = emotion(kalimat)
preds
``` | {"language": "id", "widget": [{"text": "dia orang yang baik ya bunds."}]} | akahana/indonesia-sentiment-roberta | null | [
"transformers",
"pytorch",
"tensorboard",
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"text-classification",
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"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
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] | TAGS
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|
## how to use
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] |
feature-extraction | transformers |
# Indonesian RoBERTa Base
## How to Use
### As Masked Language Model
```python
from transformers import pipeline
pretrained_name = "akahana/roberta-base-indonesia"
fill_mask = pipeline(
"fill-mask",
model=pretrained_name,
tokenizer=pretrained_name
)
fill_mask("Gajah <mask> sedang makan di kebun binatan... | {"language": "id", "license": "mit", "tags": ["roberta-base-indonesia"], "datasets": ["wikipedia"], "widget": [{"text": "Gajah <mask> sedang makan di kebun binatang."}]} | akahana/roberta-base-indonesia | null | [
"transformers",
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"tf",
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"roberta",
"feature-extraction",
"roberta-base-indonesia",
"id",
"dataset:wikipedia",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"id"
] | TAGS
#transformers #pytorch #tf #safetensors #roberta #feature-extraction #roberta-base-indonesia #id #dataset-wikipedia #license-mit #endpoints_compatible #region-us
|
# Indonesian RoBERTa Base
## How to Use
### As Masked Language Model
### Feature Extraction in PyTorch
| [
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"## How to Use",
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"## How to Use",
"### As Masked Language Model",
"### Feature Extraction in PyTorch"
] |
feature-extraction | transformers |
# Indonesian tiny-RoBERTa
## How to Use
### As Masked Language Model
```python
from transformers import pipeline
pretrained_name = "akahana/tiny-roberta-indonesia"
fill_mask = pipeline(
"fill-mask",
model=pretrained_name,
tokenizer=pretrained_name
)
fill_mask("ikiryo adalah <mask> hantu dalam mitologi ... | {"language": "id", "license": "mit", "tags": ["tiny-roberta-indonesia"], "datasets": ["wikipedia"], "widget": [{"text": "ikiryo adalah <mask> hantu dalam mitologi jepang."}]} | akahana/tiny-roberta-indonesia | null | [
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"roberta",
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"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"id"
] | TAGS
#transformers #pytorch #tf #safetensors #roberta #feature-extraction #tiny-roberta-indonesia #id #dataset-wikipedia #license-mit #endpoints_compatible #region-us
|
# Indonesian tiny-RoBERTa
## How to Use
### As Masked Language Model
### Feature Extraction in PyTorch
| [
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"## How to Use",
"### As Masked Language Model",
"### Feature Extraction in PyTorch"
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"## How to Use",
"### As Masked Language Model",
"### Feature Extraction in PyTorch"
] |
image-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-base-cats-vs-dogs
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vi... | {"license": "apache-2.0", "tags": ["image-classification", "generated_from_trainer"], "datasets": ["cats_vs_dogs"], "metrics": ["accuracy"], "base_model": "google/vit-base-patch16-224-in21k", "model-index": [{"name": "vit-base-cats-vs-dogs", "results": [{"task": {"type": "image-classification", "name": "Image Classific... | akahana/vit-base-cats-vs-dogs | null | [
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"pytorch",
"tensorboard",
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"image-classification",
"generated_from_trainer",
"dataset:cats_vs_dogs",
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"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"reg... | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
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| vit-base-cats-vs-dogs
=====================
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the cats\_vs\_dogs dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0369
* Accuracy: 0.9883
how to use
----------
Model description
-----------------
More informati... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 1337\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1.0",
"### Tra... | [
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"### Training hyperparameters\n\n\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-tamil-colab-final
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://hugging... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-tamil-colab-final", "results": []}]} | akashsivanandan/wav2vec2-large-xls-r-300m-tamil-colab-final | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xls-r-300m-tamil-colab-final
===========================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7539
* Wer: 0.6135
Model description
-----------------
More ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* t... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-tamil-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.c... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-tamil-colab", "results": []}]} | akashsivanandan/wav2vec2-large-xls-r-300m-tamil-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xls-r-300m-tamil-colab
=====================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8072
* Wer: 0.6531
Model description
-----------------
More information ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* t... |
text-generation | transformers |
# Kaiser DialoGPT Model | {"tags": ["conversational"]} | akaushik1/DialoGPT-small-kaiser | 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
|
# Kaiser DialoGPT Model | [
"# Kaiser DialoGPT Model"
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"# Kaiser DialoGPT Model"
] |
token-classification | transformers | # Hungarian Named Entity Recognition (NER) Model
This model is the fine-tuned model of "SZTAKI-HLT/hubert-base-cc"
using the famous WikiANN dataset presented
in the "Cross-lingual Name Tagging and Linking for 282 Languages" [paper](https://aclanthology.org/P17-1178.pdf).
# Fine-tuning parameters:
```
task = "ner"
mod... | {"language": "hu", "widget": [{"text": "Karik\u00f3 Katalin megkapja Szeged d\u00edszpolg\u00e1rs\u00e1g\u00e1t."}]} | akdeniz27/bert-base-hungarian-cased-ner | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"token-classification",
"hu",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"hu"
] | TAGS
#transformers #pytorch #safetensors #bert #token-classification #hu #autotrain_compatible #endpoints_compatible #region-us
| # Hungarian Named Entity Recognition (NER) Model
This model is the fine-tuned model of "SZTAKI-HLT/hubert-base-cc"
using the famous WikiANN dataset presented
in the "Cross-lingual Name Tagging and Linking for 282 Languages" paper.
# Fine-tuning parameters:
# How to use:
Pls refer "URL for entity grouping with aggr... | [
"# Hungarian Named Entity Recognition (NER) Model\nThis model is the fine-tuned model of \"SZTAKI-HLT/hubert-base-cc\" \nusing the famous WikiANN dataset presented\nin the \"Cross-lingual Name Tagging and Linking for 282 Languages\" paper.",
"# Fine-tuning parameters:",
"# How to use: \n\nPls refer \"URL for en... | [
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token-classification | transformers |
# Turkish Named Entity Recognition (NER) Model
This model is the fine-tuned model of "dbmdz/bert-base-turkish-cased"
using a reviewed version of well known Turkish NER dataset
(https://github.com/stefan-it/turkish-bert/files/4558187/nerdata.txt).
# Fine-tuning parameters:
```
task = "ner"
model_checkpoint = "dbmdz... | {"language": "tr", "widget": [{"text": "Mustafa Kemal Atat\u00fcrk 19 May\u0131s 1919'da Samsun'a \u00e7\u0131kt\u0131."}]} | akdeniz27/bert-base-turkish-cased-ner | null | [
"transformers",
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"tr",
"doi:10.57967/hf/0949",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #onnx #safetensors #bert #token-classification #tr #doi-10.57967/hf/0949 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Turkish Named Entity Recognition (NER) Model
This model is the fine-tuned model of "dbmdz/bert-base-turkish-cased"
using a reviewed version of well known Turkish NER dataset
(URL
# Fine-tuning parameters:
# How to use:
Pls refer "URL for entity grouping with aggregation_strategy parameter.
# Reference test ... | [
"# Turkish Named Entity Recognition (NER) Model\n\nThis model is the fine-tuned model of \"dbmdz/bert-base-turkish-cased\" \nusing a reviewed version of well known Turkish NER dataset \n(URL",
"# Fine-tuning parameters:",
"# How to use: \n\nPls refer \"URL for entity grouping with aggregation_strategy parameter... | [
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"# Turkish Named Entity Recognition (NER) Model\n\nThis model is the fine-tuned model of \"dbmdz/bert-base-turkish-cased\" \nusing a reviewe... |
text-classification | transformers |
# Turkish Text Classification for Complaints Data Set
This model is a fine-tune model of https://github.com/stefan-it/turkish-bert by using text classification data with 9 categories as follows:
id_to_category = {0: 'KONFORSUZLUK', 1: 'TARİFE İHLALİ', 2: 'DURAKTA DURMAMA', 3: 'ŞOFÖR-PERSONEL ŞİKAYETİ',
... | {"language": "tr"} | akdeniz27/bert-turkish-text-classification | null | [
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"jax",
"safetensors",
"bert",
"text-classification",
"tr",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #jax #safetensors #bert #text-classification #tr #autotrain_compatible #endpoints_compatible #region-us
|
# Turkish Text Classification for Complaints Data Set
This model is a fine-tune model of URL by using text classification data with 9 categories as follows:
id_to_category = {0: 'KONFORSUZLUK', 1: 'TARİFE İHLALİ', 2: 'DURAKTA DURMAMA', 3: 'ŞOFÖR-PERSONEL ŞİKAYETİ',
4: 'YENİ GÜZERGAH/HAT/DURAK İSTE... | [
"# Turkish Text Classification for Complaints Data Set\n\nThis model is a fine-tune model of URL by using text classification data with 9 categories as follows:\n\nid_to_category = {0: 'KONFORSUZLUK', 1: 'TARİFE İHLALİ', 2: 'DURAKTA DURMAMA', 3: 'ŞOFÖR-PERSONEL ŞİKAYETİ', \n 4: 'YENİ GÜZERGAH/HAT/D... | [
"TAGS\n#transformers #pytorch #jax #safetensors #bert #text-classification #tr #autotrain_compatible #endpoints_compatible #region-us \n",
"# Turkish Text Classification for Complaints Data Set\n\nThis model is a fine-tune model of URL by using text classification data with 9 categories as follows:\n\nid_to_categ... |
token-classification | transformers | # Turkish Named Entity Recognition (NER) Model
This model is the fine-tuned model of dbmdz/convbert-base-turkish-cased (ConvBERTurk)
using a reviewed version of well known Turkish NER dataset
(https://github.com/stefan-it/turkish-bert/files/4558187/nerdata.txt).
The ConvBERT architecture is presented in the ["ConvBE... | {"language": "tr", "widget": [{"text": "Almanya, koronavir\u00fcs a\u015f\u0131s\u0131n\u0131 geli\u015ftiren Dr. \u00d6zlem T\u00fcreci ve e\u015fi Prof. Dr. U\u011fur \u015eahin'e liyakat ni\u015fan\u0131 verdi"}]} | akdeniz27/convbert-base-turkish-cased-ner | null | [
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"tr",
"arxiv:2008.02496",
"doi:10.57967/hf/0015",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2008.02496"
] | [
"tr"
] | TAGS
#transformers #pytorch #onnx #safetensors #convbert #token-classification #tr #arxiv-2008.02496 #doi-10.57967/hf/0015 #autotrain_compatible #endpoints_compatible #has_space #region-us
| # Turkish Named Entity Recognition (NER) Model
This model is the fine-tuned model of dbmdz/convbert-base-turkish-cased (ConvBERTurk)
using a reviewed version of well known Turkish NER dataset
(URL
The ConvBERT architecture is presented in the "ConvBERT: Improving BERT with Span-based Dynamic Convolution" paper.
# F... | [
"# Turkish Named Entity Recognition (NER) Model\nThis model is the fine-tuned model of dbmdz/convbert-base-turkish-cased (ConvBERTurk)\nusing a reviewed version of well known Turkish NER dataset\n \n(URL\n\nThe ConvBERT architecture is presented in the \"ConvBERT: Improving BERT with Span-based Dynamic Convolution\... | [
"TAGS\n#transformers #pytorch #onnx #safetensors #convbert #token-classification #tr #arxiv-2008.02496 #doi-10.57967/hf/0015 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Turkish Named Entity Recognition (NER) Model\nThis model is the fine-tuned model of dbmdz/convbert-base-turkish-cas... |
question-answering | transformers | # DeBERTa v2 XLarge Model fine-tuned with CUAD dataset
This model is the fine-tuned version of "DeBERTa v2 XLarge"
using CUAD dataset https://huggingface.co/datasets/cuad
Link for model checkpoint: https://github.com/TheAtticusProject/cuad
For the use of the model with CUAD: https://github.com/marshmellow77/cuad-dem... | {"language": "en", "datasets": ["cuad"]} | akdeniz27/deberta-v2-xlarge-cuad | null | [
"transformers",
"pytorch",
"safetensors",
"deberta-v2",
"question-answering",
"en",
"dataset:cuad",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #deberta-v2 #question-answering #en #dataset-cuad #endpoints_compatible #has_space #region-us
| # DeBERTa v2 XLarge Model fine-tuned with CUAD dataset
This model is the fine-tuned version of "DeBERTa v2 XLarge"
using CUAD dataset URL
Link for model checkpoint: URL
For the use of the model with CUAD: URL
and URL | [
"# DeBERTa v2 XLarge Model fine-tuned with CUAD dataset\nThis model is the fine-tuned version of \"DeBERTa v2 XLarge\" \nusing CUAD dataset URL\n\nLink for model checkpoint: URL\n\nFor the use of the model with CUAD: URL\nand URL"
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token-classification | transformers | # Turkish Named Entity Recognition (NER) Model
This model is the fine-tuned version of "microsoft/mDeBERTa-v3-base"
(a multilingual version of DeBERTa V3)
using a reviewed version of well known Turkish NER dataset
(https://github.com/stefan-it/turkish-bert/files/4558187/nerdata.txt).
# Fine-tuning parameters:
```
tas... | {"language": "tr", "widget": [{"text": "Mustafa Kemal Atat\u00fcrk 19 May\u0131s 1919'da Samsun'a \u00e7\u0131kt\u0131."}]} | akdeniz27/mDeBERTa-v3-base-turkish-ner | null | [
"transformers",
"pytorch",
"safetensors",
"deberta-v2",
"token-classification",
"tr",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #safetensors #deberta-v2 #token-classification #tr #autotrain_compatible #endpoints_compatible #region-us
| # Turkish Named Entity Recognition (NER) Model
This model is the fine-tuned version of "microsoft/mDeBERTa-v3-base"
(a multilingual version of DeBERTa V3)
using a reviewed version of well known Turkish NER dataset
(URL
# Fine-tuning parameters:
# How to use:
Pls refer "URL for entity grouping with aggregation_stra... | [
"# Turkish Named Entity Recognition (NER) Model\nThis model is the fine-tuned version of \"microsoft/mDeBERTa-v3-base\"\n(a multilingual version of DeBERTa V3) \nusing a reviewed version of well known Turkish NER dataset \n(URL",
"# Fine-tuning parameters:",
"# How to use: \n\nPls refer \"URL for entity groupin... | [
"TAGS\n#transformers #pytorch #safetensors #deberta-v2 #token-classification #tr #autotrain_compatible #endpoints_compatible #region-us \n",
"# Turkish Named Entity Recognition (NER) Model\nThis model is the fine-tuned version of \"microsoft/mDeBERTa-v3-base\"\n(a multilingual version of DeBERTa V3) \nusing a rev... |
token-classification | transformers | # Albanian Named Entity Recognition (NER) Model
This model is the fine-tuned model of "bert-base-multilingual-cased"
using the famous WikiANN dataset presented
in the "Cross-lingual Name Tagging and Linking for 282 Languages" [paper](https://aclanthology.org/P17-1178.pdf).
# Fine-tuning parameters:
```
task = "ner"
mo... | {"language": "sq", "widget": [{"text": "Varianti AY.4.2 \u00ebsht\u00eb m\u00eb i leht\u00eb p\u00ebr t'u transmetuar, thot\u00eb Francois Balu, drejtor i Institutit t\u00eb Gjenetik\u00ebs n\u00eb Lond\u00ebr."}]} | akdeniz27/mbert-base-albanian-cased-ner | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"token-classification",
"sq",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"sq"
] | TAGS
#transformers #pytorch #safetensors #bert #token-classification #sq #autotrain_compatible #endpoints_compatible #region-us
| # Albanian Named Entity Recognition (NER) Model
This model is the fine-tuned model of "bert-base-multilingual-cased"
using the famous WikiANN dataset presented
in the "Cross-lingual Name Tagging and Linking for 282 Languages" paper.
# Fine-tuning parameters:
# How to use:
Pls refer "URL for entity grouping with agg... | [
"# Albanian Named Entity Recognition (NER) Model\nThis model is the fine-tuned model of \"bert-base-multilingual-cased\" \nusing the famous WikiANN dataset presented\nin the \"Cross-lingual Name Tagging and Linking for 282 Languages\" paper.",
"# Fine-tuning parameters:",
"# How to use: \n\nPls refer \"URL for ... | [
"TAGS\n#transformers #pytorch #safetensors #bert #token-classification #sq #autotrain_compatible #endpoints_compatible #region-us \n",
"# Albanian Named Entity Recognition (NER) Model\nThis model is the fine-tuned model of \"bert-base-multilingual-cased\" \nusing the famous WikiANN dataset presented\nin the \"Cro... |
question-answering | transformers |
# RoBERTa Base Model fine-tuned with CUAD dataset
This model is the fine-tuned version of "RoBERTa Base"
using CUAD dataset https://huggingface.co/datasets/cuad
Link for model checkpoint: https://github.com/TheAtticusProject/cuad
For the use of the model with CUAD: https://github.com/marshmellow77/cuad-demo
and htt... | {"language": "en", "datasets": ["cuad"]} | akdeniz27/roberta-base-cuad | null | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"question-answering",
"en",
"dataset:cuad",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #roberta #question-answering #en #dataset-cuad #endpoints_compatible #has_space #region-us
|
# RoBERTa Base Model fine-tuned with CUAD dataset
This model is the fine-tuned version of "RoBERTa Base"
using CUAD dataset URL
Link for model checkpoint: URL
For the use of the model with CUAD: URL
and URL | [
"# RoBERTa Base Model fine-tuned with CUAD dataset\nThis model is the fine-tuned version of \"RoBERTa Base\" \nusing CUAD dataset URL\n\nLink for model checkpoint: URL\n\nFor the use of the model with CUAD: URL\nand URL"
] | [
"TAGS\n#transformers #pytorch #safetensors #roberta #question-answering #en #dataset-cuad #endpoints_compatible #has_space #region-us \n",
"# RoBERTa Base Model fine-tuned with CUAD dataset\nThis model is the fine-tuned version of \"RoBERTa Base\" \nusing CUAD dataset URL\n\nLink for model checkpoint: URL\n\nFor ... |
question-answering | transformers |
# Model Card for RoBERTa Large Model fine-tuned with CUAD dataset
This model is the fine-tuned version of "RoBERTa Large" using CUAD dataset
# Model Details
## Model Description
The [Contract Understanding Atticus Dataset (CUAD)](https://www.atticusprojectai.org/cuad), pronounced "kwad", a dataset for ... | {"language": "en", "datasets": ["cuad"]} | akdeniz27/roberta-large-cuad | null | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"question-answering",
"en",
"dataset:cuad",
"arxiv:2103.06268",
"arxiv:1910.09700",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2103.06268",
"1910.09700"
] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #roberta #question-answering #en #dataset-cuad #arxiv-2103.06268 #arxiv-1910.09700 #endpoints_compatible #has_space #region-us
|
# Model Card for RoBERTa Large Model fine-tuned with CUAD dataset
This model is the fine-tuned version of "RoBERTa Large" using CUAD dataset
# Model Details
## Model Description
The Contract Understanding Atticus Dataset (CUAD), pronounced "kwad", a dataset for legal contract review curated by the Atti... | [
"# Model Card for RoBERTa Large Model fine-tuned with CUAD dataset\n \nThis model is the fine-tuned version of \"RoBERTa Large\" using CUAD dataset",
"# Model Details",
"## Model Description\n \nThe Contract Understanding Atticus Dataset (CUAD), pronounced \"kwad\", a dataset for legal contract review curated b... | [
"TAGS\n#transformers #pytorch #safetensors #roberta #question-answering #en #dataset-cuad #arxiv-2103.06268 #arxiv-1910.09700 #endpoints_compatible #has_space #region-us \n",
"# Model Card for RoBERTa Large Model fine-tuned with CUAD dataset\n \nThis model is the fine-tuned version of \"RoBERTa Large\" using CUAD... |
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