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null | keras |
This is Image Orientation Detector by Vikram Kamthe
Given an image, it will classify it into Original Image or Upside Down Image | {"language": ["List of ISO 639-1 code for your language", "lang1", "lang2"], "license": "cc", "tags": ["tag1", "tag2"], "datasets": ["dataset1", "dataset2"], "metrics": ["metric1", "metric2"], "thumbnail": "url to a thumbnail used in social sharing"} | vkamthe/upside_down_detector | null | [
"keras",
"tag1",
"tag2",
"dataset:dataset1",
"dataset:dataset2",
"license:cc",
"region:us"
] | null | 2022-04-04T05:16:41+00:00 | [] | [
"List of ISO 639-1 code for your language",
"lang1",
"lang2"
] | TAGS
#keras #tag1 #tag2 #dataset-dataset1 #dataset-dataset2 #license-cc #region-us
|
This is Image Orientation Detector by Vikram Kamthe
Given an image, it will classify it into Original Image or Upside Down Image | [] | [
"TAGS\n#keras #tag1 #tag2 #dataset-dataset1 #dataset-dataset2 #license-cc #region-us \n"
] |
fill-mask | transformers |
# SKEP-Roberta
## Introduction
SKEP (SKEP: Sentiment Knowledge Enhanced Pre-training for Sentiment Analysis) is proposed by Baidu in 2020,
SKEP propose Sentiment Knowledge Enhanced Pre-training for sentiment analysis. Sentiment masking and three sentiment pre-training objectives are designed to incorporate various ... | {"language": "en"} | Yaxin/roberta-large-ernie2-skep-en | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-04T05:27:48+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roberta #fill-mask #en #autotrain_compatible #endpoints_compatible #region-us
| SKEP-Roberta
============
Introduction
------------
SKEP (SKEP: Sentiment Knowledge Enhanced Pre-training for Sentiment Analysis) is proposed by Baidu in 2020,
SKEP propose Sentiment Knowledge Enhanced Pre-training for sentiment analysis. Sentiment masking and three sentiment pre-training objectives are designed ... | [] | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #en #autotrain_compatible #endpoints_compatible #region-us \n"
] |
sentence-similarity | sentence-transformers |
# bespin-global/klue-sroberta-base-continue-learning-by-mnr
ํด๋น ๋ชจ๋ธ์ KLUE/NLI, KLUE/STS ๋ฐ์ดํฐ์
์ ํ์ฉํ์์ผ๋ฉฐ, sentence-transformers์ ๊ณต์ ๋ฌธ์ ๋ด ์๊ฐ๋ [continue-learning](https://github.com/UKPLab/sentence-transformers/blob/master/examples/training/sts/training_stsbenchmark_continue_training.py) ๋ฐฉ๋ฒ์ ํตํด ์๋์ ๊ฐ์ด ํ์ต๋์์ต๋๋ค.
1. NLI ๋ฐ์ดํฐ์
์ ํต... | {"language": ["ko"], "license": "cc-by-4.0", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "datasets": ["klue"], "pipeline_tag": "sentence-similarity"} | bespin-global/klue-sroberta-base-continue-learning-by-mnr | null | [
"sentence-transformers",
"pytorch",
"roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"ko",
"dataset:klue",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-04T05:33:25+00:00 | [] | [
"ko"
] | TAGS
#sentence-transformers #pytorch #roberta #feature-extraction #sentence-similarity #transformers #ko #dataset-klue #license-cc-by-4.0 #endpoints_compatible #region-us
|
# bespin-global/klue-sroberta-base-continue-learning-by-mnr
ํด๋น ๋ชจ๋ธ์ KLUE/NLI, KLUE/STS ๋ฐ์ดํฐ์
์ ํ์ฉํ์์ผ๋ฉฐ, sentence-transformers์ ๊ณต์ ๋ฌธ์ ๋ด ์๊ฐ๋ continue-learning ๋ฐฉ๋ฒ์ ํตํด ์๋์ ๊ฐ์ด ํ์ต๋์์ต๋๋ค.
1. NLI ๋ฐ์ดํฐ์
์ ํตํด nagative sampling ํ, MultipleNegativeRankingLoss๋ฅผ ํ์ฉํ์ฌ 1์ฐจ NLI training ์ํ
2. 1์์ ํ์ต์๋ฃ ๋ ๋ชจ๋ธ์ STS ๋ฐ์ดํฐ์
์ ํตํด, CosineSimilarityLoss... | [
"# bespin-global/klue-sroberta-base-continue-learning-by-mnr\n\nํด๋น ๋ชจ๋ธ์ KLUE/NLI, KLUE/STS ๋ฐ์ดํฐ์
์ ํ์ฉํ์์ผ๋ฉฐ, sentence-transformers์ ๊ณต์ ๋ฌธ์ ๋ด ์๊ฐ๋ continue-learning ๋ฐฉ๋ฒ์ ํตํด ์๋์ ๊ฐ์ด ํ์ต๋์์ต๋๋ค.\n1. NLI ๋ฐ์ดํฐ์
์ ํตํด nagative sampling ํ, MultipleNegativeRankingLoss๋ฅผ ํ์ฉํ์ฌ 1์ฐจ NLI training ์ํ\n2. 1์์ ํ์ต์๋ฃ ๋ ๋ชจ๋ธ์ STS ๋ฐ์ดํฐ์
์ ํตํด, CosineSimila... | [
"TAGS\n#sentence-transformers #pytorch #roberta #feature-extraction #sentence-similarity #transformers #ko #dataset-klue #license-cc-by-4.0 #endpoints_compatible #region-us \n",
"# bespin-global/klue-sroberta-base-continue-learning-by-mnr\n\nํด๋น ๋ชจ๋ธ์ KLUE/NLI, KLUE/STS ๋ฐ์ดํฐ์
์ ํ์ฉํ์์ผ๋ฉฐ, sentence-transformers์ ๊ณต์ ๋ฌธ์ ๋ด ์... |
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. -->
# codebert-base-buggy-token-classification
This model is a fine-tuned version of [microsoft/codebert-base](https://huggingface.co/... | {"tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "codebert-base-buggy-token-classification", "results": []}]} | alexjercan/codebert-base-buggy-token-classification | null | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-04T06:02:54+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# codebert-base-buggy-token-classification
This model is a fine-tuned version of microsoft/codebert-base on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5217
- Precision: 0.6942
- Recall: 0.0940
- F1: 0.1656
- Accuracy: 0.7714
## Model description
More information needed
... | [
"# codebert-base-buggy-token-classification\n\nThis model is a fine-tuned version of microsoft/codebert-base on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.5217\n- Precision: 0.6942\n- Recall: 0.0940\n- F1: 0.1656\n- Accuracy: 0.7714",
"## Model description\n\nMore inf... | [
"TAGS\n#transformers #pytorch #roberta #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"# codebert-base-buggy-token-classification\n\nThis model is a fine-tuned version of microsoft/codebert-base on an unknown dataset.\nIt achieves the following results on... |
text-generation | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | MrYiRen/DialoGPT-small-harrypotter | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-04T06:30:18+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Harry Potter DialoGPT Model | [
"# Harry Potter DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Harry Potter DialoGPT Model"
] |
text-generation | transformers |
# pszemraj/gpt-peter-2.7B
- This model is a fine-tuned version of [EleutherAI/gpt-neo-2.7B](https://huggingface.co/EleutherAI/gpt-neo-2.7B) on about 80k WhatsApp and iMessage texts.
- The model is too large to use the inference API. linked [here](https://colab.research.google.com/gist/pszemraj/a59b43813437b43973c8f8... | {"tags": ["gpt-neo", "gpt-peter", "chatbot"], "inference": false, "base_model": "EleutherAI/gpt-neo-2.7B"} | pszemraj/gpt-peter-2.7B | null | [
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"gpt-neo",
"gpt-peter",
"chatbot",
"base_model:EleutherAI/gpt-neo-2.7B",
"autotrain_compatible",
"region:us"
] | null | 2022-04-04T07:04:50+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt_neo #text-generation #gpt-neo #gpt-peter #chatbot #base_model-EleutherAI/gpt-neo-2.7B #autotrain_compatible #region-us
|
# pszemraj/gpt-peter-2.7B
- This model is a fine-tuned version of EleutherAI/gpt-neo-2.7B on about 80k WhatsApp and iMessage texts.
- The model is too large to use the inference API. linked here is a notebook for testing in Colab.
- alternatively, you can message a bot on telegram where I test LLMs for dialogue ... | [
"# pszemraj/gpt-peter-2.7B\n\n- This model is a fine-tuned version of EleutherAI/gpt-neo-2.7B on about 80k WhatsApp and iMessage texts.\n- The model is too large to use the inference API. linked here is a notebook for testing in Colab.\n - alternatively, you can message a bot on telegram where I test LLMs for di... | [
"TAGS\n#transformers #pytorch #gpt_neo #text-generation #gpt-neo #gpt-peter #chatbot #base_model-EleutherAI/gpt-neo-2.7B #autotrain_compatible #region-us \n",
"# pszemraj/gpt-peter-2.7B\n\n- This model is a fine-tuned version of EleutherAI/gpt-neo-2.7B on about 80k WhatsApp and iMessage texts.\n- The model is too... |
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-es-en-finetuned-es-to-en
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-es-en](https://huggingface.co/Helsi... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["bleu"], "model-index": [{"name": "opus-mt-es-en-finetuned-es-to-en", "results": []}]} | pfloyd/opus-mt-es-en-finetuned-es-to-en | null | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-04T07:13:11+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #marian #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| opus-mt-es-en-finetuned-es-to-en
================================
This model is a fine-tuned version of Helsinki-NLP/opus-mt-es-en on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5851
* Bleu: 71.1382
* Gen Len: 10.3225
Model description
-----------------
More information ... | [
"### 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 #marian #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batc... |
null | transformers |
# LeBenchmark: wav2vec2 base model trained on 1K hours of French *female-only* speech
LeBenchmark provides an ensemble of pretrained wav2vec2 models on different French datasets containing spontaneous, read, and broadcasted speech.
For more information about our gender study for SSL moddels, please refer to our ... | {"language": "fr", "license": "apache-2.0", "tags": ["wav2vec2"]} | LeBenchmark/wav2vec-FR-1K-Female-base | null | [
"transformers",
"pytorch",
"wav2vec2",
"pretraining",
"fr",
"arxiv:2204.01397",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-04T07:25:53+00:00 | [
"2204.01397"
] | [
"fr"
] | TAGS
#transformers #pytorch #wav2vec2 #pretraining #fr #arxiv-2204.01397 #license-apache-2.0 #endpoints_compatible #region-us
|
# LeBenchmark: wav2vec2 base model trained on 1K hours of French *female-only* speech
LeBenchmark provides an ensemble of pretrained wav2vec2 models on different French datasets containing spontaneous, read, and broadcasted speech.
For more information about our gender study for SSL moddels, please refer to our ... | [
"# LeBenchmark: wav2vec2 base model trained on 1K hours of French *female-only* speech\n\n \nLeBenchmark provides an ensemble of pretrained wav2vec2 models on different French datasets containing spontaneous, read, and broadcasted speech. \n\nFor more information about our gender study for SSL moddels, please refe... | [
"TAGS\n#transformers #pytorch #wav2vec2 #pretraining #fr #arxiv-2204.01397 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# LeBenchmark: wav2vec2 base model trained on 1K hours of French *female-only* speech\n\n \nLeBenchmark provides an ensemble of pretrained wav2vec2 models on different French data... |
null | transformers |
# LeBenchmark: wav2vec2 base model trained on 1K hours of French *female-only* speech
LeBenchmark provides an ensemble of pretrained wav2vec2 models on different French datasets containing spontaneous, read, and broadcasted speech.
For more information about our gender study for SSL moddels, please refer to our ... | {"language": "fr", "license": "apache-2.0", "tags": ["wav2vec2"]} | LeBenchmark/wav2vec-FR-1K-Male-base | null | [
"transformers",
"pytorch",
"wav2vec2",
"pretraining",
"fr",
"arxiv:2204.01397",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-04T07:33:54+00:00 | [
"2204.01397"
] | [
"fr"
] | TAGS
#transformers #pytorch #wav2vec2 #pretraining #fr #arxiv-2204.01397 #license-apache-2.0 #endpoints_compatible #region-us
|
# LeBenchmark: wav2vec2 base model trained on 1K hours of French *female-only* speech
LeBenchmark provides an ensemble of pretrained wav2vec2 models on different French datasets containing spontaneous, read, and broadcasted speech.
For more information about our gender study for SSL moddels, please refer to our ... | [
"# LeBenchmark: wav2vec2 base model trained on 1K hours of French *female-only* speech\n\n \nLeBenchmark provides an ensemble of pretrained wav2vec2 models on different French datasets containing spontaneous, read, and broadcasted speech. \n\nFor more information about our gender study for SSL moddels, please refe... | [
"TAGS\n#transformers #pytorch #wav2vec2 #pretraining #fr #arxiv-2204.01397 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# LeBenchmark: wav2vec2 base model trained on 1K hours of French *female-only* speech\n\n \nLeBenchmark provides an ensemble of pretrained wav2vec2 models on different French data... |
null | transformers |
# LeBenchmark: wav2vec2 base model trained on 1K hours of French *female-only* speech
LeBenchmark provides an ensemble of pretrained wav2vec2 models on different French datasets containing spontaneous, read, and broadcasted speech.
For more information about our gender study for SSL moddels, please refer to our ... | {"language": "fr", "license": "apache-2.0", "tags": ["wav2vec2"]} | LeBenchmark/wav2vec-FR-1K-Male-large | null | [
"transformers",
"pytorch",
"wav2vec2",
"pretraining",
"fr",
"arxiv:2204.01397",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-04T07:37:06+00:00 | [
"2204.01397"
] | [
"fr"
] | TAGS
#transformers #pytorch #wav2vec2 #pretraining #fr #arxiv-2204.01397 #license-apache-2.0 #endpoints_compatible #region-us
|
# LeBenchmark: wav2vec2 base model trained on 1K hours of French *female-only* speech
LeBenchmark provides an ensemble of pretrained wav2vec2 models on different French datasets containing spontaneous, read, and broadcasted speech.
For more information about our gender study for SSL moddels, please refer to our ... | [
"# LeBenchmark: wav2vec2 base model trained on 1K hours of French *female-only* speech\n\n \nLeBenchmark provides an ensemble of pretrained wav2vec2 models on different French datasets containing spontaneous, read, and broadcasted speech. \n\nFor more information about our gender study for SSL moddels, please refe... | [
"TAGS\n#transformers #pytorch #wav2vec2 #pretraining #fr #arxiv-2204.01397 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# LeBenchmark: wav2vec2 base model trained on 1K hours of French *female-only* speech\n\n \nLeBenchmark provides an ensemble of pretrained wav2vec2 models on different French data... |
null | transformers |
# LeBenchmark: wav2vec2 base model trained on 1K hours of French *female-only* speech
LeBenchmark provides an ensemble of pretrained wav2vec2 models on different French datasets containing spontaneous, read, and broadcasted speech.
For more information about our gender study for SSL moddels, please refer to our ... | {"language": "fr", "license": "apache-2.0", "tags": ["wav2vec2"]} | LeBenchmark/wav2vec-FR-1K-Female-large | null | [
"transformers",
"pytorch",
"wav2vec2",
"pretraining",
"fr",
"arxiv:2204.01397",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-04T07:38:53+00:00 | [
"2204.01397"
] | [
"fr"
] | TAGS
#transformers #pytorch #wav2vec2 #pretraining #fr #arxiv-2204.01397 #license-apache-2.0 #endpoints_compatible #region-us
|
# LeBenchmark: wav2vec2 base model trained on 1K hours of French *female-only* speech
LeBenchmark provides an ensemble of pretrained wav2vec2 models on different French datasets containing spontaneous, read, and broadcasted speech.
For more information about our gender study for SSL moddels, please refer to our ... | [
"# LeBenchmark: wav2vec2 base model trained on 1K hours of French *female-only* speech\n\n \nLeBenchmark provides an ensemble of pretrained wav2vec2 models on different French datasets containing spontaneous, read, and broadcasted speech. \n\nFor more information about our gender study for SSL moddels, please refe... | [
"TAGS\n#transformers #pytorch #wav2vec2 #pretraining #fr #arxiv-2204.01397 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# LeBenchmark: wav2vec2 base model trained on 1K hours of French *female-only* speech\n\n \nLeBenchmark provides an ensemble of pretrained wav2vec2 models on different French data... |
object-detection | keras |
### Ignore me, pipeline is broken (WIP)
| {"license": "cc0-1.0", "tags": ["object-detection", "tensorflow"], "dataset": ["oxfort-iit pets"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/savanna.jpg", "example_title": "Savanna"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/football-match.jp... | drab/distribution-equipment-belgium | null | [
"keras",
"tf",
"object-detection",
"tensorflow",
"license:cc0-1.0",
"region:us"
] | null | 2022-04-04T07:42:21+00:00 | [] | [] | TAGS
#keras #tf #object-detection #tensorflow #license-cc0-1.0 #region-us
|
### Ignore me, pipeline is broken (WIP)
| [
"### Ignore me, pipeline is broken (WIP)"
] | [
"TAGS\n#keras #tf #object-detection #tensorflow #license-cc0-1.0 #region-us \n",
"### Ignore me, pipeline is broken (WIP)"
] |
text-classification | transformers |
## Czech Media Bias Classifier
A FERNET-C5 model fine-tuned to perform binary classification task on czech media bias detection. | {"language": "cs", "tags": ["Czech"], "inference": false} | horychtom/czech_media_bias_classifier | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"Czech",
"cs",
"autotrain_compatible",
"region:us"
] | null | 2022-04-04T08:04:34+00:00 | [] | [
"cs"
] | TAGS
#transformers #pytorch #bert #text-classification #Czech #cs #autotrain_compatible #region-us
|
## Czech Media Bias Classifier
A FERNET-C5 model fine-tuned to perform binary classification task on czech media bias detection. | [
"## Czech Media Bias Classifier\n\nA FERNET-C5 model fine-tuned to perform binary classification task on czech media bias detection."
] | [
"TAGS\n#transformers #pytorch #bert #text-classification #Czech #cs #autotrain_compatible #region-us \n",
"## Czech Media Bias Classifier\n\nA FERNET-C5 model fine-tuned to perform binary classification task on czech media bias detection."
] |
null | null | Model for Fatima Fellowship code challenge. <br>
Full training and evaluation pipelines can be found at: https://colab.research.google.com/drive/1hjHn6EggRDsxOZz5fMo6ZdT-4aKcUCTt | {"language": ["Python", "PyTorch"], "license": "mit", "tags": ["cifar", "cats", "upsidedown"], "datasets": ["cifar10_reduced"], "metrics": ["Accuracy", "Precision", "Recall"], "model-index": [{"name": "CatsNet", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "... | ramazan/fatima-cats | null | [
"cifar",
"cats",
"upsidedown",
"dataset:cifar10_reduced",
"license:mit",
"model-index",
"region:us"
] | null | 2022-04-04T08:05:27+00:00 | [] | [
"Python",
"PyTorch"
] | TAGS
#cifar #cats #upsidedown #dataset-cifar10_reduced #license-mit #model-index #region-us
| Model for Fatima Fellowship code challenge. <br>
Full training and evaluation pipelines can be found at: URL | [] | [
"TAGS\n#cifar #cats #upsidedown #dataset-cifar10_reduced #license-mit #model-index #region-us \n"
] |
text-classification | transformers | # Fatima Fellowship NLP Project
## Fake News Classifier
- BERT base model finetuned to classify fake news. | {"license": "bsd-3-clause"} | pinku/FatimaFellowship_fake_and_real_news | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"license:bsd-3-clause",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-04T08:09:53+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #license-bsd-3-clause #autotrain_compatible #endpoints_compatible #region-us
| # Fatima Fellowship NLP Project
## Fake News Classifier
- BERT base model finetuned to classify fake news. | [
"# Fatima Fellowship NLP Project",
"## Fake News Classifier\r\n\r\n- BERT base model finetuned to classify fake news."
] | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #license-bsd-3-clause #autotrain_compatible #endpoints_compatible #region-us \n",
"# Fatima Fellowship NLP Project",
"## Fake News Classifier\r\n\r\n- BERT base model finetuned to classify fake news."
] |
token-classification | transformers |
This model predicts the punctuation of Catalan language.
The model restores the following punctuation markers: **"." "," "?" "-" ":"**
Based on the work https://github.com/oliverguhr/fullstop-deep-punctuation-prediction
## Results
The performance differs for the single punctuation markers as hyphens and colons, in... | {"language": ["ca"], "tags": ["punctuation prediction", "punctuation"], "datasets": "softcatala/Europarl-catalan", "metrics": ["f1"], "widget": [{"text": "Els investigadors suggereixen que tot i que es tracta de la cua d'un dinosaure jove la mostra revela un plomatge adult i no pas plomissol", "example_title": "Catalan... | softcatala/fullstop-catalan-punctuation-prediction | null | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"punctuation prediction",
"punctuation",
"ca",
"dataset:softcatala/Europarl-catalan",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-04T08:31:12+00:00 | [] | [
"ca"
] | TAGS
#transformers #pytorch #roberta #token-classification #punctuation prediction #punctuation #ca #dataset-softcatala/Europarl-catalan #autotrain_compatible #endpoints_compatible #region-us
| This model predicts the punctuation of Catalan language.
The model restores the following punctuation markers: "." "," "?" "-" ":"
Based on the work URL
Results
-------
The performance differs for the single punctuation markers as hyphens and colons, in many cases, are optional and can be substituted by either ... | [] | [
"TAGS\n#transformers #pytorch #roberta #token-classification #punctuation prediction #punctuation #ca #dataset-softcatala/Europarl-catalan #autotrain_compatible #endpoints_compatible #region-us \n"
] |
null | null |
This model is a fine-turned version of the [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the [Fake News Dataset](https://www.kaggle.com/clmentbisaillon/fake-and-real-news-dataset) from Kaggle to classified news item as either **Fake** or **Real**.
The following results were achieved on the evaluat... | {"license": "cc-by-4.0"} | olasammy/fatima-fellowship-nlp | null | [
"license:cc-by-4.0",
"region:us"
] | null | 2022-04-04T09:00:35+00:00 | [] | [] | TAGS
#license-cc-by-4.0 #region-us
|
This model is a fine-turned version of the bert-base-uncased on the Fake News Dataset from Kaggle to classified news item as either Fake or Real.
The following results were achieved on the evaluation set:
- F1 Score: 0.99
- Accuracy Score: 0.99
- AUC: 0.99
| [] | [
"TAGS\n#license-cc-by-4.0 #region-us \n"
] |
fill-mask | 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. -->
# avialfont/dummy-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "avialfont/dummy-finetuned-imdb", "results": []}]} | avialfont/dummy-finetuned-imdb | null | [
"transformers",
"tf",
"distilbert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-04T09:06:50+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| avialfont/dummy-finetuned-imdb
==============================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 2.8606
* Validation Loss: 2.5865
* Epoch: 0
Model description
-----------------
More informati... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'WarmUp', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_schedule\\_fn': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_le... | [
"TAGS\n#transformers #tf #distilbert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate'... |
text2text-generation | transformers |
# NMT for Finno-Ugric Languages
This is an NMT system for translating between Vรตro, Livonian, North Sami, South Sami as well as Estonian, Finnish, Latvian and English. It was created by fine-tuning Facebook's m2m100-418M on the liv4ever and smugri datasets.
## Tokenizer
Four language codes were added to the tokenize... | {"language": ["en"], "license": "mit", "widget": [{"text": "Let us translate some text from Livonian to V\u00f5ro!"}]} | tartuNLP/m2m100_418M_smugri | null | [
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-04T10:24:56+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #m2m_100 #text2text-generation #en #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# NMT for Finno-Ugric Languages
This is an NMT system for translating between Vรตro, Livonian, North Sami, South Sami as well as Estonian, Finnish, Latvian and English. It was created by fine-tuning Facebook's m2m100-418M on the liv4ever and smugri datasets.
## Tokenizer
Four language codes were added to the tokenize... | [
"# NMT for Finno-Ugric Languages\n\nThis is an NMT system for translating between Vรตro, Livonian, North Sami, South Sami as well as Estonian, Finnish, Latvian and English. It was created by fine-tuning Facebook's m2m100-418M on the liv4ever and smugri datasets.",
"## Tokenizer\nFour language codes were added to t... | [
"TAGS\n#transformers #pytorch #m2m_100 #text2text-generation #en #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# NMT for Finno-Ugric Languages\n\nThis is an NMT system for translating between Vรตro, Livonian, North Sami, South Sami as well as Estonian, Finnish, Latvian and English. It w... |
image-classification | transformers |
# Convolutional Vision Transformer (CvT)
CvT-13 model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in the paper [CvT: Introducing Convolutions to Vision Transformers](https://arxiv.org/abs/2103.15808) by Wu et al. and first released in [this repository](https://github.com/microsoft/CvT).
Disc... | {"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet-1k"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example... | microsoft/cvt-13 | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"cvt",
"image-classification",
"vision",
"dataset:imagenet-1k",
"arxiv:2103.15808",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-04T10:32:10+00:00 | [
"2103.15808"
] | [] | TAGS
#transformers #pytorch #tf #safetensors #cvt #image-classification #vision #dataset-imagenet-1k #arxiv-2103.15808 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Convolutional Vision Transformer (CvT)
CvT-13 model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in the paper CvT: Introducing Convolutions to Vision Transformers by Wu et al. and first released in this repository.
Disclaimer: The team releasing CvT did not write a model card for this model... | [
"# Convolutional Vision Transformer (CvT)\n\nCvT-13 model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in the paper CvT: Introducing Convolutions to Vision Transformers by Wu et al. and first released in this repository. \n\nDisclaimer: The team releasing CvT did not write a model card for th... | [
"TAGS\n#transformers #pytorch #tf #safetensors #cvt #image-classification #vision #dataset-imagenet-1k #arxiv-2103.15808 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Convolutional Vision Transformer (CvT)\n\nCvT-13 model pre-trained on ImageNet-1k at resolution 224... |
image-classification | transformers |
# Convolutional Vision Transformer (CvT)
CvT-13 model pre-trained on ImageNet-1k at resolution 384x384. It was introduced in the paper [CvT: Introducing Convolutions to Vision Transformers](https://arxiv.org/abs/2103.15808) by Wu et al. and first released in [this repository](https://github.com/microsoft/CvT).
Disc... | {"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet-1k"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example... | microsoft/cvt-13-384 | null | [
"transformers",
"pytorch",
"tf",
"cvt",
"image-classification",
"vision",
"dataset:imagenet-1k",
"arxiv:2103.15808",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-04T10:32:39+00:00 | [
"2103.15808"
] | [] | TAGS
#transformers #pytorch #tf #cvt #image-classification #vision #dataset-imagenet-1k #arxiv-2103.15808 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Convolutional Vision Transformer (CvT)
CvT-13 model pre-trained on ImageNet-1k at resolution 384x384. It was introduced in the paper CvT: Introducing Convolutions to Vision Transformers by Wu et al. and first released in this repository.
Disclaimer: The team releasing CvT did not write a model card for this model... | [
"# Convolutional Vision Transformer (CvT)\n\nCvT-13 model pre-trained on ImageNet-1k at resolution 384x384. It was introduced in the paper CvT: Introducing Convolutions to Vision Transformers by Wu et al. and first released in this repository. \n\nDisclaimer: The team releasing CvT did not write a model card for th... | [
"TAGS\n#transformers #pytorch #tf #cvt #image-classification #vision #dataset-imagenet-1k #arxiv-2103.15808 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Convolutional Vision Transformer (CvT)\n\nCvT-13 model pre-trained on ImageNet-1k at resolution 384x384. It was introduced ... |
image-classification | transformers |
# Convolutional Vision Transformer (CvT)
CvT-13 model pre-trained on ImageNet-22k and fine-tuned on ImageNet-1k at resolution 384x384. It was introduced in the paper [CvT: Introducing Convolutions to Vision Transformers](https://arxiv.org/abs/2103.15808) by Wu et al. and first released in [this repository](https://gi... | {"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet-1k"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example... | microsoft/cvt-13-384-22k | null | [
"transformers",
"pytorch",
"tf",
"cvt",
"image-classification",
"vision",
"dataset:imagenet-1k",
"arxiv:2103.15808",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-04T10:32:55+00:00 | [
"2103.15808"
] | [] | TAGS
#transformers #pytorch #tf #cvt #image-classification #vision #dataset-imagenet-1k #arxiv-2103.15808 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Convolutional Vision Transformer (CvT)
CvT-13 model pre-trained on ImageNet-22k and fine-tuned on ImageNet-1k at resolution 384x384. It was introduced in the paper CvT: Introducing Convolutions to Vision Transformers by Wu et al. and first released in this repository.
Disclaimer: The team releasing CvT did not wr... | [
"# Convolutional Vision Transformer (CvT)\n\nCvT-13 model pre-trained on ImageNet-22k and fine-tuned on ImageNet-1k at resolution 384x384. It was introduced in the paper CvT: Introducing Convolutions to Vision Transformers by Wu et al. and first released in this repository. \n\nDisclaimer: The team releasing CvT di... | [
"TAGS\n#transformers #pytorch #tf #cvt #image-classification #vision #dataset-imagenet-1k #arxiv-2103.15808 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Convolutional Vision Transformer (CvT)\n\nCvT-13 model pre-trained on ImageNet-22k and fine-tuned on ImageNet-1k at resolut... |
image-classification | transformers |
# Convolutional Vision Transformer (CvT)
CvT-21 model pre-trained on ImageNet-22k and fine-tuned on ImageNet-1k at resolution 384x384. It was introduced in the paper [CvT: Introducing Convolutions to Vision Transformers](https://arxiv.org/abs/2103.15808) by Wu et al. and first released in [this repository](https://gi... | {"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet-1k"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example... | microsoft/cvt-21-384-22k | null | [
"transformers",
"pytorch",
"tf",
"cvt",
"image-classification",
"vision",
"dataset:imagenet-1k",
"arxiv:2103.15808",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-04T10:33:08+00:00 | [
"2103.15808"
] | [] | TAGS
#transformers #pytorch #tf #cvt #image-classification #vision #dataset-imagenet-1k #arxiv-2103.15808 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Convolutional Vision Transformer (CvT)
CvT-21 model pre-trained on ImageNet-22k and fine-tuned on ImageNet-1k at resolution 384x384. It was introduced in the paper CvT: Introducing Convolutions to Vision Transformers by Wu et al. and first released in this repository.
Disclaimer: The team releasing CvT did not wr... | [
"# Convolutional Vision Transformer (CvT)\n\nCvT-21 model pre-trained on ImageNet-22k and fine-tuned on ImageNet-1k at resolution 384x384. It was introduced in the paper CvT: Introducing Convolutions to Vision Transformers by Wu et al. and first released in this repository. \n\nDisclaimer: The team releasing CvT di... | [
"TAGS\n#transformers #pytorch #tf #cvt #image-classification #vision #dataset-imagenet-1k #arxiv-2103.15808 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Convolutional Vision Transformer (CvT)\n\nCvT-21 model pre-trained on ImageNet-22k and fine-tuned on ImageNet-1k at resolut... |
image-classification | transformers |
# Convolutional Vision Transformer (CvT)
CvT-21 model pre-trained on ImageNet-1k at resolution 384x384. It was introduced in the paper [CvT: Introducing Convolutions to Vision Transformers](https://arxiv.org/abs/2103.15808) by Wu et al. and first released in [this repository](https://github.com/microsoft/CvT).
Disc... | {"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet-1k"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example... | microsoft/cvt-21-384 | null | [
"transformers",
"pytorch",
"tf",
"cvt",
"image-classification",
"vision",
"dataset:imagenet-1k",
"arxiv:2103.15808",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-04T10:33:19+00:00 | [
"2103.15808"
] | [] | TAGS
#transformers #pytorch #tf #cvt #image-classification #vision #dataset-imagenet-1k #arxiv-2103.15808 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Convolutional Vision Transformer (CvT)
CvT-21 model pre-trained on ImageNet-1k at resolution 384x384. It was introduced in the paper CvT: Introducing Convolutions to Vision Transformers by Wu et al. and first released in this repository.
Disclaimer: The team releasing CvT did not write a model card for this model... | [
"# Convolutional Vision Transformer (CvT)\n\nCvT-21 model pre-trained on ImageNet-1k at resolution 384x384. It was introduced in the paper CvT: Introducing Convolutions to Vision Transformers by Wu et al. and first released in this repository. \n\nDisclaimer: The team releasing CvT did not write a model card for th... | [
"TAGS\n#transformers #pytorch #tf #cvt #image-classification #vision #dataset-imagenet-1k #arxiv-2103.15808 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Convolutional Vision Transformer (CvT)\n\nCvT-21 model pre-trained on ImageNet-1k at resolution 384x384. It was introduced ... |
image-classification | transformers |
# Convolutional Vision Transformer (CvT)
CvT-21 model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in the paper [CvT: Introducing Convolutions to Vision Transformers](https://arxiv.org/abs/2103.15808) by Wu et al. and first released in [this repository](https://github.com/microsoft/CvT).
Disc... | {"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet-1k"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example... | microsoft/cvt-21 | null | [
"transformers",
"pytorch",
"tf",
"cvt",
"image-classification",
"vision",
"dataset:imagenet-1k",
"arxiv:2103.15808",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-04T10:33:51+00:00 | [
"2103.15808"
] | [] | TAGS
#transformers #pytorch #tf #cvt #image-classification #vision #dataset-imagenet-1k #arxiv-2103.15808 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Convolutional Vision Transformer (CvT)
CvT-21 model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in the paper CvT: Introducing Convolutions to Vision Transformers by Wu et al. and first released in this repository.
Disclaimer: The team releasing CvT did not write a model card for this model... | [
"# Convolutional Vision Transformer (CvT)\n\nCvT-21 model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in the paper CvT: Introducing Convolutions to Vision Transformers by Wu et al. and first released in this repository. \n\nDisclaimer: The team releasing CvT did not write a model card for th... | [
"TAGS\n#transformers #pytorch #tf #cvt #image-classification #vision #dataset-imagenet-1k #arxiv-2103.15808 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Convolutional Vision Transformer (CvT)\n\nCvT-21 model pre-trained on ImageNet-1k at resolution 224x224. It was introduced ... |
null | transformers |
# SKEP-
## Introduction
SKEP (SKEP: Sentiment Knowledge Enhanced Pre-training for Sentiment Analysis) is proposed by Baidu in 2020,
SKEP propose Sentiment Knowledge Enhanced Pre-training for sentiment analysis. Sentiment masking and three sentiment pre-training objectives are designed to incorporate various types o... | {"language": "zh"} | Yaxin/ernie_1.0_skep_large_ch | null | [
"transformers",
"pytorch",
"bert",
"zh",
"endpoints_compatible",
"region:us"
] | null | 2022-04-04T11:24:04+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #bert #zh #endpoints_compatible #region-us
| SKEP-
=====
Introduction
------------
SKEP (SKEP: Sentiment Knowledge Enhanced Pre-training for Sentiment Analysis) is proposed by Baidu in 2020,
SKEP propose Sentiment Knowledge Enhanced Pre-training for sentiment analysis. Sentiment masking and three sentiment pre-training objectives are designed to incorporate... | [] | [
"TAGS\n#transformers #pytorch #bert #zh #endpoints_compatible #region-us \n"
] |
audio-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. -->
# xtreme_s_xlsr_300m_fleurs_langid_quicker_warmup
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://hug... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["xtreme_s"], "metrics": ["accuracy"], "model-index": [{"name": "xtreme_s_xlsr_300m_fleurs_langid_quicker_warmup", "results": []}]} | anton-l/xtreme_s_xlsr_300m_fleurs_langid_quicker_warmup | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"dataset:xtreme_s",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-04T11:39:10+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #dataset-xtreme_s #license-apache-2.0 #endpoints_compatible #region-us
| xtreme\_s\_xlsr\_300m\_fleurs\_langid\_quicker\_warmup
======================================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the xtreme\_s dataset.
It achieves the following results on the evaluation set:
* Loss: 1.9765
* Accuracy: 0.6199
Model description
--... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 1\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 8\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 64\n... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #dataset-xtreme_s #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-finetuned-imdb
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"], "model-index": [{"name": "bert-base-uncased-finetuned-imdb", "results": []}]} | medhabi/bert-base-uncased-finetuned-imdb | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-04T11:47:31+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased-finetuned-imdb
================================
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.2887
Model description
-----------------
More information needed
Intended uses & limitations
-----... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0\n* mixed\\_pr... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-issues-128
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased)... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bert-base-uncased-issues-128", "results": []}]} | frahman/bert-base-uncased-issues-128 | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-04T12:00:28+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased-issues-128
============================
This model is a fine-tuned version of bert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2551
Model description
-----------------
More information needed
Intended uses & limitations
---------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 16",
"### Traini... | [
"TAGS\n#transformers #pytorch #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_bat... |
image-classification | keras |
# Sample output on test set:
14/14 [==============================] - 0s 21ms/step - loss: 0.1492 - accuracy: 0.9330
Test accuracy : 0.9330357313156128
| {"library_name": "keras", "tags": ["image-classification", "keras"]} | Ozi/Upsidedown_street_classifier | null | [
"keras",
"image-classification",
"region:us"
] | null | 2022-04-04T12:07:15+00:00 | [] | [] | TAGS
#keras #image-classification #region-us
|
# Sample output on test set:
14/14 [==============================] - 0s 21ms/step - loss: 0.1492 - accuracy: 0.9330
Test accuracy : 0.9330357313156128
!Screenshot
| [
"# Sample output on test set: \n\n14/14 [==============================] - 0s 21ms/step - loss: 0.1492 - accuracy: 0.9330\nTest accuracy : 0.9330357313156128\n\n!Screenshot"
] | [
"TAGS\n#keras #image-classification #region-us \n",
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] |
text-classification | transformers |
#### DATASET: [Fake and real news dataset](https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset)
#### Matthews correlation: 0.998 | {"license": "afl-3.0"} | yj2773/distilbert-base-uncased-fakenews-classif-task | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-04T12:09:09+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #license-afl-3.0 #autotrain_compatible #endpoints_compatible #region-us
|
#### DATASET: Fake and real news dataset
#### Matthews correlation: 0.998 | [
"#### DATASET: Fake and real news dataset",
"#### Matthews correlation: 0.998"
] | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #license-afl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"#### DATASET: Fake and real news dataset",
"#### Matthews correlation: 0.998"
] |
text-classification | transformers | ### Dataset used
[Fake and real news dataset](https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset)
### Labels
Fake news: 1 </br>
Real news: 0
### Usage
```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoConfig
import torch
config = AutoConfig.from_pret... | {"license": "mit"} | bhavitvyamalik/fake-news_xtremedistil-l6-h256-uncased | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-04T12:17:52+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #license-mit #autotrain_compatible #endpoints_compatible #region-us
| ### Dataset used
Fake and real news dataset
### Labels
Fake news: 1 </br>
Real news: 0
### Usage
### Performance on test data
### Run can be tracked here
Wandb project for Fake news classifier | [
"### Dataset used \nFake and real news dataset",
"### Labels\nFake news: 1 </br>\nReal news: 0",
"### Usage",
"### Performance on test data",
"### Run can be tracked here\nWandb project for Fake news classifier"
] | [
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"### Dataset used \nFake and real news dataset",
"### Labels\nFake news: 1 </br>\nReal news: 0",
"### Usage",
"### Performance on test data",
"### Run can be tracked here\nWand... |
null | null |
# Write up:
## Link to hugging face model:
https://huggingface.co/Sajib-006/fake_news_detection_xlmRoberta
## Model Description:
* Used pretrained XLM-Roberta base model.
* Added classifier layer after bert model
* For tokenization, i used max length of text as 512(which is max bert can handle)
## R... | {"language": ["Python"], "tags": ["NLP", "Fake News Detection", "XLM RoBERTa"], "datasets": ["https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset"], "metrics": ["Accuracy", "F1-score"]} | Sajib-006/fake_news_detection_xlmRoberta | null | [
"NLP",
"Fake News Detection",
"XLM RoBERTa",
"region:us"
] | null | 2022-04-04T12:19:49+00:00 | [] | [
"Python"
] | TAGS
#NLP #Fake News Detection #XLM RoBERTa #region-us
|
# Write up:
## Link to hugging face model:
URL
## Model Description:
* Used pretrained XLM-Roberta base model.
* Added classifier layer after bert model
* For tokenization, i used max length of text as 512(which is max bert can handle)
## Result:
* Using bert base uncased english model, the accu... | [
"# Write up:",
"## Link to hugging face model:\nURL",
"## Model Description:\n * Used pretrained XLM-Roberta base model.\n * Added classifier layer after bert model\n * For tokenization, i used max length of text as 512(which is max bert can handle)",
"## Result:\n * Using bert base uncased englis... | [
"TAGS\n#NLP #Fake News Detection #XLM RoBERTa #region-us \n",
"# Write up:",
"## Link to hugging face model:\nURL",
"## Model Description:\n * Used pretrained XLM-Roberta base model.\n * Added classifier layer after bert model\n * For tokenization, i used max length of text as 512(which is max bert c... |
null | transformers |
# SKEP-
## Introduction
SKEP (SKEP: Sentiment Knowledge Enhanced Pre-training for Sentiment Analysis) is proposed by Baidu in 2020,
SKEP propose Sentiment Knowledge Enhanced Pre-training for sentiment analysis. Sentiment masking and three sentiment pre-training objectives are designed to incorporate various types o... | {"language": "en"} | Yaxin/ernie_2.0_skep_large_en | null | [
"transformers",
"pytorch",
"bert",
"en",
"endpoints_compatible",
"region:us"
] | null | 2022-04-04T12:45:07+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #en #endpoints_compatible #region-us
| SKEP-
=====
Introduction
------------
SKEP (SKEP: Sentiment Knowledge Enhanced Pre-training for Sentiment Analysis) is proposed by Baidu in 2020,
SKEP propose Sentiment Knowledge Enhanced Pre-training for sentiment analysis. Sentiment masking and three sentiment pre-training objectives are designed to incorporate... | [] | [
"TAGS\n#transformers #pytorch #bert #en #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-wikihow_3epoch_v2
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wi... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wikihow"], "metrics": ["rouge"], "model-index": [{"name": "t5-small-finetuned-wikihow_3epoch_v2", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "wikihow", "type": "w... | Sevil/t5-small-finetuned-wikihow_3epoch_v2 | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:wikihow",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-04T12:45:46+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wikihow #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-wikihow\_3epoch\_v2
======================================
This model is a fine-tuned version of t5-small on the wikihow dataset.
It achieves the following results on the evaluation set:
* Loss: 2.2758
* Rouge1: 27.48
* Rouge2: 10.7621
* Rougel: 23.4136
* Rougelsum: 26.7923
* Gen Len: 18.5424
M... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3\n* mixed\\_preci... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wikihow #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during tr... |
null | null |
# AlexNet Classification for MNIST
| {"license": "cc0-1.0"} | csisc/MNISTClassification | null | [
"license:cc0-1.0",
"region:us"
] | null | 2022-04-04T12:55:54+00:00 | [] | [] | TAGS
#license-cc0-1.0 #region-us
|
# AlexNet Classification for MNIST
| [
"# AlexNet Classification for MNIST"
] | [
"TAGS\n#license-cc0-1.0 #region-us \n",
"# AlexNet Classification for MNIST"
] |
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. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-de", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "args": "PAN-X.de"}, "me... | leixu/xlm-roberta-base-finetuned-panx-de | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-04T13:32:44+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-de
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1377
* F1: 0.8605
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\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... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #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-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-finetuned-sentiment
This model is a fine-tuned version of [nlptown/bert-base-multilingual-uncased-sentiment](https://huggin... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "bert-finetuned-sentiment", "results": []}]} | Kalaoke/bert-finetuned-sentiment | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-04T13:49:40+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| bert-finetuned-sentiment
========================
This model is a fine-tuned version of nlptown/bert-base-multilingual-uncased-sentiment on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.4884
* Accuracy: 0.7698
Model description
-----------------
More information needed
I... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: ... |
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. -->
# AraBART-finetuned-ar
This model is a fine-tuned version of [moussaKam/AraBART](https://huggingface.co/moussaKam/AraBART) on the ... | {"license": "apache-2.0", "tags": ["summarization", "generated_from_trainer"], "datasets": ["xlsum"], "model-index": [{"name": "AraBART-finetune-ar-xlsum", "results": []}]} | ahmeddbahaa/AraBART-finetuned-ar | null | [
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"summarization",
"generated_from_trainer",
"dataset:xlsum",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-04T13:58:44+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #mbart #text2text-generation #summarization #generated_from_trainer #dataset-xlsum #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| AraBART-finetuned-ar
====================
This model is a fine-tuned version of moussaKam/AraBART on the xlsum dataset.
It achieves the following results on the evaluation set:
* Loss: 3.7449
* Rouge-1: 31.08
* Rouge-2: 14.68
* Rouge-l: 27.36
* Gen Len: 19.64
* Bertscore: 73.86
Model description
-----------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-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* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #tensorboard #mbart #text2text-generation #summarization #generated_from_trainer #dataset-xlsum #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\... |
zero-shot-classification | transformers |
# A zero-shot classifier based on bertin-roberta-base-spanish
This model was trained on the basis of the model `bertin-roberta-base-spanish` using **Cross encoder** for NLI task. A CrossEncoder takes a sentence pair as input and outputs a label so it learns to predict the labels: "contradiction": 0, "entailment": 1, ... | {"language": ["es"], "tags": ["zero-shot-classification", "nli"], "datasets": ["hackathon-pln-es/nli-es"], "pipeline_tag": "zero-shot-classification", "widget": [{"text": "Para detener la pandemia, es importante que todos se presenten a vacunarse.", "candidate_labels": "salud, deporte, entretenimiento"}]} | hackathon-pln-es/bertin-roberta-base-zeroshot-esnli | null | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"text-classification",
"zero-shot-classification",
"nli",
"es",
"dataset:hackathon-pln-es/nli-es",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-04T14:05:03+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #safetensors #roberta #text-classification #zero-shot-classification #nli #es #dataset-hackathon-pln-es/nli-es #autotrain_compatible #endpoints_compatible #region-us
|
# A zero-shot classifier based on bertin-roberta-base-spanish
This model was trained on the basis of the model 'bertin-roberta-base-spanish' using Cross encoder for NLI task. A CrossEncoder takes a sentence pair as input and outputs a label so it learns to predict the labels: "contradiction": 0, "entailment": 1, "neu... | [
"# A zero-shot classifier based on bertin-roberta-base-spanish\nThis model was trained on the basis of the model 'bertin-roberta-base-spanish' using Cross encoder for NLI task. A CrossEncoder takes a sentence pair as input and outputs a label so it learns to predict the labels: \"contradiction\": 0, \"entailment\":... | [
"TAGS\n#transformers #pytorch #safetensors #roberta #text-classification #zero-shot-classification #nli #es #dataset-hackathon-pln-es/nli-es #autotrain_compatible #endpoints_compatible #region-us \n",
"# A zero-shot classifier based on bertin-roberta-base-spanish\nThis model was trained on the basis of the model ... |
null | null | # Pokemon Classifier
This repo is a part of my study in deep learning with [fast.ai](https://www.fast.ai), this app uses this template [repo](https://github.com/render-examples/fastai-v3). thanks to them for the starter code and the [fast ai MOOC](https://course.fast.ai/) for making it easy to build deep learning m... | {"license": "wtfpl"} | Manimaran/pokemon_classifer | null | [
"license:wtfpl",
"has_space",
"region:us"
] | null | 2022-04-04T14:31:57+00:00 | [] | [] | TAGS
#license-wtfpl #has_space #region-us
| # Pokemon Classifier
This repo is a part of my study in deep learning with URL, this app uses this template repo. thanks to them for the starter code and the fast ai MOOC for making it easy to build deep learning models, and also the creator of this <del>dataset</del> for putting up a curated dataset (removed from ... | [
"# Pokemon Classifier\r\n\r\nThis repo is a part of my study in deep learning with URL, this app uses this template repo. thanks to them for the starter code and the fast ai MOOC for making it easy to build deep learning models, and also the creator of this <del>dataset</del> for putting up a curated dataset (remo... | [
"TAGS\n#license-wtfpl #has_space #region-us \n",
"# Pokemon Classifier\r\n\r\nThis repo is a part of my study in deep learning with URL, this app uses this template repo. thanks to them for the starter code and the fast ai MOOC for making it easy to build deep learning models, and also the creator of this <del>d... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ascend_with_timit
This model is a fine-tuned version of [GleamEyeBeast/ascend_with_timit](https://huggingface.co/GleamEyeBeast/a... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "ascend_with_timit", "results": []}]} | GleamEyeBeast/ascend_with_timit | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2022-04-04T14:47:08+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #endpoints_compatible #region-us
| ascend\_with\_timit
===================
This model is a fine-tuned version of GleamEyeBeast/ascend\_with\_timit on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8013
* Wer: 0.4781
* Cer: 0.1727
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_s... |
null | transformers | ## Model and data descriptions
This is a wav2vec 2.0 base model pre-trained on 243 hours of Tamasheq speech from the corpus presented in [Boito et al., 2022](https://arxiv.org/abs/2201.05051).
**This is not an ASR fine-tuned model. There is no vocabulary file.**
## Intended uses & limitations
Pretrained wav2vec2 mod... | {} | LIA-AvignonUniversity/IWSLT2022-tamasheq-only | null | [
"transformers",
"pytorch",
"wav2vec2",
"pretraining",
"arxiv:2201.05051",
"endpoints_compatible",
"region:us"
] | null | 2022-04-04T14:48:55+00:00 | [
"2201.05051"
] | [] | TAGS
#transformers #pytorch #wav2vec2 #pretraining #arxiv-2201.05051 #endpoints_compatible #region-us
| ## Model and data descriptions
This is a wav2vec 2.0 base model pre-trained on 243 hours of Tamasheq speech from the corpus presented in Boito et al., 2022.
This is not an ASR fine-tuned model. There is no vocabulary file.
## Intended uses & limitations
Pretrained wav2vec2 models are distributed under the Apache-2.0... | [
"## Model and data descriptions\n\nThis is a wav2vec 2.0 base model pre-trained on 243 hours of Tamasheq speech from the corpus presented in Boito et al., 2022.\nThis is not an ASR fine-tuned model. There is no vocabulary file.",
"## Intended uses & limitations\n\nPretrained wav2vec2 models are distributed under ... | [
"TAGS\n#transformers #pytorch #wav2vec2 #pretraining #arxiv-2201.05051 #endpoints_compatible #region-us \n",
"## Model and data descriptions\n\nThis is a wav2vec 2.0 base model pre-trained on 243 hours of Tamasheq speech from the corpus presented in Boito et al., 2022.\nThis is not an ASR fine-tuned model. There ... |
text-classification | transformers |
# Cross-Encoder
This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class.
<p align="center">
<img src="https://user-images.githubusercontent.com/7140210/72913702-d55a8480-3d3d-11ea-99fc-f2ef29af4e72.jpg" wi... | {"language": ["it"], "tags": ["cross-encoder", "sentence-similarity", "transformers"], "datasets": ["stsb_multi_mt"], "pipeline_tag": "text-classification"} | efederici/cross-encoder-umberto-stsb | null | [
"transformers",
"pytorch",
"camembert",
"text-classification",
"cross-encoder",
"sentence-similarity",
"it",
"dataset:stsb_multi_mt",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-04T14:48:58+00:00 | [] | [
"it"
] | TAGS
#transformers #pytorch #camembert #text-classification #cross-encoder #sentence-similarity #it #dataset-stsb_multi_mt #autotrain_compatible #endpoints_compatible #region-us
|
# Cross-Encoder
This model was trained using SentenceTransformers Cross-Encoder class.
<p align="center">
<img src="URL width="700"> </br>
Marco Lodola, Monument to Umberto Eco, Alessandria 2019
</p>
## Training Data
This model was trained on stsb. The model will predict a score between 0 and 1 how for the ... | [
"# Cross-Encoder\n\nThis model was trained using SentenceTransformers Cross-Encoder class.\n\n<p align=\"center\">\n <img src=\"URL width=\"700\"> </br>\n Marco Lodola, Monument to Umberto Eco, Alessandria 2019\n</p>",
"## Training Data\nThis model was trained on stsb. The model will predict a score between... | [
"TAGS\n#transformers #pytorch #camembert #text-classification #cross-encoder #sentence-similarity #it #dataset-stsb_multi_mt #autotrain_compatible #endpoints_compatible #region-us \n",
"# Cross-Encoder\n\nThis model was trained using SentenceTransformers Cross-Encoder class.\n\n<p align=\"center\">\n <img src=... |
null | transformers | ## Model and data descriptions
This is a wav2vec 2.0 base model trained on the Niger-Mali audio collection and on the Tamasheq-French speech corpus. These combined contained 111 hours of French, 109 hours of Fulfulde, 100 hours of Hausa, 243 hours of Tamasheq and 95 hours of Zarma.
These corpora were presented in [Boi... | {} | LIA-AvignonUniversity/IWSLT2022-Niger-Mali | null | [
"transformers",
"pytorch",
"wav2vec2",
"pretraining",
"arxiv:2201.05051",
"endpoints_compatible",
"region:us"
] | null | 2022-04-04T15:13:17+00:00 | [
"2201.05051"
] | [] | TAGS
#transformers #pytorch #wav2vec2 #pretraining #arxiv-2201.05051 #endpoints_compatible #region-us
| ## Model and data descriptions
This is a wav2vec 2.0 base model trained on the Niger-Mali audio collection and on the Tamasheq-French speech corpus. These combined contained 111 hours of French, 109 hours of Fulfulde, 100 hours of Hausa, 243 hours of Tamasheq and 95 hours of Zarma.
These corpora were presented in Boit... | [
"## Model and data descriptions\n\nThis is a wav2vec 2.0 base model trained on the Niger-Mali audio collection and on the Tamasheq-French speech corpus. These combined contained 111 hours of French, 109 hours of Fulfulde, 100 hours of Hausa, 243 hours of Tamasheq and 95 hours of Zarma.\nThese corpora were presented... | [
"TAGS\n#transformers #pytorch #wav2vec2 #pretraining #arxiv-2201.05051 #endpoints_compatible #region-us \n",
"## Model and data descriptions\n\nThis is a wav2vec 2.0 base model trained on the Niger-Mali audio collection and on the Tamasheq-French speech corpus. These combined contained 111 hours of French, 109 ho... |
null | null | Badly trained lightweightgan - ignore | {} | johnowhitaker/butterfly-gan-10k | null | [
"pytorch",
"region:us"
] | null | 2022-04-04T15:23:33+00:00 | [] | [] | TAGS
#pytorch #region-us
| Badly trained lightweightgan - ignore | [] | [
"TAGS\n#pytorch #region-us \n"
] |
text-classification | transformers | # Cross-Encoder
This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class.
<p align="center">
<img src="https://upload.wikimedia.org/wikipedia/commons/f/f6/Edouard_Vuillard%2C_1920c_-_Sunlit_Interior.jpg" wid... | {"language": ["it"], "tags": ["cross-encoder", "sentence-similarity", "transformers"], "datasets": ["stsb_multi_mt"], "pipeline_tag": "text-classification"} | efederici/cross-encoder-bert-base-stsb | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"cross-encoder",
"sentence-similarity",
"it",
"dataset:stsb_multi_mt",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-04T15:26:27+00:00 | [] | [
"it"
] | TAGS
#transformers #pytorch #bert #text-classification #cross-encoder #sentence-similarity #it #dataset-stsb_multi_mt #autotrain_compatible #endpoints_compatible #region-us
| # Cross-Encoder
This model was trained using SentenceTransformers Cross-Encoder class.
<p align="center">
<img src="URL width="400"> </br>
Edouard Vuillard, Sunlit Interior
</p>
## Training Data
This model was trained on stsb. The model will predict a score between 0 and 1 how for the semantic similarity of... | [
"# Cross-Encoder\n\nThis model was trained using SentenceTransformers Cross-Encoder class.\n\n<p align=\"center\">\n <img src=\"URL width=\"400\"> </br>\n Edouard Vuillard, Sunlit Interior\n</p>",
"## Training Data\n\nThis model was trained on stsb. The model will predict a score between 0 and 1 how for the... | [
"TAGS\n#transformers #pytorch #bert #text-classification #cross-encoder #sentence-similarity #it #dataset-stsb_multi_mt #autotrain_compatible #endpoints_compatible #region-us \n",
"# Cross-Encoder\n\nThis model was trained using SentenceTransformers Cross-Encoder class.\n\n<p align=\"center\">\n <img src=\"URL... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1447886082163417093/l0n4... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]} | huggingtweets/weirdokun | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-04T15:40:03+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
#LetLeniLead
@weirdokun
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
---------... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-classification | transformers |
This model fine-tuned [ClimateBert](https://huggingface.co/climatebert/distilroberta-base-climate-f) on the textual entailment task using Climate FEVER data. Given (claim, evidence) pairs, the model predicts support (entailment), refute (contradict), or not enough info (neutral). The model has 67% validation accuracy.... | {"language": ["en"], "license": "mit", "tags": ["fact-checking", "climate", "text entailment"], "datasets": "climate_fever"} | amandakonet/climatebert-fact-checking | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"fact-checking",
"climate",
"text entailment",
"en",
"dataset:climate_fever",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-04T15:55:23+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roberta #text-classification #fact-checking #climate #text entailment #en #dataset-climate_fever #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
|
This model fine-tuned ClimateBert on the textual entailment task using Climate FEVER data. Given (claim, evidence) pairs, the model predicts support (entailment), refute (contradict), or not enough info (neutral). The model has 67% validation accuracy.
| [] | [
"TAGS\n#transformers #pytorch #roberta #text-classification #fact-checking #climate #text entailment #en #dataset-climate_fever #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
text-classification | transformers |
Demo model for predicting the polarity of Yelp reviews.
Trained for 1 epoch on 4096 reviews. | {"language": ["en"], "license": "apache-2.0", "tags": ["text-classification", "tag2"], "datasets": ["yelp_polarity"], "metrics": ["accuracy"]} | mgreenbe/607-demo-model | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"tag2",
"en",
"dataset:yelp_polarity",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-04T16:07:37+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #distilbert #text-classification #tag2 #en #dataset-yelp_polarity #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
Demo model for predicting the polarity of Yelp reviews.
Trained for 1 epoch on 4096 reviews. | [] | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #tag2 #en #dataset-yelp_polarity #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
sentence-similarity | sentence-transformers |
# paraphrase-filipino-mpnet-base-v2
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.
This model was trained using the student--teacher approach outlined in [Reimers and ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | meedan/paraphrase-filipino-mpnet-base-v2 | null | [
"sentence-transformers",
"pytorch",
"xlm-roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-04-04T17:06:35+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #xlm-roberta #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# paraphrase-filipino-mpnet-base-v2
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.
This model was trained using the student--teacher approach outlined in Reimers and Gurevych (2020).
The tea... | [
"# paraphrase-filipino-mpnet-base-v2\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.\n\nThis model was trained using the student--teacher approach outlined in Reimers and Gurevych (2020). \... | [
"TAGS\n#sentence-transformers #pytorch #xlm-roberta #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# paraphrase-filipino-mpnet-base-v2\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for... |
text-generation | transformers |
# Peter Griffin DialoGPT Model | {"tags": ["conversational"]} | TropicalJuice/Dialog-PeterGriffin | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-04T17:09:45+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Peter Griffin DialoGPT Model | [
"# Peter Griffin DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Peter Griffin DialoGPT Model"
] |
null | null | Deep Learning for NLP: Training a text classification model to detect fake news articles!
Training and test dataset gotten from https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset
Dataset size = 44898 articles
Training set size = 35918 articles
Test set size = 8980 articles
Accuracy on t... | {"license": "other"} | 2NRC/Fake-New-Classifier | null | [
"license:other",
"region:us"
] | null | 2022-04-04T17:33:31+00:00 | [] | [] | TAGS
#license-other #region-us
| Deep Learning for NLP: Training a text classification model to detect fake news articles!
Training and test dataset gotten from URL
Dataset size = 44898 articles
Training set size = 35918 articles
Test set size = 8980 articles
Accuracy on the training set = 0.990394788128515
Accuracy on the test set = 0.98318... | [] | [
"TAGS\n#license-other #region-us \n"
] |
text-classification | transformers |
The fake news classifer built using distillbert uncased. Created for the Fatima Fellowship coding challenge and trained on P100 instance for 3 epochs. The model is a binary classifier which predicts 1 in case of real news.
Library: transformers \
Language: English \
Dataset: https:\/\/www.kaggle.com/datasets/clm... | {"license": "afl-3.0"} | aswinsson/fake_new_classifier | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-04T17:35:15+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #license-afl-3.0 #autotrain_compatible #endpoints_compatible #region-us
|
The fake news classifer built using distillbert uncased. Created for the Fatima Fellowship coding challenge and trained on P100 instance for 3 epochs. The model is a binary classifier which predicts 1 in case of real news.
Library: transformers \
Language: English \
Dataset: https:\/\/URL | [] | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #license-afl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
image-classification | transformers |
# ConvNext fine-tuned on Eurosat
This model is a `facebook/convnext-tiny-224` model fine-tuned on the [Eurosat dataset](https://github.com/phelber/EuroSAT). | {"license": "apache-2.0", "datasets": ["eurosat"], "widget": [{"src": "forest.png", "example_title": "Forest"}]} | nielsr/convnext-tiny-finetuned-eurostat | null | [
"transformers",
"pytorch",
"convnext",
"image-classification",
"dataset:eurosat",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-04T17:59:04+00:00 | [] | [] | TAGS
#transformers #pytorch #convnext #image-classification #dataset-eurosat #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# ConvNext fine-tuned on Eurosat
This model is a 'facebook/convnext-tiny-224' model fine-tuned on the Eurosat dataset. | [
"# ConvNext fine-tuned on Eurosat\n\nThis model is a 'facebook/convnext-tiny-224' model fine-tuned on the Eurosat dataset."
] | [
"TAGS\n#transformers #pytorch #convnext #image-classification #dataset-eurosat #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# ConvNext fine-tuned on Eurosat\n\nThis model is a 'facebook/convnext-tiny-224' model fine-tuned on the Eurosat dataset."
] |
text2text-generation | 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. -->
# avialfont/dummy-translation-marian-kde4-en-to-fr
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingfac... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "avialfont/dummy-translation-marian-kde4-en-to-fr", "results": []}]} | avialfont/dummy-translation-marian-kde4-en-to-fr | null | [
"transformers",
"tf",
"marian",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-04T18:57:33+00:00 | [] | [] | TAGS
#transformers #tf #marian #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| avialfont/dummy-translation-marian-kde4-en-to-fr
================================================
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-fr on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.9807
* Validation Loss: 0.8658
* Epoch: 0
Model descrip... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 5e-05, 'decay\\_steps': 17733, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle':... | [
"TAGS\n#transformers #tf #marian #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\... |
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. -->
# ProtBert-finetuned-proteinBindingDB
This model is a fine-tuned version of [Rostlab/prot_bert](https://huggingface.co/Rostlab/pro... | {"tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1", "precision", "recall"], "model-index": [{"name": "ProtBert-finetuned-proteinBindingDB", "results": []}]} | nepp1d0/ProtBert-finetuned-proteinBindingDB | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-04T19:33:53+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| ProtBert-finetuned-proteinBindingDB
===================================
This model is a fine-tuned version of Rostlab/prot\_bert on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5764
* Accuracy: 0.885
* F1: 0.8459
* Precision: 0.8255
* Recall: 0.885
Model description
-------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10\n* mixed\\_preci... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 1\n* eval\\_b... |
text-generation | transformers |
#Bot Chat | {"tags": ["conversational"]} | TheGoldenToaster/DialoGPT-medium-Bot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-04T20:25:15+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#Bot Chat | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# DistilBert_Fine_tune_QuestionVsAnswer
This model was trained from scratch on an unknown dataset.
## Model description
More inf... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "DistilBert_Fine_tune_QuestionVsAnswer", "results": []}]} | Ahmedgr/DistilBert_Fine_tune_QuestionVsAnswer | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-04T20:44:01+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# DistilBert_Fine_tune_QuestionVsAnswer
This model was trained from scratch on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
T... | [
"# DistilBert_Fine_tune_QuestionVsAnswer\n\nThis model was trained from scratch on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"#... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"# DistilBert_Fine_tune_QuestionVsAnswer\n\nThis model was trained from scratch on an unknown dataset.",
"## Model description\n\nMore information neede... |
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. -->
# fake-news-fatima-fellowship
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-ba... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "fake-news-fatima-fellowship", "results": []}]} | gagan3012/fake-news-fatima-fellowship | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-04T20:45:24+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| fake-news-fatima-fellowship
===========================
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0000
* Accuracy: 1.0
* F1: 1.0
Model description
-----------------
More information needed
Intended uses... | [
"### 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... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_b... |
text2text-generation | transformers |
<!-- 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. -->
# opus-mt-en-ar-finetunedSTEM-v5-en-to-ar
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ar](https://huggingface.co/Hels... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "opus-mt-en-ar-finetunedSTEM-v5-en-to-ar", "results": []}]} | MaryaAI/opus-mt-en-ar-finetunedSTEM-v5-en-to-ar | null | [
"transformers",
"tf",
"marian",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-04T20:50:43+00:00 | [] | [] | TAGS
#transformers #tf #marian #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| opus-mt-en-ar-finetunedSTEM-v5-en-to-ar
=======================================
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-ar on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0921
* Validation Loss: 8.1798
* Epoch: 4
Model description
-------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': 2e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32",
... | [
"TAGS\n#transformers #tf #marian #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\... |
text-generation | transformers | ```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/MediumInformalToFormalLincoln")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/MediumInformalToFormalLincoln")
```
```
- moviepass to return
- this summer
- swooped up by
- original co-found... | {} | BigSalmon/MediumInformalToFormalLincoln | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-04-04T20:54:23+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
Keywords to sentences or sentence. | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
text-generation | transformers | Trained on this model: https://huggingface.co/xhyi/PT_GPTNEO350_ATG/tree/main
```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/GPTNeo350MInformalToFormalLincoln7")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/GPTNeo350MInformalToFormalLi... | {} | BigSalmon/GPTNeo350MInformalToFormalLincoln7 | null | [
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-04T21:54:00+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt_neo #text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us
| Trained on this model: URL
| [] | [
"TAGS\n#transformers #pytorch #gpt_neo #text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-cnndm_3epoch_v2
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the cnn_... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["cnn_dailymail"], "metrics": ["rouge"], "model-index": [{"name": "t5-small-finetuned-cnndm_3epoch_v2", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "cnn_dailymail", ... | Sevil/t5-small-finetuned-cnndm_3epoch_v2 | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:cnn_dailymail",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-04T22:07:29+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-cnn_dailymail #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-cnndm\_3epoch\_v2
====================================
This model is a fine-tuned version of t5-small on the cnn\_dailymail dataset.
It achieves the following results on the evaluation set:
* Loss: 1.6070
* Rouge1: 24.7696
* Rouge2: 11.9467
* Rougel: 20.4495
* Rougelsum: 23.3341
* Gen Len: 18.999... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3\n* mixed\\_preci... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-cnn_dailymail #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used dur... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-moral-action
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingfa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-moral-action", "results": []}]} | agi-css/distilbert-base-uncased-finetuned-moral-action | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-04T22:52:54+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-moral-action
==============================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4632
* Accuracy: 0.7912
* F1: 0.7912
Model description
-------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 9.716387809233253e-05\n* train\\_batch\\_size: 2000\n* eval\\_batch\\_size: 2000\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epoch... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 9.716387809233253e... |
text-classification | transformers | Demo model trained for 1 epoch on 4096 examples from the `yelp_polarity` dataset. | {} | mgreenbe/607-live-demo-yelp-polarity | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-04T23:21:06+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| Demo model trained for 1 epoch on 4096 examples from the 'yelp_polarity' dataset. | [] | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/951980805542350848/Xx1Lc... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/zei_squirrel/1649119290934/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/zei_squirrel | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-04T23:39:55+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
๏ธ
@zei\_squirrel
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
-------------
... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #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. -->
# nick_asr_v2
This model is a fine-tuned version of [ntoldalagi/nick_asr_v2](https://huggingface.co/ntoldalagi/nick_asr_v2) on an ... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "nick_asr_v2", "results": []}]} | ntoldalagi/nick_asr_v2 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2022-04-04T23:56:55+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #endpoints_compatible #region-us
| nick\_asr\_v2
=============
This model is a fine-tuned version of ntoldalagi/nick\_asr\_v2 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.4562
* Wer: 0.6422
* Cer: 0.2409
Model description
-----------------
More information needed
Intended uses & limitations
--------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_si... |
automatic-speech-recognition | espnet |
## ESPnet2 ASR model
### `espnet/german_commonvoice_blstm`
This model was trained by dzeinali using commonvoice recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 716eb8f92e19708acfd08ba3bd39d40890d3a84b
pip install -e .
cd egs2/commonvoice/asr1
.... | {"language": "de", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["commonvoice"]} | espnet/german_commonvoice_blstm | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"de",
"dataset:commonvoice",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-04-05T00:07:06+00:00 | [
"1804.00015"
] | [
"de"
] | TAGS
#espnet #audio #automatic-speech-recognition #de #dataset-commonvoice #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ESPnet2 ASR model
-----------------
### 'espnet/german\_commonvoice\_blstm'
This model was trained by dzeinali using commonvoice recipe in espnet.
### Demo: How to use in ESPnet2
RESULTS
=======
Environments
------------
* date: 'Mon Apr 4 16:41:54 EDT 2022'
* python version: '3.9.5 (default, Jun 4 2021, 12... | [
"### 'espnet/german\\_commonvoice\\_blstm'\n\n\nThis model was trained by dzeinali using commonvoice recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Mon Apr 4 16:41:54 EDT 2022'\n* python version: '3.9.5 (default, Jun 4 2021, 12:28:51) [GCC ... | [
"TAGS\n#espnet #audio #automatic-speech-recognition #de #dataset-commonvoice #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"### 'espnet/german\\_commonvoice\\_blstm'\n\n\nThis model was trained by dzeinali using commonvoice recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nE... |
null | null | # Self-Distilled StyleGAN
- https://arxiv.org/abs/2202.12211
- https://github.com/self-distilled-stylegan/self-distilled-internet-photos
- weights
- https://storage.googleapis.com/self-distilled-stylegan/dogs_1024_pytorch.pkl
- https://storage.googleapis.com/self-distilled-stylegan/elephants_512_pytorch.pkl
... | {} | public-data/Self-Distilled-StyleGAN | null | [
"arxiv:2202.12211",
"has_space",
"region:us"
] | null | 2022-04-05T00:39:51+00:00 | [
"2202.12211"
] | [] | TAGS
#arxiv-2202.12211 #has_space #region-us
| # Self-Distilled StyleGAN
- URL
- URL
- weights
- URL
- URL
- URL
- URL
- URL
- URL
- URL
| [
"# Self-Distilled StyleGAN\n\n- URL\n- URL\n- weights\n - URL\n - URL\n - URL\n - URL\n - URL\n - URL\n - URL"
] | [
"TAGS\n#arxiv-2202.12211 #has_space #region-us \n",
"# Self-Distilled StyleGAN\n\n- URL\n- URL\n- weights\n - URL\n - URL\n - URL\n - URL\n - URL\n - URL\n - URL"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-moral-ctx-action-conseq
This model is a fine-tuned version of [distilbert-base-uncased](https:... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-moral-ctx-action-conseq", "results": []}]} | agi-css/distilbert-base-uncased-finetuned-moral-ctx-action-conseq | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-05T00:58:12+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-moral-ctx-action-conseq
=========================================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1111
* Accuracy: 0.9676
* F1: 0.9676
Model des... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 9.989502318502869e-05\n* train\\_batch\\_size: 2000\n* eval\\_batch\\_size: 2000\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epoch... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 9.989502318502869e... |
multiple-choice | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-finetuned-swag
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": ["swag"], "metrics": ["accuracy"], "model-index": [{"name": "bert-base-uncased-finetuned-swag", "results": []}]} | jeremykke/bert-base-uncased-finetuned-swag | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"multiple-choice",
"generated_from_trainer",
"dataset:swag",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-05T02:34:21+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #multiple-choice #generated_from_trainer #dataset-swag #license-apache-2.0 #endpoints_compatible #region-us
| bert-base-uncased-finetuned-swag
================================
This model is a fine-tuned version of bert-base-uncased on the swag dataset.
It achieves the following results on the evaluation set:
* Loss: 1.0087
* Accuracy: 0.7911
Model description
-----------------
More information needed
Intended uses & ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-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... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #multiple-choice #generated_from_trainer #dataset-swag #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: 5e-05\n* train\\_batch\\_size: 16\n*... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-toxicity
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.c... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-toxicity", "results": []}]} | agi-css/distilbert-base-uncased-finetuned-toxicity | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-05T04:38:55+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-toxicity
==========================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0086
* Accuracy: 0.999
* F1: 0.9990
Model description
-----------------
Mo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 8.589778712669143e-05\n* train\\_batch\\_size: 400\n* eval\\_batch\\_size: 400\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs:... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 8.589778712669143e... |
token-classification | spacy | Spacy transformer pipeline for Ukrainian language ([XLM-Roberta based](https://huggingface.co/ukr-models/xlm-roberta-base-uk)). Components: transformer, ner, morphologizer, parser.
[Training details](https://github.com/kurnosovv/ukr-spacy) | {"language": ["uk"], "license": "mit", "tags": ["spacy", "token-classification"], "widget": [{"text": "\u041c\u043e\u0433\u0438\u043b\u0430 \u0422\u0430\u0440\u0430\u0441\u0430 \u0428\u0435\u0432\u0447\u0435\u043d\u043a\u0430 \u2014 \u043c\u0456\u0441\u0446\u0435 \u043f\u043e\u0445\u043e\u0432\u0430\u043d\u043d\u044f \... | ukr-models/uk_core_news_trf | null | [
"spacy",
"token-classification",
"uk",
"license:mit",
"model-index",
"region:us"
] | null | 2022-04-05T04:50:26+00:00 | [] | [
"uk"
] | TAGS
#spacy #token-classification #uk #license-mit #model-index #region-us
| Spacy transformer pipeline for Ukrainian language (XLM-Roberta based). Components: transformer, ner, morphologizer, parser.
Training details | [] | [
"TAGS\n#spacy #token-classification #uk #license-mit #model-index #region-us \n"
] |
text2text-generation | 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. -->
# opus-mt-ar-en-finetunedTanzil-v7-ar-to-en
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-ar-en](https://huggingface.co/He... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "opus-mt-ar-en-finetunedTanzil-v7-ar-to-en", "results": []}]} | MaryaAI/opus-mt-ar-en-finetunedTanzil-v7-ar-to-en | null | [
"transformers",
"tf",
"marian",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-05T05:04:57+00:00 | [] | [] | TAGS
#transformers #tf #marian #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| opus-mt-ar-en-finetunedTanzil-v7-ar-to-en
=========================================
This model is a fine-tuned version of Helsinki-NLP/opus-mt-ar-en on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.1919
* Validation Loss: 0.5047
* Train Rouge1: 49.6877
* Train Rouge2: ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': 2e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32",
... | [
"TAGS\n#transformers #tf #marian #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\... |
text-classification | transformers | # Sentiment analysis model | {} | Seethal/sentimentanalysis | null | [
"transformers",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-05T05:06:51+00:00 | [] | [] | TAGS
#transformers #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| # Sentiment analysis model | [
"# Sentiment analysis model"
] | [
"TAGS\n#transformers #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# Sentiment analysis model"
] |
null | null | A lightweightgan trained briefly on https://huggingface.co/datasets/johnowhitaker/colorbs
See https://huggingface.co/johnowhitaker/orbgan_e1 for training script and so on, since this was basically just copying that and running on a new dataset.
Note: lightweightgan code was updated between training orbgan_e1 and this o... | {} | johnowhitaker/colorb_gan | null | [
"pytorch",
"has_space",
"region:us"
] | null | 2022-04-05T05:55:12+00:00 | [] | [] | TAGS
#pytorch #has_space #region-us
| A lightweightgan trained briefly on URL
See URL for training script and so on, since this was basically just copying that and running on a new dataset.
Note: lightweightgan code was updated between training orbgan_e1 and this one, so if you're trying to run the CPU inference notebook you'll get errors. See an updated v... | [] | [
"TAGS\n#pytorch #has_space #region-us \n"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-truthful
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.c... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-truthful", "results": []}]} | agi-css/distilbert-base-uncased-finetuned-truthful | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-05T06:09:11+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-truthful
==========================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4660
* Accuracy: 0.87
* F1: 0.8697
Model description
-----------------
Mor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 9.910294163459086e-05\n* train\\_batch\\_size: 400\n* eval\\_batch\\_size: 400\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs:... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 9.910294163459086e... |
null | null | # Fetima Coding Challenge (Task DL for Vision)
| {} | zigonk/Fetima_CodingChallenge_DL4Vision | null | [
"tensorboard",
"region:us"
] | null | 2022-04-05T07:39:29+00:00 | [] | [] | TAGS
#tensorboard #region-us
| # Fetima Coding Challenge (Task DL for Vision)
| [
"# Fetima Coding Challenge (Task DL for Vision)"
] | [
"TAGS\n#tensorboard #region-us \n",
"# Fetima Coding Challenge (Task DL for Vision)"
] |
null | null |
Repository for Challenge 1 of Fatima Fellowship Quick Coding Challenge
Notebook for Challenge 1 and Challenge 4 is here: https://colab.research.google.com/drive/1oaglh1tOybYyedlT57TnYMMdNe9PiDc_?usp=sharing | {"license": "apache-2.0"} | DaoistKalki/upside_down_detector | null | [
"tensorboard",
"license:apache-2.0",
"region:us"
] | null | 2022-04-05T08:07:17+00:00 | [] | [] | TAGS
#tensorboard #license-apache-2.0 #region-us
|
Repository for Challenge 1 of Fatima Fellowship Quick Coding Challenge
Notebook for Challenge 1 and Challenge 4 is here: URL | [] | [
"TAGS\n#tensorboard #license-apache-2.0 #region-us \n"
] |
null | null | # MobileStyleGAN
- https://arxiv.org/abs/2104.04767
- https://github.com/bes-dev/MobileStyleGAN.pytorch
- weights
- https://drive.google.com/uc?id=11Kja0XGE8liLb6R5slNZjF3j3v_6xydt
- https://drive.google.com/uc?id=1Pes8TiRdxcJcGMNuQ66vNkNmnSsaY2Pl
- https://drive.google.com/uc?id=1vzGGISwCXix73emSFAi62nckH... | {} | public-data/MobileStyleGAN | null | [
"arxiv:2104.04767",
"region:us",
"has_space"
] | null | 2022-04-05T08:10:39+00:00 | [
"2104.04767"
] | [] | TAGS
#arxiv-2104.04767 #region-us #has_space
| # MobileStyleGAN
- URL
- URL
- weights
- URL
- URL
- URL
| [
"# MobileStyleGAN\n\n- URL\n- URL\n- weights\n - URL\n - URL\n - URL"
] | [
"TAGS\n#arxiv-2104.04767 #region-us #has_space \n",
"# MobileStyleGAN\n\n- URL\n- URL\n- weights\n - URL\n - URL\n - URL"
] |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 705021428
- CO2 Emissions (in grams): 10.03748863138583
## Validation Metrics
- Loss: 0.5534441471099854
- Accuracy: 0.768964665184087
- Macro F1: 0.7629008163259284
- Micro F1: 0.768964665184087
- Weighted F1: 0.7685397042536148... | {"language": "unk", "tags": "autotrain", "datasets": ["ramnika003/autotrain-data-sentiment_analysis_project"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 10.03748863138583} | ramnika003/autotrain-sentiment_analysis_project-705021428 | null | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"autotrain",
"unk",
"dataset:ramnika003/autotrain-data-sentiment_analysis_project",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-05T08:17:50+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #xlm-roberta #text-classification #autotrain #unk #dataset-ramnika003/autotrain-data-sentiment_analysis_project #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 705021428
- CO2 Emissions (in grams): 10.03748863138583
## Validation Metrics
- Loss: 0.5534441471099854
- Accuracy: 0.768964665184087
- Macro F1: 0.7629008163259284
- Micro F1: 0.768964665184087
- Weighted F1: 0.7685397042536148... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 705021428\n- CO2 Emissions (in grams): 10.03748863138583",
"## Validation Metrics\n\n- Loss: 0.5534441471099854\n- Accuracy: 0.768964665184087\n- Macro F1: 0.7629008163259284\n- Micro F1: 0.768964665184087\n- Weighted F1: ... | [
"TAGS\n#transformers #pytorch #xlm-roberta #text-classification #autotrain #unk #dataset-ramnika003/autotrain-data-sentiment_analysis_project #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 70... |
image-to-image | pytorch |
# AnimeBackgroundGAN (CartoonGAN by Chen et. al.)
<img src="https://m.media-amazon.com/images/M/MV5BZTExN2EwMmYtNDcwZS00ZmI1LTk1NGQtNTQ3YWFjMmY3YjQwXkEyXkFqcGdeQXVyNTU1OTUzNDg@._V1_.jpg" alt="5 Centimetres per Second directed by Makoto Shinkai" style="height: 300px;"/>
- [Makoto Shinkai ๏ผๆฐๆตท่ช ๏ผ](https://en.wikipe... | {"license": "mit", "library_name": "pytorch", "tags": ["gan", "image-to-image"]} | akiyamasho/AnimeBackgroundGAN-Shinkai | null | [
"pytorch",
"gan",
"image-to-image",
"license:mit",
"has_space",
"region:us"
] | null | 2022-04-05T08:34:35+00:00 | [] | [] | TAGS
#pytorch #gan #image-to-image #license-mit #has_space #region-us
|
# AnimeBackgroundGAN (CartoonGAN by Chen et. al.)
<img src="https://m.URL alt="5 Centimetres per Second directed by Makoto Shinkai" style="height: 300px;"/>
- Makoto Shinkai ๏ผๆฐๆตท่ช ๏ผ pre-trained model from CartoonGAN '[Chen et al., CVPR18]'.
- This model can transform real-life photos into Japanese-animation-like... | [
"# AnimeBackgroundGAN (CartoonGAN by Chen et. al.)\r\n\r\n<img src=\"https://m.URL alt=\"5 Centimetres per Second directed by Makoto Shinkai\" style=\"height: 300px;\"/>\r\n\r\n- Makoto Shinkai ๏ผๆฐๆตท่ช ๏ผ pre-trained model from CartoonGAN '[Chen et al., CVPR18]'.\r\n- This model can transform real-life photos into Japan... | [
"TAGS\n#pytorch #gan #image-to-image #license-mit #has_space #region-us \n",
"# AnimeBackgroundGAN (CartoonGAN by Chen et. al.)\r\n\r\n<img src=\"https://m.URL alt=\"5 Centimetres per Second directed by Makoto Shinkai\" style=\"height: 300px;\"/>\r\n\r\n- Makoto Shinkai ๏ผๆฐๆตท่ช ๏ผ pre-trained model from CartoonGAN '[C... |
image-to-image | pytorch |
# AnimeBackgroundGAN-Hosoda (CartoonGAN by Chen et. al.)
<img src="https://m.media-amazon.com/images/M/MV5BYjgxYjk4OTktZjU3Ni00YzE5LTkyMmItMzI4YzY1YTlhNDg2XkEyXkFqcGdeQXVyNzEyMDQ1MDA@._V1_.jpg" alt="Mirai directed by Mamoru Hosoda" style="height: 300px;"/>
- [Mamoru Hosoda๏ผ็ดฐ็ฐๅฎ๏ผ](https://en.wikipedia.org/wiki/Ma... | {"license": "mit", "library_name": "pytorch", "tags": ["gan", "image-to-image"]} | akiyamasho/AnimeBackgroundGAN-Hosoda | null | [
"pytorch",
"gan",
"image-to-image",
"license:mit",
"has_space",
"region:us"
] | null | 2022-04-05T08:37:32+00:00 | [] | [] | TAGS
#pytorch #gan #image-to-image #license-mit #has_space #region-us
|
# AnimeBackgroundGAN-Hosoda (CartoonGAN by Chen et. al.)
<img src="https://m.URL alt="Mirai directed by Mamoru Hosoda" style="height: 300px;"/>
- Mamoru Hosoda๏ผ็ดฐ็ฐๅฎ๏ผ pre-trained model from CartoonGAN '[Chen et al., CVPR18]'.
- This model can transform real-life photos into Japanese-animation-like backgrounds, f... | [
"# AnimeBackgroundGAN-Hosoda (CartoonGAN by Chen et. al.)\r\n\r\n<img src=\"https://m.URL alt=\"Mirai directed by Mamoru Hosoda\" style=\"height: 300px;\"/>\r\n\r\n- Mamoru Hosoda๏ผ็ดฐ็ฐๅฎ๏ผ pre-trained model from CartoonGAN '[Chen et al., CVPR18]'.\r\n- This model can transform real-life photos into Japanese-animation-l... | [
"TAGS\n#pytorch #gan #image-to-image #license-mit #has_space #region-us \n",
"# AnimeBackgroundGAN-Hosoda (CartoonGAN by Chen et. al.)\r\n\r\n<img src=\"https://m.URL alt=\"Mirai directed by Mamoru Hosoda\" style=\"height: 300px;\"/>\r\n\r\n- Mamoru Hosoda๏ผ็ดฐ็ฐๅฎ๏ผ pre-trained model from CartoonGAN '[Chen et al., CVP... |
image-to-image | pytorch |
# AnimeBackgroundGAN-Miyazaki (CartoonGAN by Chen et. al.)
<img src="https://m.media-amazon.com/images/M/MV5BMTM4MTg2MjAzN15BMl5BanBnXkFtZTcwMTk1NzEyNw@@._V1_.jpg" alt="Howl's Moving Castle directed by Hayao Miyazaki" style="height: 300px;"/>
- [Hayao Miyazaki๏ผๅฎฎๅด้งฟ๏ผ](https://en.wikipedia.org/wiki/Hayao_Miyazaki)... | {"license": "mit", "library_name": "pytorch", "tags": ["gan", "image-to-image"]} | akiyamasho/AnimeBackgroundGAN-Miyazaki | null | [
"pytorch",
"gan",
"image-to-image",
"license:mit",
"has_space",
"region:us"
] | null | 2022-04-05T08:38:20+00:00 | [] | [] | TAGS
#pytorch #gan #image-to-image #license-mit #has_space #region-us
|
# AnimeBackgroundGAN-Miyazaki (CartoonGAN by Chen et. al.)
<img src="https://m.URL alt="Howl's Moving Castle directed by Hayao Miyazaki" style="height: 300px;"/>
- Hayao Miyazaki๏ผๅฎฎๅด้งฟ๏ผ pre-trained model from CartoonGAN '[Chen et al., CVPR18]'.
- This model can transform real-life photos into Japanese-animation-... | [
"# AnimeBackgroundGAN-Miyazaki (CartoonGAN by Chen et. al.)\r\n\r\n<img src=\"https://m.URL alt=\"Howl's Moving Castle directed by Hayao Miyazaki\" style=\"height: 300px;\"/>\r\n\r\n- Hayao Miyazaki๏ผๅฎฎๅด้งฟ๏ผ pre-trained model from CartoonGAN '[Chen et al., CVPR18]'.\r\n- This model can transform real-life photos into J... | [
"TAGS\n#pytorch #gan #image-to-image #license-mit #has_space #region-us \n",
"# AnimeBackgroundGAN-Miyazaki (CartoonGAN by Chen et. al.)\r\n\r\n<img src=\"https://m.URL alt=\"Howl's Moving Castle directed by Hayao Miyazaki\" style=\"height: 300px;\"/>\r\n\r\n- Hayao Miyazaki๏ผๅฎฎๅด้งฟ๏ผ pre-trained model from CartoonGAN... |
image-to-image | pytorch |
# AnimeBackgroundGAN (CartoonGAN by Chen et. al.)
<img src="https://m.media-amazon.com/images/M/MV5BNjNjYTRkNGUtMGQ2MS00MTFiLTg0OTEtYTM3MmM1YTY1OTM1XkEyXkFqcGdeQXVyNjc3OTE4Nzk@._V1_.jpg" alt="Paprika directed by Satoshi Kon" style="height: 300px;"/>
- [Satoshi Kon๏ผไปๆ๏ผ](https://en.wikipedia.org/wiki/Satoshi_Kon)... | {"license": "mit", "library_name": "pytorch", "tags": ["gan", "image-to-image"]} | akiyamasho/AnimeBackgroundGAN-Kon | null | [
"pytorch",
"gan",
"image-to-image",
"license:mit",
"has_space",
"region:us"
] | null | 2022-04-05T08:38:47+00:00 | [] | [] | TAGS
#pytorch #gan #image-to-image #license-mit #has_space #region-us
|
# AnimeBackgroundGAN (CartoonGAN by Chen et. al.)
<img src="https://m.URL alt="Paprika directed by Satoshi Kon" style="height: 300px;"/>
- Satoshi Kon๏ผไปๆ๏ผ pre-trained model from CartoonGAN '[Chen et al., CVPR18]'.
- This model can transform real-life photos into Japanese-animation-like backgrounds, following t... | [
"# AnimeBackgroundGAN (CartoonGAN by Chen et. al.)\r\n\r\n<img src=\"https://m.URL alt=\"Paprika directed by Satoshi Kon\" style=\"height: 300px;\"/>\r\n\r\n- Satoshi Kon๏ผไปๆ๏ผ pre-trained model from CartoonGAN '[Chen et al., CVPR18]'.\r\n- This model can transform real-life photos into Japanese-animation-like backgr... | [
"TAGS\n#pytorch #gan #image-to-image #license-mit #has_space #region-us \n",
"# AnimeBackgroundGAN (CartoonGAN by Chen et. al.)\r\n\r\n<img src=\"https://m.URL alt=\"Paprika directed by Satoshi Kon\" style=\"height: 300px;\"/>\r\n\r\n- Satoshi Kon๏ผไปๆ๏ผ pre-trained model from CartoonGAN '[Chen et al., CVPR18]'.\r\n... |
text-classification | transformers | # Sentiment analysis model | {} | Seethal/Distilbert-base-uncased-fine-tuned-service-bc | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-05T10:00:42+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| # Sentiment analysis model | [
"# Sentiment analysis model"
] | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# Sentiment analysis model"
] |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-mlm-ta-local
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/dist... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilbert-base-uncased-mlm-ta-local", "results": []}]} | medhabi/distilbert-base-uncased-mlm-ta-local | null | [
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-05T10:20:38+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-mlm-ta-local
====================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.0658
Model description
-----------------
More information needed
Intended uses & lim... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0\n* mixed\\_pr... | [
"TAGS\n#transformers #pytorch #distilbert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval... |
null | null | Fake news classifier:
The project is about building an NLP algorithm to detect fake News Articles
We use a pretrained model namely Distilbert
| {} | urielnguefack/Fake_News_Classification_with_Distilbert | null | [
"region:us"
] | null | 2022-04-05T11:25:15+00:00 | [] | [] | TAGS
#region-us
| Fake news classifier:
The project is about building an NLP algorithm to detect fake News Articles
We use a pretrained model namely Distilbert
| [] | [
"TAGS\n#region-us \n"
] |
feature-extraction | transformers |
## bert-base-multilingual-cased-segment1
This is a version of multilingual bert (bert-base-multilingual-cased), where the segment embedding of the 1's is copied into the 0's. Yes, that's all there is to it. We have found that this improves performance substantially in low-resource setups for word-level tasks (e.g. a... | {"language": ["multilingual"], "tags": ["hack"], "datasets": ["Wikipedia"]} | robvanderg/bert-base-multilingual-cased-segment1 | null | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"hack",
"multilingual",
"dataset:Wikipedia",
"endpoints_compatible",
"region:us"
] | null | 2022-04-05T11:27:21+00:00 | [] | [
"multilingual"
] | TAGS
#transformers #pytorch #bert #feature-extraction #hack #multilingual #dataset-Wikipedia #endpoints_compatible #region-us
|
## bert-base-multilingual-cased-segment1
This is a version of multilingual bert (bert-base-multilingual-cased), where the segment embedding of the 1's is copied into the 0's. Yes, that's all there is to it. We have found that this improves performance substantially in low-resource setups for word-level tasks (e.g. a... | [
"## bert-base-multilingual-cased-segment1\n\nThis is a version of multilingual bert (bert-base-multilingual-cased), where the segment embedding of the 1's is copied into the 0's. Yes, that's all there is to it. We have found that this improves performance substantially in low-resource setups for word-level tasks (e... | [
"TAGS\n#transformers #pytorch #bert #feature-extraction #hack #multilingual #dataset-Wikipedia #endpoints_compatible #region-us \n",
"## bert-base-multilingual-cased-segment1\n\nThis is a version of multilingual bert (bert-base-multilingual-cased), where the segment embedding of the 1's is copied into the 0's. Ye... |
fill-mask | 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. -->
# distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbe... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "distilbert-base-uncased-finetuned-imdb", "results": []}]} | ViktorDo/distilbert-base-uncased-finetuned-imdb | null | [
"transformers",
"tf",
"distilbert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-05T11:28:13+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation ... | [
"# distilbert-base-uncased-finetuned-imdb\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Trai... | [
"TAGS\n#transformers #tf #distilbert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# distilbert-base-uncased-finetuned-imdb\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.\nIt achieves the followin... |
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. -->
# convnext-tiny-224_flyswot
This model was trained from scratch on the image_folder dataset.
It achieves the following results on ... | {"tags": ["generated_from_trainer"], "datasets": ["image_folder"], "metrics": ["f1"], "model-index": [{"name": "convnext-tiny-224_flyswot", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "image_folder", "type": "image_folder", "args": "default"}, "metrics": [{... | flyswot/convnext-tiny-224_flyswot | null | [
"transformers",
"pytorch",
"coreml",
"onnx",
"safetensors",
"convnext",
"image-classification",
"generated_from_trainer",
"dataset:image_folder",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-05T12:30:32+00:00 | [] | [] | TAGS
#transformers #pytorch #coreml #onnx #safetensors #convnext #image-classification #generated_from_trainer #dataset-image_folder #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| convnext-tiny-224\_flyswot
==========================
This model was trained from scratch on the image\_folder dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5319
* F1: 0.9756
Model description
-----------------
More information needed
Intended uses & limitations
-----------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 666\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_pr... | [
"TAGS\n#transformers #pytorch #coreml #onnx #safetensors #convnext #image-classification #generated_from_trainer #dataset-image_folder #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-german-cased-finetuned-subj_v3
This model is a fine-tuned version of [bert-base-german-cased](https://huggingface.co/b... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-base-german-cased-finetuned-subj_v3", "results": []}]} | tbosse/bert-base-german-cased-finetuned-subj_v3 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-05T12:32:50+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| bert-base-german-cased-finetuned-subj\_v3
=========================================
This model is a fine-tuned version of bert-base-german-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1790
* Precision: 0.1875
* Recall: 0.0079
* F1: 0.0152
* Accuracy: 0.9472
Model... | [
"### 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... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size:... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "model-index": [{"name": "distilbert-base-uncased-finetuned-imdb", "results": []}]} | impawankr/distilbert-base-uncased-finetuned-imdb | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-05T12:35:28+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-imdb
======================================
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
* Loss: 2.4725
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0\n* mixed\\_pr... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train... |
text-classification | transformers | # BERT base model (uncased)
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1810.04805) and first released in
[this repository](https://github.com/google-research/bert). This model is uncased: it does not make a difference
b... | {"language": "en", "license": "apache-2.0", "tags": ["exbert"], "datasets": ["Confidential"]} | xaqren/sentiment_analysis | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"exbert",
"en",
"dataset:Confidential",
"arxiv:1810.04805",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-05T12:46:58+00:00 | [
"1810.04805"
] | [
"en"
] | TAGS
#transformers #pytorch #distilbert #text-classification #exbert #en #dataset-Confidential #arxiv-1810.04805 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| # BERT base model (uncased)
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This model is uncased: it does not make a difference
between english and English.
## Model description
BERT is a transformers model... | [
"# BERT base model (uncased)\n\nPretrained model on English language using a masked language modeling (MLM) objective. It was introduced in\nthis paper and first released in\nthis repository. This model is uncased: it does not make a difference\nbetween english and English.",
"## Model description\n\nBERT is a tr... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #exbert #en #dataset-Confidential #arxiv-1810.04805 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# BERT base model (uncased)\n\nPretrained model on English language using a masked language modeling (MLM) objective. ... |
text-generation | transformers |
# Harry Potter2 DialoGPT Model | {"tags": ["conversational"]} | MrYiRen/DialoGPT-small-harrypotter2 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-05T12:57:10+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Harry Potter2 DialoGPT Model | [
"# Harry Potter2 DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Harry Potter2 DialoGPT Model"
] |
feature-extraction | transformers | ERROR: type should be string, got "\nhttps://github.com/BM-K/Sentence-Embedding-is-all-you-need\n\n# Korean-Sentence-Embedding\n๐ญ Korean sentence embedding repository. You can download the pre-trained models and inference right away, also it provides environments where individuals can train models.\n\n## Quick tour\n```python\nimport torch\nfrom transformers import AutoModel, AutoTokenizer\n\ndef cal_score(a, b):\n if len(a.shape) == 1: a = a.unsqueeze(0)\n if len(b.shape) == 1: b = b.unsqueeze(0)\n\n a_norm = a / a.norm(dim=1)[:, None]\n b_norm = b / b.norm(dim=1)[:, None]\n return torch.mm(a_norm, b_norm.transpose(0, 1)) * 100\n\nmodel = AutoModel.from_pretrained('BM-K/KoSimCSE-roberta')\ntokenizer = AutoTokenizer.from_pretrained('BM-K/KoSimCSE-roberta')\n\nsentences = ['์นํ๊ฐ ๋คํ์ ๊ฐ๋ก ์ง๋ฌ ๋จน์ด๋ฅผ ์ซ๋๋ค.',\n '์นํ ํ ๋ง๋ฆฌ๊ฐ ๋จน์ด ๋ค์์ ๋ฌ๋ฆฌ๊ณ ์๋ค.',\n '์์ญ์ด ํ ๋ง๋ฆฌ๊ฐ ๋๋ผ์ ์ฐ์ฃผํ๋ค.']\n\ninputs = tokenizer(sentences, padding=True, truncation=True, return_tensors=\"pt\")\nembeddings, _ = model(**inputs, return_dict=False)\n\nscore01 = cal_score(embeddings[0][0], embeddings[1][0])\nscore02 = cal_score(embeddings[0][0], embeddings[2][0])\n```\n\n## Performance\n- Semantic Textual Similarity test set results <br>\n\n| Model | AVG | Cosine Pearson | Cosine Spearman | Euclidean Pearson | Euclidean Spearman | Manhattan Pearson | Manhattan Spearman | Dot Pearson | Dot Spearman |\n|------------------------|:----:|:----:|:----:|:----:|:----:|:----:|:----:|:----:|:----:|\n| KoSBERT<sup>โ </sup><sub>SKT</sub> | 77.40 | 78.81 | 78.47 | 77.68 | 77.78 | 77.71 | 77.83 | 75.75 | 75.22 |\n| KoSBERT | 80.39 | 82.13 | 82.25 | 80.67 | 80.75 | 80.69 | 80.78 | 77.96 | 77.90 |\n| KoSRoBERTa | 81.64 | 81.20 | 82.20 | 81.79 | 82.34 | 81.59 | 82.20 | 80.62 | 81.25 |\n| | | | | | | | | |\n| KoSentenceBART | 77.14 | 79.71 | 78.74 | 78.42 | 78.02 | 78.40 | 78.00 | 74.24 | 72.15 |\n| KoSentenceT5 | 77.83 | 80.87 | 79.74 | 80.24 | 79.36 | 80.19 | 79.27 | 72.81 | 70.17 |\n| | | | | | | | | |\n| KoSimCSE-BERT<sup>โ </sup><sub>SKT</sub> | 81.32 | 82.12 | 82.56 | 81.84 | 81.63 | 81.99 | 81.74 | 79.55 | 79.19 |\n| KoSimCSE-BERT | 83.37 | 83.22 | 83.58 | 83.24 | 83.60 | 83.15 | 83.54 | 83.13 | 83.49 |\n| KoSimCSE-RoBERTa | 83.65 | 83.60 | 83.77 | 83.54 | 83.76 | 83.55 | 83.77 | 83.55 | 83.64 |\n| | | | | | | | | | |\n| KoSimCSE-BERT-multitask | 85.71 | 85.29 | 86.02 | 85.63 | 86.01 | 85.57 | 85.97 | 85.26 | 85.93 |\n| KoSimCSE-RoBERTa-multitask | 85.77 | 85.08 | 86.12 | 85.84 | 86.12 | 85.83 | 86.12 | 85.03 | 85.99 |" | {"language": "ko", "tags": ["korean"]} | BM-K/KoSimCSE-roberta | null | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"feature-extraction",
"korean",
"ko",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-05T12:59:27+00:00 | [] | [
"ko"
] | TAGS
#transformers #pytorch #safetensors #roberta #feature-extraction #korean #ko #endpoints_compatible #has_space #region-us
| URL
Korean-Sentence-Embedding
=========================
Korean sentence embedding repository. You can download the pre-trained models and inference right away, also it provides environments where individuals can train models.
Quick tour
----------
Performance
-----------
* Semantic Textual Similarity test set... | [] | [
"TAGS\n#transformers #pytorch #safetensors #roberta #feature-extraction #korean #ko #endpoints_compatible #has_space #region-us \n"
] |
text-generation | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | gulgulglut/DialoGPT-small-Rick | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-05T13:01:12+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"
] |
audio-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. -->
# hubert-base-superb-ks
This model is a fine-tuned version of [facebook/hubert-base-ls960](https://huggingface.co/facebook/hubert-... | {"license": "apache-2.0", "tags": ["audio-classification", "generated_from_trainer"], "datasets": ["superb"], "metrics": ["accuracy"], "model-index": [{"name": "hubert-base-superb-ks", "results": []}]} | Graphcore/hubert-base-superb-ks | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"optimum_graphcore",
"hubert",
"text-classification",
"audio-classification",
"generated_from_trainer",
"dataset:superb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-05T13:16:46+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #optimum_graphcore #hubert #text-classification #audio-classification #generated_from_trainer #dataset-superb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# hubert-base-superb-ks
This model is a fine-tuned version of facebook/hubert-base-ls960 on the superb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0848
- Accuracy: 0.9822
## Model description
More information needed
## Intended uses & limitations
More information needed
## Train... | [
"# hubert-base-superb-ks\n\nThis model is a fine-tuned version of facebook/hubert-base-ls960 on the superb dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.0848\n- Accuracy: 0.9822",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore informati... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #optimum_graphcore #hubert #text-classification #audio-classification #generated_from_trainer #dataset-superb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# hubert-base-superb-ks\n\nThis model is a fine-tuned version of fa... |
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. -->
# scibert_scivocab_uncased-finetuned-ner
This model is a fine-tuned version of [allenai/scibert_scivocab_uncased](https://huggingf... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "scibert_scivocab_uncased-finetuned-ner", "results": []}]} | HenryHXR/scibert_scivocab_uncased-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-05T13:23:14+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# scibert_scivocab_uncased-finetuned-ner
This model is a fine-tuned version of allenai/scibert_scivocab_uncased on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedur... | [
"# scibert_scivocab_uncased-finetuned-ner\n\nThis model is a fine-tuned version of allenai/scibert_scivocab_uncased 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... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"# scibert_scivocab_uncased-finetuned-ner\n\nThis model is a fine-tuned version of allenai/scibert_scivocab_uncased on the None dataset.",
"## Model descript... |
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