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621ffdc036468d709f174338 | 53ecf467267e2824 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 0 | google-bert/bert-base-uncased | google-bert | null | 71,392,969 | 2,805,772,843 | False | 2022-03-02T23:29:04 | 2024-02-19T11:06:12 | transformers | 2,595 | 8 | null | fill-mask | {"parameters": {"F32": 110106428}, "total": 110106428} | [
".gitattributes",
"LICENSE",
"README.md",
"config.json",
"coreml/fill-mask/float32_model.mlpackage/Data/com.apple.CoreML/model.mlmodel",
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... | 86b5e0934494bd15c9632b12f734a8a67f723594 | [
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"tf",
"jax",
"rust",
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"onnx",
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"bert",
"fill-mask",
"exbert",
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"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"license:apache-2.0",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["BertForMaskedLM"], "model_type": "bert", "tokenizer_config": {}} | {
"auto_model": "AutoModelForMaskedLM",
"custom_class": null,
"pipeline_tag": "fill-mask",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["bookcorpus", "wikipedia"], "eval_results": null, "language": "en", "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["exbert"]} | # 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... | null | [
"apache-2.0"
] | [
"bookcorpus",
"wikipedia"
] | [
"en"
] | 110,106,428 | null | null | [
"AutoModelForMaskedLM",
"bert",
"BertForMaskedLM"
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"fill-mask"
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"text"
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"text"
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621ffdc136468d709f180294 | fc29ccd738d51f6b | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 1 | sentence-transformers/all-MiniLM-L6-v2 | sentence-transformers | null | 206,073,068 | 2,406,656,367 | False | 2022-03-02T23:29:05 | 2025-03-06T13:37:44 | sentence-transformers | 4,620 | 21 | null | sentence-similarity | {"parameters": {"I64": 512, "F32": 22713216}, "total": 22713728} | [
".gitattributes",
"1_Pooling/config.json",
"README.md",
"config.json",
"config_sentence_transformers.json",
"data_config.json",
"model.safetensors",
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"onnx/model_O2.onnx",
"onnx/model_O3.onnx",
"onnx/model_O4.onnx",
"onnx/model_qint8_... | c9745ed1d9f207416be6d2e6f8de32d1f16199bf | [
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"pytorch",
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"rust",
"onnx",
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"bert",
"feature-extraction",
"sentence-similarity",
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"en",
"dataset:s2orc",
"dataset:flax-sentence-embeddings/stackexchange_xml",
"dataset:ms_marco",
"dataset:gooaq",
"dataset:yahoo_a... | null | {"architectures": ["BertModel"], "model_type": "bert", "tokenizer_config": {"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}} | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": "feature-extraction",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["s2orc", "flax-sentence-embeddings/stackexchange_xml", "ms_marco", "gooaq", "yahoo_answers_topics", "code_search_net", "search_qa", "eli5", "snli", "multi_nli", "wikihow", "natural_questions", "trivia_qa", "embedding-data/sentence-compression", "embedding-data/flickr30k-captions", "emb... | # all-MiniLM-L6-v2
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](ht... | null | [
"apache-2.0"
] | [
"s2orc",
"flax-sentence-embeddings/stackexchange_xml",
"ms_marco",
"gooaq",
"yahoo_answers_topics",
"code_search_net",
"search_qa",
"eli5",
"snli",
"multi_nli",
"wikihow",
"natural_questions",
"trivia_qa",
"embedding-data/sentence-compression",
"embedding-data/flickr30k-captions",
"emb... | [
"en"
] | 22,713,728 | null | null | [
"BertModel",
"AutoModel",
"bert"
] | [
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
621ffdc136468d709f17ea63 | c97a6495c5454551 | f26f356cd728caa8 | 49,408 | BPE | 48,894 | true | true | true | true | 2 | Sequence | Sequence | ByteLevel | [
"NFC",
"Replace",
"Lowercase"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 2 | openai/clip-vit-large-patch14 | openai | null | 24,178,746 | 1,101,271,075 | False | 2022-03-02T23:29:05 | 2023-09-15T15:49:35 | transformers | 1,978 | 6 | null | zero-shot-image-classification | {"parameters": {"I64": 334, "F32": 427616512}, "total": 427616846} | [
".gitattributes",
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"config.json",
"flax_model.msgpack",
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"preprocessor_config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tf_model.h5",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | 32bd64288804d66eefd0ccbe215aa642df71cc41 | [
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"clip",
"zero-shot-image-classification",
"vision",
"arxiv:2103.00020",
"arxiv:1908.04913",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["CLIPModel"], "model_type": "clip", "tokenizer_config": {"unk_token": {"content": "<|endoftext|>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "bos_token": {"content": "<|startoftext|>", "single_word": false, "lstrip": false, "rstrip": false, "... | {
"auto_model": "AutoModelForZeroShotImageClassification",
"custom_class": null,
"pipeline_tag": "zero-shot-image-classification",
"processor": "AutoProcessor"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": null, "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["vision"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolv... | # Model Card: CLIP
Disclaimer: The model card is taken and modified from the official CLIP repository, it can be found [here](https://github.com/openai/CLIP/blob/main/model-card.md).
## Model Details
The CLIP model was developed by researchers at OpenAI to learn about what contributes to robustness in computer visio... | null | null | null | null | 427,616,846 | null | null | [
"AutoModelForZeroShotImageClassification",
"CLIPModel",
"clip"
] | [
"zero-shot-image-classification"
] | [
"multimodal"
] | [
"text",
"image"
] | [
"logits"
] |
621ffdc136468d709f180297 | 715c95117d4d9069 | 84fb34b2e1636fc5 | 30,527 | WordPiece | null | true | true | true | true | 6 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 3 | sentence-transformers/all-mpnet-base-v2 | sentence-transformers | null | 29,268,568 | 1,158,149,103 | False | 2022-03-02T23:29:05 | 2025-08-19T10:14:25 | sentence-transformers | 1,262 | 4 | null | sentence-similarity | {"parameters": {"I64": 514, "F32": 109486464}, "total": 109486978} | [
".gitattributes",
"1_Pooling/config.json",
"README.md",
"config.json",
"config_sentence_transformers.json",
"data_config.json",
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"onnx/model_qint8_... | e8c3b32edf5434bc2275fc9bab85f82640a19130 | [
"sentence-transformers",
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"safetensors",
"openvino",
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"feature-extraction",
"sentence-similarity",
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"text-embeddings-inference",
"en",
"dataset:s2orc",
"dataset:flax-sentence-embeddings/stackexchange_xml",
"dataset:ms_marco",
"datas... | null | {"architectures": ["MPNetForMaskedLM"], "model_type": "mpnet", "tokenizer_config": {"bos_token": "<s>", "eos_token": "</s>", "sep_token": "</s>", "cls_token": "<s>", "unk_token": "[UNK]", "pad_token": "<pad>", "mask_token": "<mask>"}} | {
"auto_model": "AutoModelForMaskedLM",
"custom_class": null,
"pipeline_tag": "fill-mask",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["s2orc", "flax-sentence-embeddings/stackexchange_xml", "ms_marco", "gooaq", "yahoo_answers_topics", "code_search_net", "search_qa", "eli5", "snli", "multi_nli", "wikihow", "natural_questions", "trivia_qa", "embedding-data/sentence-compression", "embedding-data/flickr30k-captions", "emb... | # all-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.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](h... | null | [
"apache-2.0"
] | [
"s2orc",
"flax-sentence-embeddings/stackexchange_xml",
"ms_marco",
"gooaq",
"yahoo_answers_topics",
"code_search_net",
"search_qa",
"eli5",
"snli",
"multi_nli",
"wikihow",
"natural_questions",
"trivia_qa",
"embedding-data/sentence-compression",
"embedding-data/flickr30k-captions",
"emb... | [
"en"
] | 109,486,978 | null | null | [
"MPNetForMaskedLM",
"AutoModelForMaskedLM",
"mpnet"
] | [
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"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
621ffdc036468d709f17434d | 30094a1eabdbef67 | db117e9883c4d0ab | 50,257 | BPE | 50,000 | false | true | true | true | 1 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 4 | openai-community/gpt2 | openai-community | null | 11,636,493 | 836,599,686 | False | 2022-03-02T23:29:04 | 2024-02-19T10:57:45 | transformers | 3,153 | 22 | null | text-generation | {"parameters": {"F32": 137022720}, "total": 137022720} | [
".gitattributes",
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"onnx/decoder_with_past_model.... | 607a30d783dfa663caf39e06633721c8d4cfcd7e | [
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"doi:10.57967/hf/0039",
"license:mit",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["GPT2LMHeadModel"], "model_type": "gpt2", "tokenizer_config": {}} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": "en", "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["exbert"]} | # GPT-2
Test the whole generation capabilities here: https://transformer.huggingface.co/doc/gpt2-large
Pretrained model on English language using a causal language modeling (CLM) objective. It was introduced in
[this paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_mu... | null | [
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621ffdc036468d709f174369 | 492a9f4f584c75aa | dd84a2b1ff97657d | 250,002 | Unigram | null | true | true | true | true | 5 | Precompiled | Sequence | Metaspace | [
"Precompiled"
] | [
"WhitespaceSplit",
"Metaspace"
] | [
"Metaspace"
] | 5 | FacebookAI/xlm-roberta-large | FacebookAI | null | 6,940,260 | 680,718,648 | False | 2022-03-02T23:29:04 | 2024-02-19T12:48:30 | transformers | 498 | 1 | null | fill-mask | {"parameters": {"F32": 561192082}, "total": 561192082} | [
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XLM-RoBERTa model pre-trained on 2.5TB of filtered CommonCrawl data containing 100 languages. It was introduced in the paper [Unsupervised Cross-lingual Representation Learning at Scale](https://arxiv.org/abs/1911.02116) by Conneau et al. and first released in [this repository](https... | null | [
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621ffdc036468d709f174347 | 53ecf467267e2824 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
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] | 0 | distilbert/distilbert-base-uncased | distilbert | null | 7,004,758 | 627,000,229 | False | 2022-03-02T23:29:04 | 2024-05-06T13:44:53 | transformers | 848 | 5 | null | fill-mask | {"parameters": {"F32": 66985530}, "total": 66985530} | [
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"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1910.01108",
"license:apache-2.0",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["DistilBertForMaskedLM"], "model_type": "distilbert", "tokenizer_config": {}} | {
"auto_model": "AutoModelForMaskedLM",
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"pipeline_tag": "fill-mask",
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} | {"base_model": null, "datasets": ["bookcorpus", "wikipedia"], "eval_results": null, "language": "en", "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["exbert"]} | # DistilBERT base model (uncased)
This model is a distilled version of the [BERT base model](https://huggingface.co/bert-base-uncased). It was
introduced in [this paper](https://arxiv.org/abs/1910.01108). The code for the distillation process can be found
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"da",
"de",
"el",
"en",
"eo",
"es",
"et",
"eu",
"fa",
"fi... | null | {"architectures": ["XLMRobertaForMaskedLM"], "model_type": "xlm-roberta", "tokenizer_config": {}} | {
"auto_model": "AutoModelForMaskedLM",
"custom_class": null,
"pipeline_tag": "fill-mask",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["multilingual", "af", "am", "ar", "as", "az", "be", "bg", "bn", "br", "bs", "ca", "cs", "cy", "da", "de", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "he", "hi", "hr", "hu", "hy", "id", "is", "it", "j... | # XLM-RoBERTa (base-sized model)
XLM-RoBERTa model pre-trained on 2.5TB of filtered CommonCrawl data containing 100 languages. It was introduced in the paper [Unsupervised Cross-lingual Representation Learning at Scale](https://arxiv.org/abs/1911.02116) by Conneau et al. and first released in [this repository](https:... | null | [
"mit"
] | null | [
"multilingual",
"af",
"am",
"ar",
"as",
"az",
"be",
"bg",
"bn",
"br",
"bs",
"ca",
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"da",
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"el",
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"eo",
"es",
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"ga",
"gd",
"gl",
"gu",
"ha",
"he",
"hi",
"hr",
"hu",
"hy",
"id",
"is",
"i... | 278,885,778 | null | null | [
"AutoModelForMaskedLM",
"xlm-roberta",
"XLMRobertaForMaskedLM"
] | [
"fill-mask"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
621ffdc036468d709f174350 | 35f8a93a304f6b78 | 70bd27c212bc339c | 50,265 | BPE | 50,000 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 6 | FacebookAI/roberta-base | FacebookAI | null | 14,703,379 | 575,619,465 | False | 2022-03-02T23:29:04 | 2024-02-19T12:39:28 | transformers | 574 | 1 | null | fill-mask | {"parameters": {"F32": 124697433, "I64": 514}, "total": 124697947} | [
".gitattributes",
"README.md",
"config.json",
"dict.txt",
"flax_model.msgpack",
"merges.txt",
"model.safetensors",
"pytorch_model.bin",
"rust_model.ot",
"tf_model.h5",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | e2da8e2f811d1448a5b465c236feacd80ffbac7b | [
"transformers",
"pytorch",
"tf",
"jax",
"rust",
"safetensors",
"roberta",
"fill-mask",
"exbert",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1907.11692",
"arxiv:1806.02847",
"license:mit",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["RobertaForMaskedLM"], "model_type": "roberta", "tokenizer_config": {}} | {
"auto_model": "AutoModelForMaskedLM",
"custom_class": null,
"pipeline_tag": "fill-mask",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["bookcorpus", "wikipedia"], "eval_results": null, "language": "en", "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["exbert"]} | # RoBERTa base model
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1907.11692) and first released in
[this repository](https://github.com/pytorch/fairseq/tree/master/examples/roberta). This model is case-sensitive: it
make... | null | [
"mit"
] | [
"bookcorpus",
"wikipedia"
] | [
"en"
] | 124,697,947 | null | null | [
"roberta",
"AutoModelForMaskedLM",
"RobertaForMaskedLM"
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"fill-mask"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
64ff2c767a4a6ae49afa72b5 | 53ecf467267e2824 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 0 | BAAI/bge-base-en-v1.5 | BAAI | null | 5,438,497 | 514,491,010 | False | 2023-09-11T15:04:22 | 2024-02-21T03:00:19 | sentence-transformers | 408 | 1 | "[{\"name\": \"bge-base-en-v1.5\", \"results\": [{\"task\": {\"type\": \"Classification\"}, \"datase(...TRUNCATED) | feature-extraction | {"parameters": {"I64": 512, "F32": 109482240}, "total": 109482752} | [".gitattributes","1_Pooling/config.json","README.md","config.json","config_sentence_transformers.js(...TRUNCATED) | a5beb1e3e68b9ab74eb54cfd186867f64f240e1a | ["sentence-transformers","pytorch","onnx","safetensors","bert","feature-extraction","sentence-simila(...TRUNCATED) | null | "{\"architectures\": [\"BertModel\"], \"model_type\": \"bert\", \"tokenizer_config\": {\"cls_token\"(...TRUNCATED) | {"auto_model":"AutoModel","custom_class":null,"pipeline_tag":"feature-extraction","processor":"AutoT(...TRUNCATED) | "{\"language\": [\"en\"], \"license\": \"mit\", \"tags\": [\"sentence-transformers\", \"feature-extr(...TRUNCATED) | "<h1 align=\"center\">FlagEmbedding</h1>\n\n\n<h4 align=\"center\">\n <p>\n <a href=#model(...TRUNCATED) | null | [
"mit"
] | null | [
"en"
] | 109,482,752 | null | null | [
"BertModel",
"AutoModel",
"bert"
] | [
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
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