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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 | 2022-03-02T23:29:04Z | 2024-02-19T11:06:12Z | 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",
"coreml/fill-mask/float32_model.mlpackage/Data/com.apple.CoreML/weights/weight.bin",
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"flax_model.msgpack",
... | 86b5e0934494bd15c9632b12f734a8a67f723594 | [
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"onnx",
"safetensors",
"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"
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"en"
] | 110,106,428 | null | null | [
"AutoModelForMaskedLM",
"bert",
"BertForMaskedLM"
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"fill-mask"
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621ffdc136468d709f180294 | fc29ccd738d51f6b | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
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"BertPreTokenizer"
] | [
"WordPiece"
] | 1 | sentence-transformers/all-MiniLM-L6-v2 | sentence-transformers | null | 206,073,068 | 2,406,656,367 | False | 2022-03-02T23:29:05Z | 2025-03-06T13:37:44Z | 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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"rust",
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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 | 2022-03-02T23:29:05Z | 2023-09-15T15:49:35Z | transformers | 1,978 | 6 | null | zero-shot-image-classification | {"parameters": {"I64": 334, "F32": 427616512}, "total": 427616846} | [
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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 | 2022-03-02T23:29:05Z | 2025-08-19T10:14:25Z | sentence-transformers | 1,262 | 4 | null | sentence-similarity | {"parameters": {"I64": 514, "F32": 109486464}, "total": 109486978} | [
".gitattributes",
"1_Pooling/config.json",
"README.md",
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"data_config.json",
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"onnx/model_qint8_... | e8c3b32edf5434bc2275fc9bab85f82640a19130 | [
"sentence-transformers",
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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 | 2022-03-02T23:29:04Z | 2024-02-19T10:57:45Z | 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"
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"WhitespaceSplit",
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] | [
"Metaspace"
] | 5 | FacebookAI/xlm-roberta-large | FacebookAI | null | 6,940,260 | 680,718,648 | False | 2022-03-02T23:29:04Z | 2024-02-19T12:48:30Z | 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 | 2022-03-02T23:29:04Z | 2024-05-06T13:44:53Z | transformers | 848 | 5 | null | fill-mask | {"parameters": {"F32": 66985530}, "total": 66985530} | [
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"tf_model.h5",
"tokenizer.json",
"tokenizer_config.json",
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"transformers",
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"en",
"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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"ca",
"cs",
"cy",
"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",
"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",
"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 | 2022-03-02T23:29:04Z | 2024-02-19T12:39:28Z | 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"
] | [
"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 | 2023-09-11T15:04:22Z | 2024-02-21T03:00:19Z | sentence-transformers | 408 | 1 | [{"name": "bge-base-en-v1.5", "results": [{"task": {"type": "Classification"}, "dataset": {"type": "mteb/amazon_counterfactual", "name": "MTEB AmazonCounterfactualClassification (en)", "config": "en", "split": "test", "revision": "e8379541af4e31359cca9fbcf4b00f2671dba205"}, "metrics": [{"type": "accuracy", "value": 76.... | feature-extraction | {"parameters": {"I64": 512, "F32": 109482240}, "total": 109482752} | [
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"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
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"sentence-transformers",
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"arxiv:2309.07597",
"license:mit",
"model-index",
"text-embe... | null | {"architectures": ["BertModel"], "model_type": "bert", "tokenizer_config": {"cls_token": "[CLS]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": "feature-extraction",
"processor": "AutoTokenizer"
} | {"language": ["en"], "license": "mit", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers", "mteb"], "model-index": [{"name": "bge-base-en-v1.5", "results": [{"task": {"type": "Classification"}, "dataset": {"name": "MTEB AmazonCounterfactualClassification (en)", "type": "mteb/a... | <h1 align="center">FlagEmbedding</h1>
<h4 align="center">
<p>
<a href=#model-list>Model List</a> |
<a href=#frequently-asked-questions>FAQ</a> |
<a href=#usage>Usage</a> |
<a href="#evaluation">Evaluation</a> |
<a href="#train">Train</a> |
<a href="#contact">Conta... | null | [
"mit"
] | null | [
"en"
] | 109,482,752 | null | null | [
"BertModel",
"AutoModel",
"bert"
] | [
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
621ffdc036468d709f174353 | 35f8a93a304f6b78 | 70bd27c212bc339c | 50,265 | BPE | 50,000 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 6 | FacebookAI/roberta-large | FacebookAI | null | 20,366,973 | 578,685,726 | False | 2022-03-02T23:29:04Z | 2024-02-19T12:47:04Z | transformers | 269 | 1 | null | fill-mask | {"parameters": {"F32": 355412057, "I64": 514}, "total": 355412571} | [
".gitattributes",
"README.md",
"config.json",
"flax_model.msgpack",
"merges.txt",
"model.onnx",
"model.safetensors",
"pytorch_model.bin",
"tf_model.h5",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | 722cf37b1afa9454edce342e7895e588b6ff1d59 | [
"transformers",
"pytorch",
"tf",
"jax",
"onnx",
"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 large 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... | null | [
"mit"
] | [
"bookcorpus",
"wikipedia"
] | [
"en"
] | 355,412,571 | null | null | [
"roberta",
"AutoModelForMaskedLM",
"RobertaForMaskedLM"
] | [
"fill-mask"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
621ffdc136468d709f17ea62 | f56392a3c3b561ef | f26f356cd728caa8 | 49,408 | BPE | 48,894 | true | true | true | true | 2 | Sequence | Sequence | ByteLevel | [
"NFC",
"Replace",
"Lowercase"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 7 | openai/clip-vit-base-patch32 | openai | null | 20,480,777 | 550,794,862 | False | 2022-03-02T23:29:05Z | 2024-02-29T09:45:55Z | transformers | 890 | 2 | null | zero-shot-image-classification | null | [
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"pytorch_model.bin",
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"tf_model.h5",
"tokenizer.json",
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] | 3d74acf9a28c67741b2f4f2ea7635f0aaf6f0268 | [
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"tf",
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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 | null | null | null | [
"AutoModelForZeroShotImageClassification",
"CLIPModel",
"clip"
] | [
"zero-shot-image-classification"
] | [
"multimodal"
] | [
"text",
"image"
] | [
"logits"
] |
621ffdc136468d709f17b61d | 53ecf467267e2824 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 0 | google/electra-base-discriminator | google | null | 50,954,761 | 616,352,339 | False | 2022-03-02T23:29:05Z | 2024-02-29T10:20:20Z | transformers | 86 | 1 | null | null | null | [
".gitattributes",
"README.md",
"config.json",
"flax_model.msgpack",
"pytorch_model.bin",
"rust_model.ot",
"tf_model.h5",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | 1ae76a97c7e84a4e640876a07453fccd636f0667 | [
"transformers",
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"tf",
"jax",
"rust",
"electra",
"pretraining",
"en",
"arxiv:1406.2661",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["ElectraForPreTraining"], "model_type": "electra", "tokenizer_config": {}} | {
"auto_model": "AutoModelForPreTraining",
"custom_class": null,
"pipeline_tag": "pretraining",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "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": null, "thumbnail": "https://huggingface.co/front/thumbnails/google.png"} | ## ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators
**ELECTRA** is a new method for self-supervised language representation learning. It can be used to pre-train transformer networks using relatively little compute. ELECTRA models are trained to distinguish "real" input tokens vs "fake" inp... | null | [
"apache-2.0"
] | null | [
"en"
] | null | null | null | [
"ElectraForPreTraining",
"AutoModelForPreTraining",
"electra"
] | [
"pretraining"
] | null | null | null |
621ffdc036468d709f174331 | 634a9186a6003172 | 5f9f736c7dc6fbfa | 28,996 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 8 | google-bert/bert-base-cased | google-bert | null | 4,641,471 | 305,980,111 | False | 2022-03-02T23:29:04Z | 2024-02-19T11:02:26Z | transformers | 353 | 1 | null | fill-mask | {"parameters": {"F32": 108932934}, "total": 108932934} | [
".gitattributes",
"README.md",
"config.json",
"flax_model.msgpack",
"model.safetensors",
"pytorch_model.bin",
"tf_model.h5",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | cd5ef92a9fb2f889e972770a36d4ed042daf221e | [
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"exbert",
"en",
"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 (cased)
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 case-sensitive: it makes a difference betw... | null | [
"apache-2.0"
] | [
"bookcorpus",
"wikipedia"
] | [
"en"
] | 108,932,934 | null | null | [
"AutoModelForMaskedLM",
"bert",
"BertForMaskedLM"
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"fill-mask"
] | [
"text"
] | [
"text"
] | [
"logits"
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621ffdc036468d709f174348 | 30094a1eabdbef67 | db117e9883c4d0ab | 50,257 | BPE | 50,000 | false | true | true | true | 1 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 4 | distilbert/distilgpt2 | distilbert | null | 2,665,174 | 238,503,650 | False | 2022-03-02T23:29:04Z | 2024-02-19T11:09:53Z | transformers | 620 | 1 | [{"name": "distilgpt2", "results": [{"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"type": "wikitext", "name": "WikiText-103"}, "metrics": [{"type": "perplexity", "name": "Perplexity", "value": 21.1, "verified": false}]}]}] | text-generation | {"parameters": {"F32": 88204032}, "total": 88204032} | [
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"c... | 2290a62682d06624634c1f46a6ad5be0f47f38aa | [
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"arxiv:2203.12574",
"arxiv:1910.09700",
"arxiv:1503.02531",
"license:apache-2.0",
"model-... | null | {"architectures": ["GPT2LMHeadModel"], "model_type": "gpt2", "tokenizer_config": {}} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"datasets": ["openwebtext"], "language": "en", "license": "apache-2.0", "tags": ["exbert"], "co2_eq_emissions": 149200, "model-index": [{"name": "distilgpt2", "results": [{"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "WikiText-103", "type": "wikitext"}, "metrics": [{"type": "perp... | # DistilGPT2
DistilGPT2 (short for Distilled-GPT2) is an English-language model pre-trained with the supervision of the smallest version of Generative Pre-trained Transformer 2 (GPT-2). Like GPT-2, DistilGPT2 can be used to generate text. Users of this model card should also consider information about the design, trai... | null | [
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] | [
"openwebtext"
] | [
"en"
] | 88,204,032 | null | null | [
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"text"
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"text"
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621ffdc036468d709f174336 | 71140a663949f258 | 8a01c02d4d0a401c | 119,547 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
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"BertPreTokenizer"
] | [
"WordPiece"
] | 9 | google-bert/bert-base-multilingual-cased | google-bert | null | 3,717,654 | 242,245,368 | False | 2022-03-02T23:29:04Z | 2024-02-19T11:05:41Z | transformers | 580 | 0 | null | fill-mask | {"parameters": {"F32": 178566653}, "total": 178566653} | [
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"tf_model.h5",
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"tokenizer_config.json",
"vocab.txt"
] | 3f076fdb1ab68d5b2880cb87a0886f315b8146f8 | [
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"multilingual",
"af",
"sq",
"ar",
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"hy",
"ast",
"az",
"ba",
"eu",
"bar",
"be",
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"br",
"bg",
"my",
"ca",
"ceb",
"ce",
"zh",
"cv",
"hr",
"cs",
"da",
"nl"... | null | {"architectures": ["BertForMaskedLM"], "model_type": "bert", "tokenizer_config": {}} | {
"auto_model": "AutoModelForMaskedLM",
"custom_class": null,
"pipeline_tag": "fill-mask",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["wikipedia"], "eval_results": null, "language": ["multilingual", "af", "sq", "ar", "an", "hy", "ast", "az", "ba", "eu", "bar", "be", "bn", "inc", "bs", "br", "bg", "my", "ca", "ceb", "ce", "zh", "cv", "hr", "cs", "da", "nl", "en", "et", "fi", "fr", "gl", "ka", "de", "el", "gu", "ht", "... | # BERT multilingual base model (cased)
Pretrained model on the top 104 languages with the largest Wikipedia 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 ... | null | [
"apache-2.0"
] | [
"wikipedia"
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"et",
"fi",
"fr",
"gl",
"ka",
"de",
"el",
"gu",
"ht",
"he",
... | 178,566,653 | null | null | [
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"text"
] | [
"logits"
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621ffdc136468d709f17ea61 | 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-base-patch16 | openai | null | 1,898,842 | 229,348,495 | False | 2022-03-02T23:29:05Z | 2022-10-04T09:42:28Z | transformers | 153 | 0 | null | zero-shot-image-classification | null | [
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"preprocessor_config.json",
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"zero-shot-image-classification",
"vision",
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"endpoints_compatible",
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] | 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 vision... | null | null | null | null | null | null | null | [
"AutoModelForZeroShotImageClassification",
"CLIPModel",
"clip"
] | [
"zero-shot-image-classification"
] | [
"multimodal"
] | [
"text",
"image"
] | [
"logits"
] |
621ffdc136468d709f180292 | fc29ccd738d51f6b | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 1 | sentence-transformers/all-MiniLM-L12-v2 | sentence-transformers | null | 4,020,003 | 230,513,472 | False | 2022-03-02T23:29:05Z | 2025-12-16T07:37:40Z | sentence-transformers | 299 | 0 | null | sentence-similarity | {"parameters": {"I64": 512, "F32": 33360000}, "total": 33360512} | [
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"feature-extraction",
"sentence-similarity",
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"dataset:s2orc",
"dataset:flax-sentence-embeddings/stackexchange_xml",
"dataset:ms_marco",
"dataset:gooaq",
"dataset:yahoo_answers_t... | null | {"architectures": ["BertModel"], "model_type": "bert", "tokenizer_config": {"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}} | {
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"custom_class": null,
"pipeline_tag": "feature-extraction",
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} | {"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-L12-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](h... | null | [
"apache-2.0"
] | [
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"yahoo_answers_topics",
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"multi_nli",
"wikihow",
"natural_questions",
"trivia_qa",
"embedding-data/sentence-compression",
"embedding-data/flickr30k-captions",
"emb... | [
"en"
] | 33,360,512 | null | null | [
"BertModel",
"AutoModel",
"bert"
] | [
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
621ffdc136468d709f1802ec | aed290a0124eb522 | dd84a2b1ff97657d | 250,002 | Unigram | null | true | true | true | true | 5 | Precompiled | Sequence | Metaspace | [
"Precompiled"
] | [
"WhitespaceSplit",
"Metaspace"
] | [
"Metaspace"
] | 10 | sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 | sentence-transformers | null | 26,743,180 | 314,376,005 | False | 2022-03-02T23:29:05Z | 2026-01-28T10:02:26Z | sentence-transformers | 1,172 | 6 | null | sentence-similarity | {"parameters": {"I64": 512, "F32": 117653760}, "total": 117654272} | [
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"fi",
"fr",
"gl",
"gu",
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"hi"... | null | {"architectures": ["BertModel"], "model_type": "bert", "tokenizer_config": {"unk_token": "<unk>", "sep_token": "</s>", "pad_token": "<pad>", "cls_token": "<s>", "mask_token": {"content": "<mask>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "bos_token": "<s>", "eo... | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": "feature-extraction",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["multilingual", "ar", "bg", "ca", "cs", "da", "de", "el", "en", "es", "et", "fa", "fi", "fr", "gl", "gu", "he", "hi", "hr", "hu", "hy", "id", "it", "ja", "ka", "ko", "ku", "lt", "lv", "mk", "mn", "mr", "ms", "my", "nb", "nl", "pl", "pt", "ro", "r... | # sentence-transformers/paraphrase-multilingual-MiniLM-L12-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 become... | null | [
"apache-2.0"
] | null | [
"multilingual",
"ar",
"bg",
"ca",
"cs",
"da",
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"mr",
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"my",
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"nl",
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"pt",
"r... | 117,654,272 | null | null | [
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6262c267cbebf7e1ac29e364 | f56392a3c3b561ef | f26f356cd728caa8 | 49,408 | BPE | 48,894 | true | true | true | true | 2 | Sequence | Sequence | ByteLevel | [
"NFC",
"Replace",
"Lowercase"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 7 | openai/clip-vit-large-patch14-336 | openai | null | 8,663,430 | 189,784,860 | False | 2022-04-22T14:57:43Z | 2022-10-04T09:41:39Z | transformers | 290 | 1 | [{"name": "clip-vit-large-patch14-336", "results": []}] | zero-shot-image-classification | null | [
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"config.json",
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"pytorch_model.bin",
"special_tokens_map.json",
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"tokenizer.json",
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"tf",
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] | 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, "... | {
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"pipeline_tag": "zero-shot-image-classification",
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} | {"base_model": null, "datasets": null, "eval_results": [], "language": null, "library_name": null, "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": "clip-vit-large-patch14-336", "pipeline_tag": null, "tags": ["generated_from_keras_callback"], "widget": [{"src": "https://huggin... | <!-- 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. -->
# clip-vit-large-patch14-336
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluati... | null | null | null | null | null | null | null | [
"AutoModelForZeroShotImageClassification",
"CLIPModel",
"clip"
] | [
"zero-shot-image-classification"
] | [
"multimodal"
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"image"
] | [
"logits"
] |
621ffdc036468d709f174358 | 8a8c94e1a59f5186 | ef4806e2ade9022f | 32,100 | Unigram | null | true | true | true | true | 103 | Precompiled | Sequence | Metaspace | [
"Precompiled"
] | [
"WhitespaceSplit",
"Metaspace"
] | [
"Metaspace"
] | 11 | google-t5/t5-small | google-t5 | null | 1,813,634 | 174,084,827 | False | 2022-03-02T23:29:04Z | 2023-06-30T02:31:26Z | transformers | 536 | 3 | null | translation | {"parameters": {"F32": 60506880}, "total": 60506880} | [
".gitattributes",
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"onnx/decoder_model_merged_quantized.onnx",
"onnx/decoder_model_quantized.onnx",
"onnx/decoder_with_past_model.onnx",
... | df1b051c49625cf57a3d0d8d3863ed4d13564fe4 | [
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"dataset:c4",
"arxiv:1805.12471",
"arxiv:1708.00055",
"arxiv:1704.05426",
"arxiv:1606.05250",
"arxiv:... | null | {"architectures": ["T5ForConditionalGeneration"], "model_type": "t5", "tokenizer_config": {"eos_token": "</s>", "pad_token": "<pad>", "unk_token": "<unk>"}} | {
"auto_model": "AutoModelForSeq2SeqLM",
"custom_class": null,
"pipeline_tag": "text2text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["c4"], "eval_results": null, "language": ["en", "fr", "ro", "de", "multilingual"], "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["summarization", "translation"]} | # Model Card for T5 Small
# Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3. ... | null | [
"apache-2.0"
] | [
"c4"
] | [
"en",
"fr",
"ro",
"de",
"multilingual"
] | 60,506,880 | null | null | [
"t5",
"T5ForConditionalGeneration",
"AutoModelForSeq2SeqLM"
] | [
"text2text-generation",
"translation",
"summarization"
] | [
"text"
] | [
"text"
] | [
"text"
] |
621ffdc036468d709f174329 | a3d54d6c1d5c62b4 | f87d7bfce4fd9617 | 30,000 | Unigram | null | true | true | true | true | 5 | Sequence | Sequence | Metaspace | [
"Replace",
"Replace",
"NFKD",
"StripAccents",
"Lowercase",
"Precompiled"
] | [
"WhitespaceSplit",
"Metaspace"
] | [
"Metaspace"
] | 12 | albert/albert-base-v2 | albert | null | 629,246 | 161,471,707 | False | 2022-03-02T23:29:04Z | 2024-02-19T10:58:14Z | transformers | 140 | 0 | null | fill-mask | {"parameters": {"F32": 11842272}, "total": 11842272} | [
".gitattributes",
"README.md",
"config.json",
"flax_model.msgpack",
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"pytorch_model.bin",
"rust_model.ot",
"spiece.model",
"tf_model.h5",
"tokenizer.json",
"tokenizer_config.json",
"with-prefix-tf_model.h5"
] | 8e2f239c5f8a2c0f253781ca60135db913e5c80c | [
"transformers",
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"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"license:apache-2.0",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["AlbertForMaskedLM"], "model_type": "albert", "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": null} | # ALBERT Base v2
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1909.11942) and first released in
[this repository](https://github.com/google-research/albert). This model, as all ALBERT models, is uncased: it does not make ... | null | [
"apache-2.0"
] | [
"bookcorpus",
"wikipedia"
] | [
"en"
] | 11,842,272 | null | null | [
"albert",
"AutoModelForMaskedLM",
"AlbertForMaskedLM"
] | [
"fill-mask"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
621ffdc036468d709f174356 | 8a8c94e1a59f5186 | ef4806e2ade9022f | 32,100 | Unigram | null | true | true | true | true | 103 | Precompiled | Sequence | Metaspace | [
"Precompiled"
] | [
"WhitespaceSplit",
"Metaspace"
] | [
"Metaspace"
] | 11 | google-t5/t5-base | google-t5 | null | 1,828,271 | 158,702,634 | False | 2022-03-02T23:29:04Z | 2024-02-14T17:21:55Z | transformers | 770 | 0 | null | translation | null | [
".gitattributes",
"README.md",
"config.json",
"flax_model.msgpack",
"generation_config.json",
"model.safetensors",
"pytorch_model.bin",
"rust_model.ot",
"spiece.model",
"tf_model.h5",
"tokenizer.json"
] | a9723ea7f1b39c1eae772870f3b547bf6ef7e6c1 | [
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"arxiv:1708.00055",
"arxiv:1704.05426",
"arxiv:1606.05250",
"arxiv:1808.09121",
"arxiv:1810.1... | null | {"architectures": ["T5ForConditionalGeneration"], "model_type": "t5"} | {
"auto_model": "AutoModelForSeq2SeqLM",
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"pipeline_tag": "text2text-generation",
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} | {"base_model": null, "datasets": ["c4"], "eval_results": null, "language": ["en", "fr", "ro", "de"], "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "translation", "tags": ["summarization", "translation"]} | # Model Card for T5 Base
# Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3. [... | null | [
"apache-2.0"
] | [
"c4"
] | [
"en",
"fr",
"ro",
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621ffdc136468d709f17e2c1 | 41a000f2f9024404 | 70bd27c212bc339c | 50,265 | BPE | 50,000 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 13 | mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis | mrm8488 | null | 275,177 | 145,087,903 | False | 2022-03-02T23:29:05Z | 2024-01-21T15:17:58Z | transformers | 448 | 1 | [{"name": "distilRoberta-financial-sentiment", "results": [{"task": {"name": "Text Classification", "type": "text-classification"}, "dataset": {"name": "financial_phrasebank", "type": "financial_phrasebank", "args": "sentences_allagree"}, "metrics": [{"name": "Accuracy", "type": "accuracy", "value": 0.9823008849557522,... | text-classification | {"parameters": {"I64": 514, "F32": 82120707}, "total": 82121221} | [
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"runs/Sep16_18-26-05_ed005835f859/events.out.tfevents.1631816775.ed005835f85... | ae0eab9ad336d7d548e0efe394b07c04bcaf6e91 | [
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"endpoints_compatible",
"deploy:azu... | null | {"architectures": ["RobertaForSequenceClassification"], "model_type": "roberta", "tokenizer_config": {"unk_token": "<unk>", "bos_token": "<s>", "eos_token": "</s>", "sep_token": "</s>", "cls_token": "<s>", "pad_token": "<pad>", "mask_token": "<mask>"}} | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
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} | {"datasets": ["financial_phrasebank"], "license": "apache-2.0", "metrics": ["accuracy"], "tags": ["generated_from_trainer", "financial", "stocks", "sentiment"], "thumbnail": "https://huggingface.co/mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis/resolve/main/logo_no_bg.png", "widget": [{"text": "Opera... | <!-- 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. -->
<div style="text-align:center;width:250px;height:250px;">
<img src="https://huggingface.co/mrm8488/distilroberta-finetuned-fina... | null | [
"apache-2.0"
] | [
"financial_phrasebank"
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] | [
"text"
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"logits"
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621ffdc036468d709f174332 | 086036148c53e78f | d965e4a9140ecf72 | 21,128 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 14 | google-bert/bert-base-chinese | google-bert | null | 3,273,190 | 139,639,994 | False | 2022-03-02T23:29:04Z | 2025-07-03T11:58:48Z | transformers | 1,403 | 3 | null | fill-mask | null | [
".gitattributes",
"README.md",
"config.json",
"flax_model.msgpack",
"model.safetensors",
"pytorch_model.bin",
"tf_model.h5",
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"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": null, "eval_results": null, "language": "zh", "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | null | null | [
"apache-2.0"
] | null | [
"zh"
] | null | null | null | [
"AutoModelForMaskedLM",
"bert",
"BertForMaskedLM"
] | [
"fill-mask"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
621ffdc136468d709f1802e5 | 53ecf467267e2824 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 0 | sentence-transformers/paraphrase-MiniLM-L6-v2 | sentence-transformers | null | 4,252,661 | 143,660,455 | False | 2022-03-02T23:29:05Z | 2025-03-06T13:26:35Z | sentence-transformers | 145 | 0 | null | sentence-similarity | {"parameters": {"F32": 22713216, "I64": 512}, "total": 22713728} | [
".gitattributes",
"1_Pooling/config.json",
"README.md",
"config.json",
"config_sentence_transformers.json",
"model.safetensors",
"modules.json",
"onnx/model.onnx",
"onnx/model_O1.onnx",
"onnx/model_O2.onnx",
"onnx/model_O3.onnx",
"onnx/model_O4.onnx",
"onnx/model_qint8_arm64.onnx",
"onnx/m... | c9a2bfebc254878aee8c3aca9e6844d5bbb102d1 | [
"sentence-transformers",
"pytorch",
"tf",
"onnx",
"safetensors",
"openvino",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 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": null, "eval_results": null, "language": null, "library_name": "sentence-transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "sentence-similarity", "tags": ["sentence-transformers", "feature-extraction",... | # sentence-transformers/paraphrase-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 yo... | null | [
"apache-2.0"
] | null | null | 22,713,728 | null | null | [
"BertModel",
"AutoModel",
"bert"
] | [
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
621ffdc136468d709f17adb7 | 35f8a93a304f6b78 | 70bd27c212bc339c | 50,265 | BPE | 50,000 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 6 | facebook/bart-large-mnli | facebook | null | 3,358,415 | 143,603,374 | False | 2022-03-02T23:29:05Z | 2023-09-05T14:49:34Z | transformers | 1,552 | 4 | null | zero-shot-classification | {"parameters": {"F32": 407344133}, "total": 407344133} | [
".gitattributes",
"README.md",
"config.json",
"flax_model.msgpack",
"merges.txt",
"model.safetensors",
"pytorch_model.bin",
"rust_model.ot",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | d7645e127eaf1aefc7862fd59a17a5aa8558b8ce | [
"transformers",
"pytorch",
"jax",
"rust",
"safetensors",
"bart",
"text-classification",
"zero-shot-classification",
"dataset:multi_nli",
"arxiv:1910.13461",
"arxiv:1909.00161",
"license:mit",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["BartForSequenceClassification"], "model_type": "bart", "tokenizer_config": {}} | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["multi_nli"], "eval_results": null, "language": null, "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "zero-shot-classification", "tags": null, "thumbnail": "https://huggingface.co/front/thumbnails... | # bart-large-mnli
This is the checkpoint for [bart-large](https://huggingface.co/facebook/bart-large) after being trained on the [MultiNLI (MNLI)](https://huggingface.co/datasets/multi_nli) dataset.
Additional information about this model:
- The [bart-large](https://huggingface.co/facebook/bart-large) model page
- [B... | null | [
"mit"
] | [
"multi_nli"
] | null | 407,344,133 | null | null | [
"BartForSequenceClassification",
"bart",
"AutoModelForSequenceClassification"
] | [
"zero-shot-classification",
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
621ffdc136468d709f17adb6 | 35f8a93a304f6b78 | 70bd27c212bc339c | 50,265 | BPE | 50,000 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 6 | facebook/bart-large-cnn | facebook | null | 2,055,150 | 138,785,394 | False | 2022-03-02T23:29:05Z | 2024-02-13T18:02:05Z | transformers | 1,552 | 3 | [{"name": "facebook/bart-large-cnn", "results": [{"task": {"type": "summarization", "name": "Summarization"}, "dataset": {"name": "cnn_dailymail", "type": "cnn_dailymail", "config": "3.0.0", "split": "train"}, "metrics": [{"name": "ROUGE-1", "type": "rouge", "value": 42.9486, "verified": false}, {"name": "ROUGE-2", "ty... | summarization | {"parameters": {"F32": 406290432}, "total": 406290432} | [
".gitattributes",
"README.md",
"config.json",
"flax_model.msgpack",
"generation_config.json",
"generation_config_for_summarization.json",
"merges.txt",
"model.safetensors",
"pytorch_model.bin",
"rust_model.ot",
"tf_model.h5",
"tokenizer.json",
"vocab.json"
] | 37f520fa929c961707657b28798b30c003dd100b | [
"transformers",
"pytorch",
"tf",
"jax",
"rust",
"safetensors",
"bart",
"text2text-generation",
"summarization",
"en",
"dataset:cnn_dailymail",
"arxiv:1910.13461",
"license:mit",
"model-index",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["BartForConditionalGeneration"], "model_type": "bart"} | {
"auto_model": "AutoModelForSeq2SeqLM",
"custom_class": null,
"pipeline_tag": "text2text-generation",
"processor": "AutoTokenizer"
} | {"datasets": ["cnn_dailymail"], "language": ["en"], "license": "mit", "pipeline_tag": "summarization", "thumbnail": "https://huggingface.co/front/thumbnails/facebook.png", "model-index": [{"name": "facebook/bart-large-cnn", "results": [{"task": {"type": "summarization", "name": "Summarization"}, "dataset": {"name": "cn... | # BART (large-sized model), fine-tuned on CNN Daily Mail
BART model pre-trained on English language, and fine-tuned on [CNN Daily Mail](https://huggingface.co/datasets/cnn_dailymail). It was introduced in the paper [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Co... | null | [
"mit"
] | [
"cnn_dailymail"
] | [
"en"
] | 406,290,432 | null | null | [
"bart",
"BartForConditionalGeneration",
"AutoModelForSeq2SeqLM"
] | [
"text2text-generation",
"summarization"
] | [
"text"
] | [
"text"
] | [
"text"
] |
621ffdc136468d709f178e76 | 35f8a93a304f6b78 | 70bd27c212bc339c | 50,265 | BPE | 50,000 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 6 | allenai/longformer-base-4096 | allenai | null | 1,176,803 | 127,640,100 | False | 2022-03-02T23:29:05Z | 2023-04-05T18:24:00Z | transformers | 224 | 1 | null | null | null | [
".gitattributes",
"README.md",
"config.json",
"merges.txt",
"pytorch_model.bin",
"rust_model.ot",
"tf_model.h5",
"tokenizer.json",
"vocab.json"
] | 301e6a42cb0d9976a6d6a26a079fef81c18aa895 | [
"transformers",
"pytorch",
"tf",
"rust",
"longformer",
"en",
"arxiv:2004.05150",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | {"model_type": "longformer"} | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": null,
"processor": null
} | {"base_model": null, "datasets": null, "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": null} | # longformer-base-4096
[Longformer](https://arxiv.org/abs/2004.05150) is a transformer model for long documents.
`longformer-base-4096` is a BERT-like model started from the RoBERTa checkpoint and pretrained for MLM on long documents. It supports sequences of length up to 4,096.
Longformer uses a combination of a ... | null | [
"apache-2.0"
] | null | [
"en"
] | null | null | null | [
"AutoModel",
"longformer"
] | [
null
] | null | null | null |
621ffdc036468d709f174349 | 35f8a93a304f6b78 | 70bd27c212bc339c | 50,265 | BPE | 50,000 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 6 | distilbert/distilroberta-base | distilbert | null | 1,285,080 | 127,128,874 | False | 2022-03-02T23:29:04Z | 2024-02-19T11:09:58Z | transformers | 174 | 0 | null | fill-mask | {"parameters": {"F32": 82760793}, "total": 82760793} | [
".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"
] | fb53ab8802853c8e4fbdbcd0529f21fc6f459b2b | [
"transformers",
"pytorch",
"tf",
"jax",
"rust",
"safetensors",
"roberta",
"fill-mask",
"exbert",
"en",
"dataset:openwebtext",
"arxiv:1910.01108",
"arxiv:1910.09700",
"license:apache-2.0",
"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": ["openwebtext"], "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"]} | # Model Card for DistilRoBERTa base
# Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3. [Bias, Risks, and Limitations](#bias-risks-and-limitations)
4. [Training Details](#training-details)
5. [Evaluation](#evaluation)
6. [Environmental Impact](#environmental-impact)
7. [Citation](#citation)
8. ... | null | [
"apache-2.0"
] | [
"openwebtext"
] | [
"en"
] | 82,760,793 | null | null | [
"roberta",
"AutoModelForMaskedLM",
"RobertaForMaskedLM"
] | [
"fill-mask"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
66e98dd5899bdb384bd953b4 | 848b60b6cd304b65 | ffc911e785abe60e | 151,665 | BPE | 151,387 | true | true | true | true | 22 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 15 | Qwen/Qwen2.5-1.5B-Instruct | Qwen | {
"models": [
{
"_id": "66e6d032663c452ad2400439",
"id": "Qwen/Qwen2.5-1.5B"
}
],
"relation": "finetune"
} | 9,542,607 | 152,014,423 | False | 2024-09-17T14:10:29Z | 2024-09-25T12:32:50Z | transformers | 650 | 7 | null | text-generation | {"parameters": {"BF16": 1543714304}, "total": 1543714304} | [
".gitattributes",
"LICENSE",
"README.md",
"config.json",
"generation_config.json",
"merges.txt",
"model.safetensors",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | 989aa7980e4cf806f80c7fef2b1adb7bc71aa306 | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"chat",
"conversational",
"en",
"arxiv:2407.10671",
"base_model:Qwen/Qwen2.5-1.5B",
"base_model:finetune:Qwen/Qwen2.5-1.5B",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["Qwen2ForCausalLM"], "model_type": "qwen2", "tokenizer_config": {"bos_token": null, "chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- 'You are Qwen, created by Aliba... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "Qwen/Qwen2.5-1.5B", "datasets": null, "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": "https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct/blob/main/LICENSE", "metrics": null, "model_name": null, "pipeline_tag": "text... | # Qwen2.5-1.5B-Instruct
## Introduction
Qwen2.5 is the latest series of Qwen large language models. For Qwen2.5, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters. Qwen2.5 brings the following improvements upon Qwen2:
- Significantly **more kn... | null | [
"apache-2.0",
"https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct/blob/main/LICENSE"
] | null | [
"en"
] | 1,543,714,304 | null | null | [
"AutoModelForCausalLM",
"Qwen2ForCausalLM",
"qwen2"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
621ffdc036468d709f174344 | 71140a663949f258 | 8a01c02d4d0a401c | 119,547 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 9 | distilbert/distilbert-base-multilingual-cased | distilbert | null | 892,115 | 114,385,244 | False | 2022-03-02T23:29:04Z | 2024-05-06T13:46:54Z | transformers | 236 | 0 | null | fill-mask | {"parameters": {"F32": 135445755}, "total": 135445755} | [
".gitattributes",
"README.md",
"config.json",
"model.onnx",
"model.safetensors",
"pytorch_model.bin",
"tf_model.h5",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | 45c032ab32cc946ad88a166f7cb282f58c753c2e | [
"transformers",
"pytorch",
"tf",
"onnx",
"safetensors",
"distilbert",
"fill-mask",
"multilingual",
"af",
"sq",
"ar",
"an",
"hy",
"ast",
"az",
"ba",
"eu",
"bar",
"be",
"bn",
"inc",
"bs",
"br",
"bg",
"my",
"ca",
"ceb",
"ce",
"zh",
"cv",
"hr",
"cs",
"da",... | null | {"architectures": ["DistilBertForMaskedLM"], "model_type": "distilbert", "tokenizer_config": {}} | {
"auto_model": "AutoModelForMaskedLM",
"custom_class": null,
"pipeline_tag": "fill-mask",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["wikipedia"], "eval_results": null, "language": ["multilingual", "af", "sq", "ar", "an", "hy", "ast", "az", "ba", "eu", "bar", "be", "bn", "inc", "bs", "br", "bg", "my", "ca", "ceb", "ce", "zh", "cv", "hr", "cs", "da", "nl", "en", "et", "fi", "fr", "gl", "ka", "de", "el", "gu", "ht", "... | # Model Card for DistilBERT base multilingual (cased)
# Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3. [Bias, Risks, and Limitations](#bias-risks-and-limitations)
4. [Training Details](#training-details)
5. [Evaluation](#evaluation)
6. [Environmental Impact](#environmental-impact)
7. [Citati... | null | [
"apache-2.0"
] | [
"wikipedia"
] | [
"multilingual",
"af",
"sq",
"ar",
"an",
"hy",
"ast",
"az",
"ba",
"eu",
"bar",
"be",
"bn",
"inc",
"bs",
"br",
"bg",
"my",
"ca",
"ceb",
"ce",
"zh",
"cv",
"hr",
"cs",
"da",
"nl",
"en",
"et",
"fi",
"fr",
"gl",
"ka",
"de",
"el",
"gu",
"ht",
"he",
... | 135,445,755 | null | null | [
"distilbert",
"AutoModelForMaskedLM",
"DistilBertForMaskedLM"
] | [
"fill-mask"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
63314bef9e3604f3f17e5b9e | 61736c54febdeba6 | aa8af6ff55c14796 | 51,865 | BPE | 50,000 | false | true | true | true | 1,608 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 16 | openai/whisper-small | openai | null | 1,779,882 | 118,833,474 | False | 2022-09-26T06:51:27Z | 2024-02-29T10:57:38Z | transformers | 544 | 4 | [{"name": "whisper-small", "results": [{"task": {"name": "Automatic Speech Recognition", "type": "automatic-speech-recognition"}, "dataset": {"name": "LibriSpeech (clean)", "type": "librispeech_asr", "config": "clean", "split": "test", "args": {"language": "en"}}, "metrics": [{"name": "Test WER", "type": "wer", "value"... | automatic-speech-recognition | null | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"flax_model.msgpack",
"generation_config.json",
"merges.txt",
"model.safetensors",
"normalizer.json",
"preprocessor_config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tf_model.h5",
"tokenizer.json",
"tokenizer... | 973afd24965f72e36ca33b3055d56a652f456b4d | [
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"whisper",
"automatic-speech-recognition",
"audio",
"hf-asr-leaderboard",
"en",
"zh",
"de",
"es",
"ru",
"ko",
"fr",
"ja",
"pt",
"tr",
"pl",
"ca",
"nl",
"ar",
"sv",
"it",
"id",
"hi",
"fi",
"vi",
"he",
"... | null | {"architectures": ["WhisperForConditionalGeneration"], "model_type": "whisper", "tokenizer_config": {"bos_token": "<|endoftext|>", "eos_token": "<|endoftext|>", "pad_token": "<|endoftext|>", "unk_token": "<|endoftext|>"}} | {
"auto_model": "AutoModelForSpeechSeq2Seq",
"custom_class": null,
"pipeline_tag": "automatic-speech-recognition",
"processor": "AutoProcessor"
} | {"language": ["en", "zh", "de", "es", "ru", "ko", "fr", "ja", "pt", "tr", "pl", "ca", "nl", "ar", "sv", "it", "id", "hi", "fi", "vi", "he", "uk", "el", "ms", "cs", "ro", "da", "hu", "ta", "no", "th", "ur", "hr", "bg", "lt", "la", "mi", "ml", "cy", "sk", "te", "fa", "lv", "bn", "sr", "az", "sl", "kn", "et", "mk", "br", ... | # Whisper
Whisper is a pre-trained model for automatic speech recognition (ASR) and speech translation. Trained on 680k hours
of labelled data, Whisper models demonstrate a strong ability to generalise to many datasets and domains **without** the need
for fine-tuning.
Whisper was proposed in the paper [Robust Speec... | null | [
"apache-2.0"
] | null | [
"en",
"zh",
"de",
"es",
"ru",
"ko",
"fr",
"ja",
"pt",
"tr",
"pl",
"ca",
"nl",
"ar",
"sv",
"it",
"id",
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"vi",
"he",
"uk",
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"ro",
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"hu",
"ta",
"no",
"th",
"ur",
"hr",
"bg",
"lt",
"la",
"mi",
"ml",
"cy",
"sk"... | null | null | null | [
"WhisperForConditionalGeneration",
"whisper",
"AutoModelForSpeechSeq2Seq"
] | [
"automatic-speech-recognition"
] | [
"multimodal"
] | [
"audio"
] | [
"text"
] |
621ffdc136468d709f17adc0 | 53ecf467267e2824 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 0 | facebook/contriever | facebook | null | 7,438,679 | 138,473,525 | False | 2022-03-02T23:29:05Z | 2022-01-19T17:23:28Z | transformers | 73 | 0 | null | null | null | [
".gitattributes",
"README.md",
"config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | 2bd46a25019aeea091fd42d1f0fd4801675cf699 | [
"transformers",
"pytorch",
"bert",
"arxiv:2112.09118",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["Contriever"], "model_type": "bert", "tokenizer_config": {"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}} | {
"auto_model": "Contriever",
"custom_class": null,
"pipeline_tag": null,
"processor": "AutoTokenizer"
} | null | This model has been trained without supervision following the approach described in [Towards Unsupervised Dense Information Retrieval with Contrastive Learning](https://arxiv.org/abs/2112.09118). The associated GitHub repository is available here https://github.com/facebookresearch/contriever.
## Usage (HuggingFace Tr... | null | null | null | null | null | null | null | [
"Contriever",
"bert"
] | [
null
] | null | null | null |
621ffdc136468d709f1802cf | 120c2ce442fd8f55 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 17 | sentence-transformers/multi-qa-MiniLM-L6-cos-v1 | sentence-transformers | null | 956,032 | 105,442,012 | False | 2022-03-02T23:29:05Z | 2024-11-05T17:17:16Z | sentence-transformers | 137 | 0 | 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",
"modules.json",
"onnx/model.onnx",
"onnx/model_O1.onnx",
"onnx/model_O2.onnx",
"onnx/model_O3.onnx",
"onnx/model_O4.onnx",
"onnx/model_qint8_... | b207367332321f8e44f96e224ef15bc607f4dbf0 | [
"sentence-transformers",
"pytorch",
"tf",
"onnx",
"safetensors",
"openvino",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"en",
"dataset:flax-sentence-embeddings/stackexchange_xml",
"dataset:ms_marco",
"dataset:gooaq",
"dataset:yahoo_answers_topics",
"dataset:se... | 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": ["flax-sentence-embeddings/stackexchange_xml", "ms_marco", "gooaq", "yahoo_answers_topics", "search_qa", "eli5", "natural_questions", "trivia_qa", "embedding-data/QQP", "embedding-data/PAQ_pairs", "embedding-data/Amazon-QA", "embedding-data/WikiAnswers"], "eval_results": null, "language... | # multi-qa-MiniLM-L6-cos-v1
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and was designed for **semantic search**. It has been trained on 215M (question, answer) pairs from diverse sources. For an introduction to semantic search, ... | null | null | [
"flax-sentence-embeddings/stackexchange_xml",
"ms_marco",
"gooaq",
"yahoo_answers_topics",
"search_qa",
"eli5",
"natural_questions",
"trivia_qa",
"embedding-data/QQP",
"embedding-data/PAQ_pairs",
"embedding-data/Amazon-QA",
"embedding-data/WikiAnswers"
] | [
"en"
] | 22,713,728 | null | null | [
"BertModel",
"AutoModel",
"bert"
] | [
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
6698d8a0653e4babe21e1e7d | 1e0a2493163c0c63 | 6622292dca275a2d | 128,256 | BPE | 280,147 | false | true | true | true | 256 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 18 | meta-llama/Llama-3.1-8B-Instruct | meta-llama | {
"models": [
{
"_id": "66944f1fe0c5c2e493a804f5",
"id": "meta-llama/Llama-3.1-8B"
}
],
"relation": "finetune"
} | 8,384,864 | 133,735,004 | manual | 2024-07-18T08:56:00Z | 2024-09-25T17:00:57Z | transformers | 5,631 | 47 | null | text-generation | {"parameters": {"BF16": 8030261248}, "total": 8030261248} | [
".gitattributes",
"LICENSE",
"README.md",
"USE_POLICY.md",
"config.json",
"generation_config.json",
"model-00001-of-00004.safetensors",
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"model-00003-of-00004.safetensors",
"model-00004-of-00004.safetensors",
"model.safetensors.index.json",
"original/consolid... | 0e9e39f249a16976918f6564b8830bc894c89659 | [
"transformers",
"safetensors",
"llama",
"text-generation",
"facebook",
"meta",
"pytorch",
"llama-3",
"conversational",
"en",
"de",
"fr",
"it",
"pt",
"hi",
"es",
"th",
"arxiv:2204.05149",
"base_model:meta-llama/Llama-3.1-8B",
"base_model:finetune:meta-llama/Llama-3.1-8B",
"lic... | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": "<|begin_of_text|>", "chat_template": "{{- bos_token }}\n{%- if custom_tools is defined %}\n {%- set tools = custom_tools %}\n{%- endif %}\n{%- if not tools_in_user_message is defined %}\n {%- set tools_in_user_messag... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "meta-llama/Meta-Llama-3.1-8B", "datasets": null, "eval_results": null, "language": ["en", "de", "fr", "it", "pt", "hi", "es", "th"], "library_name": null, "license": "llama3.1", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-generation", "tags": ["... | null | null | [
"llama3.1"
] | null | [
"en",
"de",
"fr",
"it",
"pt",
"hi",
"es",
"th"
] | 8,030,261,248 | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
63f520d3cc1dd3168692e256 | f56392a3c3b561ef | f26f356cd728caa8 | 49,408 | BPE | 48,894 | true | true | true | true | 2 | Sequence | Sequence | ByteLevel | [
"NFC",
"Replace",
"Lowercase"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 7 | patrickjohncyh/fashion-clip | patrickjohncyh | null | 2,602,382 | 104,894,550 | False | 2023-02-21T19:51:47Z | 2024-09-17T15:19:43Z | transformers | 270 | 2 | null | zero-shot-image-classification | {"parameters": {"I64": 127, "F32": 151277312}, "total": 151277439} | [
".gitattributes",
"README.md",
"config.json",
"merges.txt",
"model.safetensors",
"onnx/config.json",
"onnx/merges.txt",
"onnx/model.onnx",
"onnx/preprocessor_config.json",
"onnx/special_tokens_map.json",
"onnx/tokenizer.json",
"onnx/tokenizer_config.json",
"onnx/vocab.json",
"preprocessor_... | 7e3ba62ce16b379a1ab479346b66f192e76f51b7 | [
"transformers",
"pytorch",
"onnx",
"safetensors",
"clip",
"zero-shot-image-classification",
"vision",
"language",
"fashion",
"ecommerce",
"en",
"license:mit",
"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": ["en"], "library_name": "transformers", "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["vision", "language", "fashion", "ecommerce"], "widget": [{"src": "https://cd... | [](https://www.youtube.com/watch?v=uqRSc-KSA1Y) [](https://huggingface.co/patrickjohncyh/fashion-clip) [](... | null | [
"mit"
] | null | [
"en"
] | 151,277,439 | null | null | [
"AutoModelForZeroShotImageClassification",
"CLIPModel",
"clip"
] | [
"zero-shot-image-classification"
] | [
"multimodal"
] | [
"text",
"image"
] | [
"logits"
] |
632322d59e488d65bf2f4742 | 5ef9eb7677e33416 | 70bd27c212bc339c | 50,265 | BPE | 50,000 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 19 | SamLowe/roberta-base-go_emotions | SamLowe | null | 271,397 | 94,033,788 | False | 2022-09-15T13:04:21Z | 2023-10-04T10:00:58Z | transformers | 661 | 1 | null | text-classification | null | [
".gitattributes",
"README.md",
"config.json",
"merges.txt",
"model.safetensors",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"trainer_state.json",
"vocab.json"
] | 58b6c5b44a7a12093f782442969019c7e2982299 | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"text-classification",
"emotions",
"multi-class-classification",
"multi-label-classification",
"en",
"dataset:go_emotions",
"doi:10.57967/hf/3548",
"license:mit",
"text-embeddings-inference",
"endpoints_compatible",
"deploy:azure",
"... | null | {"architectures": ["RobertaForSequenceClassification"], "model_type": "roberta", "tokenizer_config": {"bos_token": "<s>", "cls_token": "<s>", "eos_token": "</s>", "mask_token": "<mask>", "pad_token": "<pad>", "sep_token": "</s>", "unk_token": "<unk>"}} | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["go_emotions"], "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": ["text-classification", "pytorch", "roberta", "emotions", "multi-class-classifi... | #### Overview
Model trained from [roberta-base](https://huggingface.co/roberta-base) on the [go_emotions](https://huggingface.co/datasets/go_emotions) dataset for multi-label classification.
##### ONNX version also available
A version of this model in ONNX format (including an INT8 quantized ONNX version) is now ava... | null | [
"mit"
] | [
"go_emotions"
] | [
"en"
] | null | null | null | [
"roberta",
"AutoModelForSequenceClassification",
"RobertaForSequenceClassification"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
64fff537d522560505ad6567 | 53ecf467267e2824 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 0 | BAAI/bge-small-en-v1.5 | BAAI | null | 11,538,782 | 118,876,425 | False | 2023-09-12T05:20:55Z | 2024-02-22T03:36:23Z | sentence-transformers | 429 | 5 | [{"name": "bge-small-en-v1.5", "results": [{"task": {"type": "Classification"}, "dataset": {"type": "mteb/amazon_counterfactual", "name": "MTEB AmazonCounterfactualClassification (en)", "config": "en", "split": "test", "revision": "e8379541af4e31359cca9fbcf4b00f2671dba205"}, "metrics": [{"type": "accuracy", "value": 73... | feature-extraction | {"parameters": {"I64": 512, "F32": 33360000}, "total": 33360512} | [
".gitattributes",
"1_Pooling/config.json",
"README.md",
"config.json",
"config_sentence_transformers.json",
"model.safetensors",
"modules.json",
"onnx/model.onnx",
"pytorch_model.bin",
"sentence_bert_config.json",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab... | 5c38ec7c405ec4b44b94cc5a9bb96e735b38267a | [
"sentence-transformers",
"pytorch",
"onnx",
"safetensors",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"mteb",
"en",
"arxiv:2401.03462",
"arxiv:2312.15503",
"arxiv:2311.13534",
"arxiv:2310.07554",
"arxiv:2309.07597",
"license:mit",
"model-index",
"text-embe... | null | {"architectures": ["BertModel"], "model_type": "bert", "tokenizer_config": {"cls_token": "[CLS]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": "feature-extraction",
"processor": "AutoTokenizer"
} | {"language": ["en"], "license": "mit", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers", "mteb"], "model-index": [{"name": "bge-small-en-v1.5", "results": [{"task": {"type": "Classification"}, "dataset": {"name": "MTEB AmazonCounterfactualClassification (en)", "type": "mteb/... | <h1 align="center">FlagEmbedding</h1>
<h4 align="center">
<p>
<a href=#model-list>Model List</a> |
<a href=#frequently-asked-questions>FAQ</a> |
<a href=#usage>Usage</a> |
<a href="#evaluation">Evaluation</a> |
<a href="#train">Train</a> |
<a href="#contact">Conta... | null | [
"mit"
] | null | [
"en"
] | 33,360,512 | null | null | [
"BertModel",
"AutoModel",
"bert"
] | [
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
621ffdc036468d709f17433f | dd4f9ee07a05851c | 5d0543b90d3ada56 | 32,004 | Unigram | null | true | true | true | true | 7 | Precompiled | Sequence | Metaspace | [
"Precompiled"
] | [
"WhitespaceSplit",
"Metaspace"
] | [
"Metaspace"
] | 20 | almanach/camembert-base | almanach | null | 877,691 | 92,204,561 | False | 2022-03-02T23:29:04Z | 2025-06-23T14:02:30Z | transformers | 99 | 0 | null | fill-mask | {"parameters": {"F32": 111246085}, "total": 111246085} | [
".gitattributes",
"README.md",
"config.json",
"model.safetensors",
"pytorch_model.bin",
"sentencepiece.bpe.model",
"tf_model.h5",
"tokenizer.json",
"tokenizer_config.json"
] | a75967561c78f2aa81cc41045378d3b4ee25af9e | [
"transformers",
"pytorch",
"tf",
"safetensors",
"camembert",
"fill-mask",
"fr",
"dataset:oscar",
"arxiv:1911.03894",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["CamembertForMaskedLM"], "model_type": "camembert", "tokenizer_config": {}} | {
"auto_model": "AutoModelForMaskedLM",
"custom_class": null,
"pipeline_tag": "fill-mask",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["oscar"], "eval_results": null, "language": "fr", "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | # CamemBERT: a Tasty French Language Model
## Introduction
[CamemBERT](https://arxiv.org/abs/1911.03894) is a state-of-the-art language model for French based on the RoBERTa model.
It is now available on Hugging Face in 6 different versions with varying number of parameters, amount of pretraining data and pretraini... | null | [
"mit"
] | [
"oscar"
] | [
"fr"
] | 111,246,085 | null | null | [
"CamembertForMaskedLM",
"AutoModelForMaskedLM",
"camembert"
] | [
"fill-mask"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
654a84cadff2f49007ce6c37 | 344b7c49c2732e43 | 8386a692be535934 | 51,866 | BPE | 50,000 | false | true | true | true | 1,609 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 21 | openai/whisper-large-v3 | openai | null | 4,832,162 | 115,357,760 | False | 2023-11-07T18:41:14Z | 2024-08-12T10:20:10Z | transformers | 5,522 | 30 | null | automatic-speech-recognition | null | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"flax_model.msgpack",
"generation_config.json",
"merges.txt",
"model.fp32-00001-of-00002.safetensors",
"model.fp32-00002-of-00002.safetensors",
"model.safetensors",
"model.safetensors.index.fp32.json",
"normalizer.json",
"pre... | 06f233fe06e710322aca913c1bc4249a0d71fce1 | [
"transformers",
"pytorch",
"jax",
"safetensors",
"whisper",
"automatic-speech-recognition",
"audio",
"hf-asr-leaderboard",
"en",
"zh",
"de",
"es",
"ru",
"ko",
"fr",
"ja",
"pt",
"tr",
"pl",
"ca",
"nl",
"ar",
"sv",
"it",
"id",
"hi",
"fi",
"vi",
"he",
"uk",
"... | null | {"architectures": ["WhisperForConditionalGeneration"], "model_type": "whisper", "tokenizer_config": {"bos_token": "<|endoftext|>", "eos_token": "<|endoftext|>", "pad_token": "<|endoftext|>", "unk_token": "<|endoftext|>"}} | {
"auto_model": "AutoModelForSpeechSeq2Seq",
"custom_class": null,
"pipeline_tag": "automatic-speech-recognition",
"processor": "AutoProcessor"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en", "zh", "de", "es", "ru", "ko", "fr", "ja", "pt", "tr", "pl", "ca", "nl", "ar", "sv", "it", "id", "hi", "fi", "vi", "he", "uk", "el", "ms", "cs", "ro", "da", "hu", "ta", "no", "th", "ur", "hr", "bg", "lt", "la", "mi", "ml", "cy", "sk", "te", ... | # Whisper
Whisper is a state-of-the-art model for automatic speech recognition (ASR) and speech translation, proposed in the paper
[Robust Speech Recognition via Large-Scale Weak Supervision](https://huggingface.co/papers/2212.04356) by Alec Radford
et al. from OpenAI. Trained on >5M hours of labeled data, Whisper d... | null | [
"apache-2.0"
] | null | [
"en",
"zh",
"de",
"es",
"ru",
"ko",
"fr",
"ja",
"pt",
"tr",
"pl",
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"no",
"th",
"ur",
"hr",
"bg",
"lt",
"la",
"mi",
"ml",
"cy",
"sk"... | null | null | null | [
"WhisperForConditionalGeneration",
"whisper",
"AutoModelForSpeechSeq2Seq"
] | [
"automatic-speech-recognition"
] | [
"multimodal"
] | [
"audio"
] | [
"text"
] |
649b553bc4afb9f5bcf9c149 | 53ecf467267e2824 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 0 | colbert-ir/colbertv2.0 | colbert-ir | null | 13,575,793 | 155,344,798 | False | 2023-06-27T21:31:39Z | 2024-04-05T20:18:44Z | transformers | 315 | 0 | null | null | null | [
".gitattributes",
"README.md",
"artifact.metadata",
"config.json",
"model.onnx",
"model.safetensors",
"pytorch_model.bin",
"special_tokens_map.json",
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"tokenizer_config.json",
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] | c1e84128e85ef755c096a95bdb06b47793b13acf | [
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"ColBERT",
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"arxiv:2007.00814",
"arxiv:2101.00436",
"arxiv:2112.01488",
"arxiv:2205.09707",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["HF_ColBERT"], "model_type": "bert", "tokenizer_config": {"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}} | {
"auto_model": "HF_ColBERT",
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} | {"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": ["ColBERT"]} | <p align="center">
<img align="center" src="https://github.com/stanford-futuredata/ColBERT/blob/main/docs/images/colbertofficial.png?raw=true" width="430px" />
</p>
<p align="left">
# ColBERT (v2)
### ColBERT is a _fast_ and _accurate_ retrieval model, enabling scalable BERT-based search over large text collections... | null | [
"mit"
] | null | [
"en"
] | null | null | null | [
"HF_ColBERT",
"bert"
] | [
null
] | null | null | null |
621ffdc136468d709f17a20e | 53ecf467267e2824 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 0 | cross-encoder/ms-marco-MiniLM-L6-v2 | cross-encoder | {
"models": [
{
"_id": "621ffdc136468d709f17a20b",
"id": "cross-encoder/ms-marco-MiniLM-L12-v2"
}
],
"relation": "quantized"
} | 15,023,460 | 130,538,272 | False | 2022-03-02T23:29:05Z | 2025-08-29T14:36:10Z | sentence-transformers | 202 | 1 | null | text-ranking | {"parameters": {"I64": 512, "F32": 22713601}, "total": 22714113} | [
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"onnx/model_qint8_arm64.onnx",
"onnx/model_qint8_avx512.onnx",
"onnx/model_qint8_avx512_vnni.onn... | c5ee24cb16019beea0893ab7796b1df96625c6b8 | [
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"dataset:sentence-transformers/msmarco",
"base_model:cross-encoder/ms-marco-MiniLM-L12-v2",
"base_model:quantized:cross-encoder/ms-marco-MiniLM... | null | {"architectures": ["BertForSequenceClassification"], "model_type": "bert", "tokenizer_config": {"cls_token": "[CLS]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | {"base_model": ["cross-encoder/ms-marco-MiniLM-L12-v2"], "datasets": ["sentence-transformers/msmarco"], "eval_results": null, "language": ["en"], "library_name": "sentence-transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-ranki... | # Cross-Encoder for MS Marco
This model was trained on the [MS Marco Passage Ranking](https://github.com/microsoft/MSMARCO-Passage-Ranking) task.
The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch). Then sort the passages in a... | null | [
"apache-2.0"
] | [
"sentence-transformers/msmarco"
] | [
"en"
] | 22,714,113 | null | null | [
"BertForSequenceClassification",
"bert",
"AutoModelForSequenceClassification"
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"text-ranking",
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
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621ffdc036468d709f176a6a | bfe694f64f4a18f5 | daab2a732bab72e2 | 128,001 | Unigram | null | true | true | true | true | 5 | Sequence | Sequence | Metaspace | [
"Strip",
"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 22 | MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli | MoritzLaurer | null | 96,448 | 85,147,923 | False | 2022-03-02T23:29:04Z | 2024-04-11T13:47:27Z | transformers | 222 | 0 | [{"name": "MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli", "results": [{"task": {"type": "natural-language-inference", "name": "Natural Language Inference"}, "dataset": {"name": "anli", "type": "anli", "config": "plain_text", "split": "test_r3"}, "metrics": [{"type": "accuracy", "value": 0.495, "name": "Accuracy", "veri... | zero-shot-classification | {"parameters": {"I64": 512, "F16": 184424451}, "total": 184424963} | [
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"model.safetensors",
"pytorch_model.bin",
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"spm.model",
"tokenizer.json",
"tokenizer_config.json"
] | 6f5cf0a2b59cabb106aca4c287eed12e357e90eb | [
"transformers",
"pytorch",
"safetensors",
"deberta-v2",
"text-classification",
"zero-shot-classification",
"en",
"dataset:multi_nli",
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"dataset:fever",
"arxiv:2006.03654",
"license:mit",
"model-index",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["DebertaV2ForSequenceClassification"], "model_type": "deberta-v2", "tokenizer_config": {"bos_token": "[CLS]", "cls_token": "[CLS]", "eos_token": "[SEP]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | {"datasets": ["multi_nli", "facebook/anli", "fever"], "language": ["en"], "license": "mit", "metrics": ["accuracy"], "pipeline_tag": "zero-shot-classification", "tags": ["text-classification", "zero-shot-classification"], "model-index": [{"name": "MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli", "results": [{"task": {"ty... | # DeBERTa-v3-base-mnli-fever-anli
## Model description
This model was trained on the MultiNLI, Fever-NLI and Adversarial-NLI (ANLI) datasets, which comprise 763 913 NLI hypothesis-premise pairs. This base model outperforms almost all large models on the [ANLI benchmark](https://github.com/facebookresearch/anli).
The b... | null | [
"mit"
] | [
"multi_nli",
"facebook/anli",
"fever"
] | [
"en"
] | 184,424,963 | null | [
"accuracy"
] | [
"AutoModelForSequenceClassification",
"deberta-v2",
"DebertaV2ForSequenceClassification"
] | [
"zero-shot-classification",
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
621ffdc036468d709f17433e | 53ecf467267e2824 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 0 | google-bert/bert-large-uncased | google-bert | null | 350,123 | 85,997,164 | False | 2022-03-02T23:29:04Z | 2024-02-19T11:06:54Z | transformers | 146 | 0 | null | fill-mask | {"parameters": {"F32": 336226108}, "total": 336226108} | [
".gitattributes",
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"config.json",
"flax_model.msgpack",
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"rust_model.ot",
"tf_model.h5",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt",
"whole-word-masking.tar.gz"
] | 6da4b6a26a1877e173fca3225479512db81a5e5b | [
"transformers",
"pytorch",
"tf",
"jax",
"rust",
"safetensors",
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"en",
"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": null} | # BERT large 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
... | null | [
"apache-2.0"
] | [
"bookcorpus",
"wikipedia"
] | [
"en"
] | 336,226,108 | null | null | [
"AutoModelForMaskedLM",
"bert",
"BertForMaskedLM"
] | [
"fill-mask"
] | [
"text"
] | [
"text"
] | [
"logits"
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621ffdc136468d709f17adb5 | 35f8a93a304f6b78 | 70bd27c212bc339c | 50,265 | BPE | 50,000 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 6 | facebook/bart-base | facebook | null | 1,141,555 | 90,097,585 | False | 2022-03-02T23:29:05Z | 2022-11-16T23:23:10Z | transformers | 204 | 0 | null | feature-extraction | {"parameters": {"F32": 139420416}, "total": 139420416} | [
".gitattributes",
"README.md",
"config.json",
"flax_model.msgpack",
"merges.txt",
"model.safetensors",
"pytorch_model.bin",
"tf_model.h5",
"tokenizer.json",
"vocab.json"
] | aadd2ab0ae0c8268c7c9693540e9904811f36177 | [
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"bart",
"feature-extraction",
"en",
"arxiv:1910.13461",
"license:apache-2.0",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["BartModel"], "model_type": "bart"} | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": "feature-extraction",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "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": null} | # BART (base-sized model)
BART model pre-trained on English language. It was introduced in the paper [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://arxiv.org/abs/1910.13461) by Lewis et al. and first released in [this repository](https://git... | null | [
"apache-2.0"
] | null | [
"en"
] | 139,420,416 | null | null | [
"AutoModel",
"bart",
"BartModel"
] | [
"feature-extraction"
] | [
"multimodal"
] | [
"text"
] | [
"embeddings"
] |
621ffdc036468d709f174337 | f448f8d85afb04ca | 71902f73ac381369 | 105,879 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 23 | google-bert/bert-base-multilingual-uncased | google-bert | null | 5,029,569 | 95,498,149 | False | 2022-03-02T23:29:04Z | 2024-02-19T11:06:00Z | transformers | 153 | 0 | null | fill-mask | null | [
".gitattributes",
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"flax_model.msgpack",
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"tokenizer_config.json",
"vocab.txt"
] | 7cbf9a625e29989f6b9c6c2fa68234c304f7e38f | [
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"multilingual",
"af",
"sq",
"ar",
"an",
"hy",
"ast",
"az",
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"bg",
"my",
"ca",
"ceb",
"ce",
"zh",
"cv",
"hr",
"cs",
"da",
"nl"... | null | {"architectures": ["BertForMaskedLM"], "model_type": "bert", "tokenizer_config": {}} | {
"auto_model": "AutoModelForMaskedLM",
"custom_class": null,
"pipeline_tag": "fill-mask",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["wikipedia"], "eval_results": null, "language": ["multilingual", "af", "sq", "ar", "an", "hy", "ast", "az", "ba", "eu", "bar", "be", "bn", "inc", "bs", "br", "bg", "my", "ca", "ceb", "ce", "zh", "cv", "hr", "cs", "da", "nl", "en", "et", "fi", "fr", "gl", "ka", "de", "el", "gu", "ht", "... | # BERT multilingual base model (uncased)
Pretrained model on the top 102 languages with the largest Wikipedia 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 mode... | null | [
"apache-2.0"
] | [
"wikipedia"
] | [
"multilingual",
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"en",
"et",
"fi",
"fr",
"gl",
"ka",
"de",
"el",
"gu",
"ht",
"he",
... | null | null | null | [
"AutoModelForMaskedLM",
"bert",
"BertForMaskedLM"
] | [
"fill-mask"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
64552d5fd55525a4fee98c2a | 179f0ee8c5dedc6d | 8a01c02d4d0a401c | 119,547 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 24 | lxyuan/distilbert-base-multilingual-cased-sentiments-student | lxyuan | null | 912,277 | 75,123,176 | False | 2023-05-05T16:22:55Z | 2025-03-03T02:06:53Z | transformers | 309 | 1 | [{"name": "distilbert-base-multilingual-cased-sentiments-student", "results": []}] | text-classification | {"parameters": {"F32": 135326979}, "total": 135326979} | [
".gitattributes",
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"model.safetensors",
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"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
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"de",
"es",
"fr",
"ja",
"zh",
"id",
"hi",
"it",
"ms",
"pt",
"data... | null | {"architectures": ["DistilBertForSequenceClassification"], "model_type": "distilbert", "tokenizer_config": {"cls_token": "[CLS]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["tyqiangz/multilingual-sentiments"], "eval_results": [], "language": ["en", "ar", "de", "es", "fr", "ja", "zh", "id", "hi", "it", "ms", "pt"], "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": "distilbert-base-mult... | <!-- 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-multilingual-cased-sentiments-student
This model is distilled from the zero-shot classification pipeline on the M... | null | [
"apache-2.0"
] | [
"tyqiangz/multilingual-sentiments"
] | [
"en",
"ar",
"de",
"es",
"fr",
"ja",
"zh",
"id",
"hi",
"it",
"ms",
"pt"
] | 135,326,979 | null | null | [
"DistilBertForSequenceClassification",
"distilbert",
"AutoModelForSequenceClassification"
] | [
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"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
66d707353ee47118e05dc225 | 55a4929ac1bebb9a | 8f1cd02f3aeec49a | 128,257 | BPE | 280,147 | false | true | true | true | 257 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 25 | 1231czx/llama3_it_ultra_list_and_bold500 | 1231czx | null | 18 | 71,152,746 | False | 2024-09-03T12:55:17Z | 2024-09-03T12:58:12Z | transformers | 9 | 0 | null | text-classification | {"parameters": {"BF16": 7504932864}, "total": 7504932864} | [
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] | 31bbdb84c8c535f807fad9013a50b71a924a7fc2 | [
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"llama",
"text-classification",
"arxiv:1910.09700",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["LlamaForSequenceClassification"], "model_type": "llama", "tokenizer_config": {"bos_token": "<|begin_of_text|>", "chat_template": "{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content']... | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": "transformers", "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": []} | # Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated... | null | null | null | null | 7,504,932,864 | null | null | [
"LlamaForSequenceClassification",
"llama",
"AutoModelForSequenceClassification"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
621ffdc136468d709f17def3 | 53ecf467267e2824 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 0 | microsoft/layoutlm-base-uncased | microsoft | null | 133,674 | 71,187,529 | False | 2022-03-02T23:29:05Z | 2024-04-16T12:16:49Z | transformers | 61 | 0 | null | null | {"parameters": {"F32": 112627968}, "total": 112627968} | [
".gitattributes",
"README.md",
"config.json",
"model.safetensors",
"pytorch_model.bin",
"special_tokens_map.json",
"tf_model.h5",
"tokenizer.json",
"tokenizer_config.json",
"training_args.bin",
"vocab.txt"
] | 30e3cdd39c11f09757b0fcf7598533d05052acef | [
"transformers",
"pytorch",
"tf",
"safetensors",
"layoutlm",
"en",
"arxiv:1912.13318",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | {"model_type": "layoutlm", "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": null,
"processor": null
} | {"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": null} | # LayoutLM
**Multimodal (text + layout/format + image) pre-training for document AI**
[Microsoft Document AI](https://www.microsoft.com/en-us/research/project/document-ai/) | [GitHub](https://aka.ms/layoutlm)
## Model description
LayoutLM is a simple but effective pre-training method of text and layout for document ... | null | [
"mit"
] | null | [
"en"
] | 112,627,968 | null | null | [
"layoutlm",
"AutoModel"
] | [
null
] | null | null | null |
629f5e6d06c798ca0f45f849 | 191e06b04083279c | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 26 | marieke93/MiniLM-evidence-types | marieke93 | null | 9,011 | 67,102,690 | False | 2022-06-07T14:19:25Z | 2022-06-11T13:32:27Z | transformers | 18 | 0 | [{"name": "MiniLM-evidence-types", "results": []}] | text-classification | null | [
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"runs/bs16_lr2e-5/events.out.tfevents.1654700638.97eb22b2c97e.74.20",
"runs/bs16_lr3e-5/1654611951.1008868/events.out.tfevents.16546... | afdd48fd4eb5d666bdd2d9d34e027bb1405d0b46 | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["BertForSequenceClassification"], "model_type": "bert", "tokenizer_config": {"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}} | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": [], "language": null, "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": ["accuracy"], "model_name": "MiniLM-evidence-types", "pipeline_tag": null, "tags": ["generated_from_trainer"]} | <!-- 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. -->
# MiniLM-evidence-types
This model is a fine-tuned version of [microsoft/MiniLM-L12-H384-uncased](https://huggingface.co/microsoft/... | null | [
"mit"
] | null | null | null | null | [
"accuracy"
] | [
"BertForSequenceClassification",
"bert",
"AutoModelForSequenceClassification"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
621ffdc136468d709f17a20b | 53ecf467267e2824 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 0 | cross-encoder/ms-marco-MiniLM-L12-v2 | cross-encoder | {
"models": [
{
"_id": "621ffdc136468d709f17deca",
"id": "microsoft/MiniLM-L12-H384-uncased"
}
],
"relation": "quantized"
} | 1,576,894 | 64,115,032 | False | 2022-03-02T23:29:05Z | 2025-08-29T14:37:04Z | sentence-transformers | 102 | 0 | null | text-ranking | {"parameters": {"I64": 512, "F32": 33360385}, "total": 33360897} | [
".gitattributes",
"README.md",
"config.json",
"flax_model.msgpack",
"model.safetensors",
"onnx/model.onnx",
"onnx/model_O1.onnx",
"onnx/model_O2.onnx",
"onnx/model_O3.onnx",
"onnx/model_O4.onnx",
"onnx/model_qint8_arm64.onnx",
"onnx/model_qint8_avx512.onnx",
"onnx/model_qint8_avx512_vnni.onn... | 7b0235231ca2674cb8ca8f022859a6eba2b1c968 | [
"sentence-transformers",
"pytorch",
"jax",
"onnx",
"safetensors",
"openvino",
"bert",
"text-classification",
"transformers",
"text-ranking",
"en",
"dataset:sentence-transformers/msmarco",
"base_model:microsoft/MiniLM-L12-H384-uncased",
"base_model:quantized:microsoft/MiniLM-L12-H384-uncase... | null | {"architectures": ["BertForSequenceClassification"], "model_type": "bert", "tokenizer_config": {"cls_token": "[CLS]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | {"base_model": ["microsoft/MiniLM-L12-H384-uncased"], "datasets": ["sentence-transformers/msmarco"], "eval_results": null, "language": ["en"], "library_name": "sentence-transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-ranking"... | # Cross-Encoder for MS Marco
This model was trained on the [MS Marco Passage Ranking](https://github.com/microsoft/MSMARCO-Passage-Ranking) task.
The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch). Then sort the passages in a... | null | [
"apache-2.0"
] | [
"sentence-transformers/msmarco"
] | [
"en"
] | 33,360,897 | null | null | [
"BertForSequenceClassification",
"bert",
"AutoModelForSequenceClassification"
] | [
"text-ranking",
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
659bfad8356075e196a2dba0 | 7a9379003b337737 | 9ff9438e6447a43c | 32,000 | Unigram | null | true | true | true | true | 3 | Sequence | Metaspace | Metaspace | [
"Lowercase",
"Replace",
"Replace",
"Strip",
"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 27 | google/siglip-so400m-patch14-384 | google | null | 2,430,719 | 73,738,094 | False | 2024-01-08T13:38:32Z | 2024-09-26T08:21:46Z | transformers | 664 | 0 | null | zero-shot-image-classification | {"parameters": {"F32": 877960498}, "total": 877960498} | [
".gitattributes",
"README.md",
"config.json",
"model.safetensors",
"preprocessor_config.json",
"special_tokens_map.json",
"spiece.model",
"tokenizer.json",
"tokenizer_config.json"
] | 9fdffc58afc957d1a03a25b10dba0329ab15c2a3 | [
"transformers",
"safetensors",
"siglip",
"zero-shot-image-classification",
"vision",
"arxiv:2303.15343",
"arxiv:2305.13035",
"arxiv:2209.06794",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["SiglipModel"], "model_type": "siglip", "tokenizer_config": {"eos_token": "</s>", "pad_token": "</s>", "unk_token": "<unk>"}} | {
"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": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["vision"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_image... | # SigLIP (shape-optimized model)
SigLIP model pre-trained on WebLi at resolution 384x384. It was introduced in the paper [Sigmoid Loss for Language Image Pre-Training](https://arxiv.org/abs/2303.15343) by Zhai et al. and first released in [this repository](https://github.com/google-research/big_vision).
This model h... | null | [
"apache-2.0"
] | null | null | 877,960,498 | null | null | [
"AutoModelForZeroShotImageClassification",
"siglip",
"SiglipModel"
] | [
"zero-shot-image-classification"
] | [
"multimodal"
] | [
"text",
"image"
] | [
"logits"
] |
621ffdc136468d709f17ebd6 | e68b0b111c49dac2 | dd84a2b1ff97657d | 250,002 | Unigram | null | true | true | true | true | 5 | Precompiled | Sequence | Metaspace | [
"Precompiled"
] | [
"WhitespaceSplit",
"Metaspace"
] | [
"Metaspace"
] | 28 | papluca/xlm-roberta-base-language-detection | papluca | {
"models": [
{
"_id": "621ffdc036468d709f174364",
"id": "FacebookAI/xlm-roberta-base"
}
],
"relation": "finetune"
} | 542,633 | 60,624,428 | False | 2022-03-02T23:29:05Z | 2023-12-28T13:54:18Z | transformers | 375 | 0 | [{"name": "xlm-roberta-base-language-detection", "results": []}] | text-classification | {"parameters": {"I64": 514, "F32": 278059028}, "total": 278059542} | [
".gitattributes",
"README.md",
"config.json",
"model.safetensors",
"pytorch_model.bin",
"sentencepiece.bpe.model",
"special_tokens_map.json",
"tf_model.h5",
"tokenizer.json",
"tokenizer_config.json"
] | 9865598389ca9d95637462f743f683b51d75b87b | [
"transformers",
"pytorch",
"tf",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"multilingual",
"ar",
"bg",
"de",
"el",
"en",
"es",
"fr",
"hi",
"it",
"ja",
"nl",
"pl",
"pt",
"ru",
"sw",
"th",
"tr",
"ur",
"vi",
"zh",
"dataset:pap... | null | {"architectures": ["XLMRobertaForSequenceClassification"], "model_type": "xlm-roberta", "tokenizer_config": {"bos_token": "<s>", "eos_token": "</s>", "sep_token": "</s>", "cls_token": "<s>", "unk_token": "<unk>", "pad_token": "<pad>", "mask_token": {"content": "<mask>", "single_word": false, "lstrip": true, "rstrip": f... | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | {"base_model": "xlm-roberta-base", "datasets": "papluca/language-identification", "eval_results": [], "language": ["multilingual", "ar", "bg", "de", "el", "en", "es", "fr", "hi", "it", "ja", "nl", "pl", "pt", "ru", "sw", "th", "tr", "ur", "vi", "zh"], "library_name": null, "license": "mit", "license_name": null, "licen... | # xlm-roberta-base-language-detection
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the [Language Identification](https://huggingface.co/datasets/papluca/language-identification#additional-information) dataset.
## Model description
This model is an XLM-RoBERTa t... | null | [
"mit"
] | [
"papluca/language-identification"
] | [
"multilingual",
"ar",
"bg",
"de",
"el",
"en",
"es",
"fr",
"hi",
"it",
"ja",
"nl",
"pl",
"pt",
"ru",
"sw",
"th",
"tr",
"ur",
"vi",
"zh"
] | 278,059,542 | null | [
"accuracy",
"f1"
] | [
"AutoModelForSequenceClassification",
"XLMRobertaForSequenceClassification",
"xlm-roberta"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
64fff5087f91f94040eee912 | 53ecf467267e2824 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 0 | BAAI/bge-large-en-v1.5 | BAAI | null | 6,418,033 | 82,300,595 | False | 2023-09-12T05:20:08Z | 2024-02-21T02:51:44Z | sentence-transformers | 636 | 2 | [{"name": "bge-large-en-v1.5", "results": [{"task": {"type": "Classification"}, "dataset": {"type": "mteb/amazon_counterfactual", "name": "MTEB AmazonCounterfactualClassification (en)", "config": "en", "split": "test", "revision": "e8379541af4e31359cca9fbcf4b00f2671dba205"}, "metrics": [{"type": "accuracy", "value": 75... | feature-extraction | null | [
".gitattributes",
"1_Pooling/config.json",
"README.md",
"config.json",
"config_sentence_transformers.json",
"model.safetensors",
"modules.json",
"onnx/model.onnx",
"pytorch_model.bin",
"sentence_bert_config.json",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab... | d4aa6901d3a41ba39fb536a557fa166f842b0e09 | [
"sentence-transformers",
"pytorch",
"onnx",
"safetensors",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"mteb",
"en",
"arxiv:2401.03462",
"arxiv:2312.15503",
"arxiv:2311.13534",
"arxiv:2310.07554",
"arxiv:2309.07597",
"license:mit",
"model-index",
"text-embe... | null | {"architectures": ["BertModel"], "model_type": "bert", "tokenizer_config": {"cls_token": "[CLS]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": "feature-extraction",
"processor": "AutoTokenizer"
} | {"language": ["en"], "license": "mit", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers", "mteb"], "model-index": [{"name": "bge-large-en-v1.5", "results": [{"task": {"type": "Classification"}, "dataset": {"name": "MTEB AmazonCounterfactualClassification (en)", "type": "mteb/... | <h1 align="center">FlagEmbedding</h1>
<h4 align="center">
<p>
<a href=#model-list>Model List</a> |
<a href=#frequently-asked-questions>FAQ</a> |
<a href=#usage>Usage</a> |
<a href="#evaluation">Evaluation</a> |
<a href="#train">Train</a> |
<a href="#contact">Conta... | null | [
"mit"
] | null | [
"en"
] | null | null | null | [
"BertModel",
"AutoModel",
"bert"
] | [
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
65e9e11efd3543cc7c07ea30 | 53ecf467267e2824 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 0 | mixedbread-ai/mxbai-embed-large-v1 | mixedbread-ai | null | 2,639,489 | 68,078,326 | False | 2024-03-07T15:45:34Z | 2026-01-23T11:59:58Z | sentence-transformers | 775 | 2 | [{"name": "mxbai-angle-large-v1", "results": [{"task": {"type": "Classification"}, "dataset": {"type": "mteb/amazon_counterfactual", "name": "MTEB AmazonCounterfactualClassification (en)", "config": "en", "split": "test", "revision": "e8379541af4e31359cca9fbcf4b00f2671dba205"}, "metrics": [{"type": "accuracy", "value":... | feature-extraction | {"parameters": {"F16": 335141888}, "total": 335141888} | [
".gitattributes",
"1_Pooling/config.json",
"LICENSE",
"README.md",
"config.json",
"config_sentence_transformers.json",
"gguf/mxbai-embed-large-v1-f16.gguf",
"model.safetensors",
"modules.json",
"onnx/model.onnx",
"onnx/model_fp16.onnx",
"onnx/model_quantized.onnx",
"openvino/openvino_model.b... | b33106f585b9ce46904ad7443a3b52b7a63e231c | [
"sentence-transformers",
"onnx",
"safetensors",
"openvino",
"gguf",
"bert",
"feature-extraction",
"mteb",
"transformers.js",
"transformers",
"en",
"arxiv:2309.12871",
"license:apache-2.0",
"model-index",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | {"total": 334092288, "architecture": "bert", "context_length": 512, "causal": false, "bos_token": "[CLS]", "eos_token": "[SEP]"} | {"architectures": ["BertModel"], "model_type": "bert", "tokenizer_config": {"cls_token": "[CLS]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": "feature-extraction",
"processor": "AutoTokenizer"
} | {"language": ["en"], "library_name": "sentence-transformers", "license": "apache-2.0", "pipeline_tag": "feature-extraction", "tags": ["mteb", "transformers.js", "transformers"], "model-index": [{"name": "mxbai-angle-large-v1", "results": [{"task": {"type": "Classification"}, "dataset": {"name": "MTEB AmazonCounterfactu... | <br><br>
<p align="center">
<svg xmlns="http://www.w3.org/2000/svg" xml:space="preserve" viewBox="0 0 2020 1130" width="150" height="150" aria-hidden="true"><path fill="#e95a0f" d="M398.167 621.992c-1.387-20.362-4.092-40.739-3.851-61.081.355-30.085 6.873-59.139 21.253-85.976 10.487-19.573 24.09-36.822 40.662-51.515 16... | null | [
"apache-2.0"
] | null | [
"en"
] | 335,141,888 | 334,092,288 | null | [
"BertModel",
"AutoModel",
"bert"
] | [
"feature-extraction"
] | [
"multimodal"
] | [
"text"
] | [
"embeddings"
] |
621ffdc136468d709f17e81a | f7b02c0b9d6f991b | db117e9883c4d0ab | 50,257 | BPE | 50,000 | false | true | true | true | 1 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 29 | nlpconnect/vit-gpt2-image-captioning | nlpconnect | null | 267,724 | 59,641,972 | False | 2022-03-02T23:29:05Z | 2023-02-27T15:00:09Z | transformers | 927 | 0 | null | image-to-text | null | [
".gitattributes",
"README.md",
"config.json",
"merges.txt",
"preprocessor_config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | dc68f91c06a1ba6f15268e5b9c13ae7a7c514084 | [
"transformers",
"pytorch",
"vision-encoder-decoder",
"image-text-to-text",
"image-to-text",
"image-captioning",
"doi:10.57967/hf/0222",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["VisionEncoderDecoderModel"], "model_type": "vision-encoder-decoder", "tokenizer_config": {"unk_token": "<|endoftext|>", "bos_token": "<|endoftext|>", "eos_token": "<|endoftext|>"}} | {
"auto_model": "AutoModelForImageTextToText",
"custom_class": null,
"pipeline_tag": "image-text-to-text",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["image-to-text", "image-captioning"], "widget": [{"src": "https://huggingface.co/d... | # nlpconnect/vit-gpt2-image-captioning
This is an image captioning model trained by @ydshieh in [flax ](https://github.com/huggingface/transformers/tree/main/examples/flax/image-captioning) this is pytorch version of [this](https://huggingface.co/ydshieh/vit-gpt2-coco-en-ckpts).
# The Illustrated Image Captioning us... | null | [
"apache-2.0"
] | null | null | null | null | null | [
"AutoModelForImageTextToText",
"vision-encoder-decoder",
"VisionEncoderDecoderModel"
] | [
"image-text-to-text",
"image-to-text"
] | [
"multimodal"
] | [
"text",
"image"
] | [
"text"
] |
638e3b8c629b4d0a62cd6dcc | 61736c54febdeba6 | aa8af6ff55c14796 | 51,865 | BPE | 50,000 | false | true | true | true | 1,608 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 16 | openai/whisper-large-v2 | openai | null | 71,649 | 55,802,295 | False | 2022-12-05T18:42:20Z | 2024-02-29T10:57:50Z | transformers | 1,785 | 0 | null | automatic-speech-recognition | null | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"flax_model.msgpack",
"generation_config.json",
"merges.txt",
"model.safetensors",
"normalizer.json",
"preprocessor_config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tf_model.h5",
"tokenizer.json",
"tokenizer... | ae4642769ce2ad8fc292556ccea8e901f1530655 | [
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"whisper",
"automatic-speech-recognition",
"audio",
"hf-asr-leaderboard",
"en",
"zh",
"de",
"es",
"ru",
"ko",
"fr",
"ja",
"pt",
"tr",
"pl",
"ca",
"nl",
"ar",
"sv",
"it",
"id",
"hi",
"fi",
"vi",
"he",
"... | null | {"architectures": ["WhisperForConditionalGeneration"], "model_type": "whisper", "tokenizer_config": {"bos_token": "<|endoftext|>", "eos_token": "<|endoftext|>", "pad_token": "<|endoftext|>", "unk_token": "<|endoftext|>"}} | {
"auto_model": "AutoModelForSpeechSeq2Seq",
"custom_class": null,
"pipeline_tag": "automatic-speech-recognition",
"processor": "AutoProcessor"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en", "zh", "de", "es", "ru", "ko", "fr", "ja", "pt", "tr", "pl", "ca", "nl", "ar", "sv", "it", "id", "hi", "fi", "vi", "he", "uk", "el", "ms", "cs", "ro", "da", "hu", "ta", "no", "th", "ur", "hr", "bg", "lt", "la", "mi", "ml", "cy", "sk", "te", ... | # Whisper
Whisper is a pre-trained model for automatic speech recognition (ASR) and speech translation. Trained on 680k hours
of labelled data, Whisper models demonstrate a strong ability to generalise to many datasets and domains **without** the need
for fine-tuning.
Whisper was proposed in the paper [Robust Speec... | null | [
"apache-2.0"
] | null | [
"en",
"zh",
"de",
"es",
"ru",
"ko",
"fr",
"ja",
"pt",
"tr",
"pl",
"ca",
"nl",
"ar",
"sv",
"it",
"id",
"hi",
"fi",
"vi",
"he",
"uk",
"el",
"ms",
"cs",
"ro",
"da",
"hu",
"ta",
"no",
"th",
"ur",
"hr",
"bg",
"lt",
"la",
"mi",
"ml",
"cy",
"sk"... | null | null | null | [
"WhisperForConditionalGeneration",
"whisper",
"AutoModelForSpeechSeq2Seq"
] | [
"automatic-speech-recognition"
] | [
"multimodal"
] | [
"audio"
] | [
"text"
] |
621ffdc136468d709f1802a0 | 53ecf467267e2824 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 0 | sentence-transformers/bert-base-nli-mean-tokens | sentence-transformers | null | 161,939 | 54,783,074 | False | 2022-03-02T23:29:05Z | 2025-03-06T13:34:57Z | sentence-transformers | 40 | 0 | null | sentence-similarity | {"parameters": {"I64": 512, "F32": 109482240}, "total": 109482752} | [
".gitattributes",
"1_Pooling/config.json",
"README.md",
"added_tokens.json",
"config.json",
"config_sentence_transformers.json",
"flax_model.msgpack",
"model.safetensors",
"modules.json",
"onnx/model.onnx",
"onnx/model_O1.onnx",
"onnx/model_O2.onnx",
"onnx/model_O3.onnx",
"onnx/model_O4.on... | 160a52b38a51ae87295ec3eabcf11755e5d27a8d | [
"sentence-transformers",
"pytorch",
"tf",
"jax",
"rust",
"onnx",
"safetensors",
"openvino",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 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": null, "eval_results": null, "language": null, "library_name": "sentence-transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "sentence-similarity", "tags": ["sentence-transformers", "feature-extraction",... | **⚠️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: [SBERT.net - Pretrained Models](https://www.sbert.net/docs/pretrained_models.html)**
# sentence-transformers/bert-base-nli-mean-tokens
This is a [sentence-tran... | null | [
"apache-2.0"
] | null | null | 109,482,752 | null | null | [
"BertModel",
"AutoModel",
"bert"
] | [
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
621ffdc136468d709f17b623 | 852d97636d829c78 | d40f26db13a49dfa | 32,000 | BPE | 31,065 | true | true | true | true | 5 | Sequence | Metaspace | Metaspace | [
"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 30 | google/fnet-base | google | null | 625,131 | 57,763,611 | False | 2022-03-02T23:29:05Z | 2024-02-29T09:54:11Z | transformers | 18 | 0 | null | null | null | [
".gitattributes",
"README.md",
"config.json",
"pytorch_model.bin",
"rust_model.ot",
"special_tokens_map.json",
"spiece.model",
"tokenizer.json",
"tokenizer_config.json"
] | d89b6fad3cf5384848b783dc480f9685f49d008c | [
"transformers",
"pytorch",
"rust",
"fnet",
"pretraining",
"en",
"dataset:c4",
"arxiv:2105.03824",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["FNetForPreTraining"], "model_type": "fnet", "tokenizer_config": {"unk_token": "<unk>", "sep_token": "[SEP]", "pad_token": "<pad>", "cls_token": "[CLS]", "mask_token": {"content": "[MASK]", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}}} | {
"auto_model": "AutoModelForPreTraining",
"custom_class": null,
"pipeline_tag": "pretraining",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["c4"], "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": ["fnet"]} | # FNet base model
Pretrained model on English language using a masked language modeling (MLM) and next sentence prediction (NSP) objective. It was
introduced in [this paper](https://arxiv.org/abs/2105.03824) and first released in [this repository](https://github.com/google-research/google-research/tree/master/f_net).... | null | [
"apache-2.0"
] | [
"c4"
] | [
"en"
] | null | null | null | [
"FNetForPreTraining",
"fnet",
"AutoModelForPreTraining"
] | [
"pretraining"
] | null | null | null |
66fba7309482f97131bf08d6 | 344b7c49c2732e43 | 8386a692be535934 | 51,866 | BPE | 50,000 | false | true | true | true | 1,609 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 21 | openai/whisper-large-v3-turbo | openai | {
"models": [
{
"_id": "654a84cadff2f49007ce6c37",
"id": "openai/whisper-large-v3"
}
],
"relation": "finetune"
} | 5,038,910 | 72,108,416 | False | 2024-10-01T07:39:28Z | 2024-10-04T14:51:11Z | transformers | 2,871 | 8 | null | automatic-speech-recognition | null | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"generation_config.json",
"merges.txt",
"model.safetensors",
"normalizer.json",
"preprocessor_config.json",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | 41f01f3fe87f28c78e2fbf8b568835947dd65ed9 | [
"transformers",
"safetensors",
"whisper",
"automatic-speech-recognition",
"audio",
"en",
"zh",
"de",
"es",
"ru",
"ko",
"fr",
"ja",
"pt",
"tr",
"pl",
"ca",
"nl",
"ar",
"sv",
"it",
"id",
"hi",
"fi",
"vi",
"he",
"uk",
"el",
"ms",
"cs",
"ro",
"da",
"hu",
... | null | {"architectures": ["WhisperForConditionalGeneration"], "model_type": "whisper", "tokenizer_config": {"bos_token": "<|endoftext|>", "eos_token": "<|endoftext|>", "pad_token": "<|endoftext|>", "unk_token": "<|endoftext|>"}} | {
"auto_model": "AutoModelForSpeechSeq2Seq",
"custom_class": null,
"pipeline_tag": "automatic-speech-recognition",
"processor": "AutoProcessor"
} | {"base_model": ["openai/whisper-large-v3"], "datasets": null, "eval_results": null, "language": ["en", "zh", "de", "es", "ru", "ko", "fr", "ja", "pt", "tr", "pl", "ca", "nl", "ar", "sv", "it", "id", "hi", "fi", "vi", "he", "uk", "el", "ms", "cs", "ro", "da", "hu", "ta", "no", "th", "ur", "hr", "bg", "lt", "la", "mi", "... | # Whisper
Whisper is a state-of-the-art model for automatic speech recognition (ASR) and speech translation, proposed in the paper
[Robust Speech Recognition via Large-Scale Weak Supervision](https://huggingface.co/papers/2212.04356) by Alec Radford
et al. from OpenAI. Trained on >5M hours of labeled data, Whisper d... | null | [
"mit"
] | null | [
"en",
"zh",
"de",
"es",
"ru",
"ko",
"fr",
"ja",
"pt",
"tr",
"pl",
"ca",
"nl",
"ar",
"sv",
"it",
"id",
"hi",
"fi",
"vi",
"he",
"uk",
"el",
"ms",
"cs",
"ro",
"da",
"hu",
"ta",
"no",
"th",
"ur",
"hr",
"bg",
"lt",
"la",
"mi",
"ml",
"cy",
"sk"... | null | null | null | [
"WhisperForConditionalGeneration",
"whisper",
"AutoModelForSpeechSeq2Seq"
] | [
"automatic-speech-recognition"
] | [
"multimodal"
] | [
"audio"
] | [
"text"
] |
621ffdc136468d709f1802ed | aed290a0124eb522 | dd84a2b1ff97657d | 250,002 | Unigram | null | true | true | true | true | 5 | Precompiled | Sequence | Metaspace | [
"Precompiled"
] | [
"WhitespaceSplit",
"Metaspace"
] | [
"Metaspace"
] | 10 | sentence-transformers/paraphrase-multilingual-mpnet-base-v2 | sentence-transformers | null | 5,697,705 | 82,131,827 | False | 2022-03-02T23:29:05Z | 2025-08-19T10:29:38Z | sentence-transformers | 458 | 1 | null | sentence-similarity | {"parameters": {"I64": 514, "F32": 278043648}, "total": 278044162} | [
".gitattributes",
"1_Pooling/config.json",
"README.md",
"config.json",
"config_sentence_transformers.json",
"model.safetensors",
"modules.json",
"onnx/model.onnx",
"onnx/model_O1.onnx",
"onnx/model_O2.onnx",
"onnx/model_O3.onnx",
"onnx/model_O4.onnx",
"onnx/model_qint8_arm64.onnx",
"onnx/m... | 4328cf26390c98c5e3c738b4460a05b95f4911f5 | [
"sentence-transformers",
"pytorch",
"tf",
"onnx",
"safetensors",
"openvino",
"xlm-roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"text-embeddings-inference",
"multilingual",
"ar",
"bg",
"ca",
"cs",
"da",
"de",
"el",
"en",
"es",
"et",
"fa",
"fi",
... | null | {"architectures": ["XLMRobertaModel"], "model_type": "xlm-roberta", "tokenizer_config": {"bos_token": "<s>", "eos_token": "</s>", "sep_token": "</s>", "cls_token": "<s>", "unk_token": "<unk>", "pad_token": "<pad>", "mask_token": {"content": "<mask>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": ... | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": "feature-extraction",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["multilingual", "ar", "bg", "ca", "cs", "da", "de", "el", "en", "es", "et", "fa", "fi", "fr", "gl", "gu", "he", "hi", "hr", "hu", "hy", "id", "it", "ja", "ka", "ko", "ku", "lt", "lv", "mk", "mn", "mr", "ms", "my", "nb", "nl", "pl", "pt", "ro", "r... | # sentence-transformers/paraphrase-multilingual-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 become... | null | [
"apache-2.0"
] | null | [
"multilingual",
"ar",
"bg",
"ca",
"cs",
"da",
"de",
"el",
"en",
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"et",
"fa",
"fi",
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"lv",
"mk",
"mn",
"mr",
"ms",
"my",
"nb",
"nl",
"pl",
"pt",
"r... | 278,044,162 | null | null | [
"XLMRobertaModel",
"AutoModel",
"xlm-roberta"
] | [
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
6341a1f24c9a81858b2ed60e | 7a4cc4a6090329ff | ab8f40e5a89244c4 | 250,680 | BPE | 250,434 | false | true | true | true | 4 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 31 | bigscience/bloomz-560m | bigscience | null | 1,194,884 | 56,717,199 | False | 2022-10-08T16:14:42Z | 2023-05-27T17:27:11Z | transformers | 137 | 0 | [{"name": "bloomz-560m", "results": [{"task": {"type": "Coreference resolution"}, "dataset": {"type": "winogrande", "name": "Winogrande XL (xl)", "config": "xl", "split": "validation", "revision": "a80f460359d1e9a67c006011c94de42a8759430c"}, "metrics": [{"type": "Accuracy", "value": 52.41, "verified": false}]}, {"task"... | text-generation | {"parameters": {"F16": 559214592}, "total": 559214592} | [
".gitattributes",
"README.md",
"config.json",
"logs/logs/xp3capmixnewcodelonglossseq/main_log.txt",
"logs/tensorboard/xp3capmixnewcodelonglossseq/events.out.tfevents.1665051802.jean-zay-iam25.73114.0",
"logs/tensorboard/xp3capmixnewcodelonglossseq/events.out.tfevents.1665052484.jean-zay-iam17.3156474.0",
... | a2845d7e13dd12efae154a9f1c63fcc2e0cc4b05 | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"bloom",
"text-generation",
"ak",
"ar",
"as",
"bm",
"bn",
"ca",
"code",
"en",
"es",
"eu",
"fon",
"fr",
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"id",
"ig",
"ki",
"kn",
"lg",
"ln",
"ml",
"mr",
"ne",
"nso",
"ny",
"or",
"pa",
... | null | {"architectures": ["BloomForCausalLM"], "model_type": "bloom", "tokenizer_config": {"unk_token": "<unk>", "eos_token": "</s>", "bos_token": "<s>", "pad_token": "<pad>"}} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"datasets": ["bigscience/xP3"], "language": ["ak", "ar", "as", "bm", "bn", "ca", "code", "en", "es", "eu", "fon", "fr", "gu", "hi", "id", "ig", "ki", "kn", "lg", "ln", "ml", "mr", "ne", "nso", "ny", "or", "pa", "pt", "rn", "rw", "sn", "st", "sw", "ta", "te", "tn", "ts", "tum", "tw", "ur", "vi", "wo", "xh", "yo", "zh",... | 
# Table of Contents
1. [Model Summary](#model-summary)
2. [Use](#use)
3. [Limitations](#limitations)
4. [Training](#training)
5. [Evaluation](#evaluation)
7. [Citation](#citation)
# Model Summary
> We present BLOOMZ & mT0, a ... | null | [
"bigscience-bloom-rail-1.0"
] | [
"bigscience/xP3"
] | [
"ak",
"ar",
"as",
"bm",
"bn",
"ca",
"code",
"en",
"es",
"eu",
"fon",
"fr",
"gu",
"hi",
"id",
"ig",
"ki",
"kn",
"lg",
"ln",
"ml",
"mr",
"ne",
"nso",
"ny",
"or",
"pa",
"pt",
"rn",
"rw",
"sn",
"st",
"sw",
"ta",
"te",
"tn",
"ts",
"tum",
"tw",
... | 559,214,592 | null | null | [
"AutoModelForCausalLM",
"BloomForCausalLM",
"bloom"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
66e81cbcd683a3f4e5291bbf | 848b60b6cd304b65 | ffc911e785abe60e | 151,665 | BPE | 151,387 | true | true | true | true | 22 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 15 | Qwen/Qwen2.5-7B-Instruct | Qwen | {
"models": [
{
"_id": "66e6d06401e2adb00b0be996",
"id": "Qwen/Qwen2.5-7B"
}
],
"relation": "finetune"
} | 17,917,551 | 109,933,464 | False | 2024-09-16T11:55:40Z | 2025-01-12T02:10:10Z | transformers | 1,165 | 22 | null | text-generation | {"parameters": {"BF16": 7615616512}, "total": 7615616512} | [
".gitattributes",
"LICENSE",
"README.md",
"config.json",
"generation_config.json",
"merges.txt",
"model-00001-of-00004.safetensors",
"model-00002-of-00004.safetensors",
"model-00003-of-00004.safetensors",
"model-00004-of-00004.safetensors",
"model.safetensors.index.json",
"tokenizer.json",
"... | a09a35458c702b33eeacc393d103063234e8bc28 | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"chat",
"conversational",
"en",
"arxiv:2309.00071",
"arxiv:2407.10671",
"base_model:Qwen/Qwen2.5-7B",
"base_model:finetune:Qwen/Qwen2.5-7B",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure... | null | {"architectures": ["Qwen2ForCausalLM"], "model_type": "qwen2", "tokenizer_config": {"bos_token": null, "chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- 'You are Qwen, created by Aliba... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "Qwen/Qwen2.5-7B", "datasets": null, "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": "https://huggingface.co/Qwen/Qwen2.5-7B-Instruct/blob/main/LICENSE", "metrics": null, "model_name": null, "pipeline_tag": "text-gen... | # Qwen2.5-7B-Instruct
<a href="https://chat.qwenlm.ai/" target="_blank" style="margin: 2px;">
<img alt="Chat" src="https://img.shields.io/badge/%F0%9F%92%9C%EF%B8%8F%20Qwen%20Chat%20-536af5" style="display: inline-block; vertical-align: middle;"/>
</a>
## Introduction
Qwen2.5 is the latest series of Qwen large la... | null | [
"apache-2.0",
"https://huggingface.co/Qwen/Qwen2.5-7B-Instruct/blob/main/LICENSE"
] | null | [
"en"
] | 7,615,616,512 | null | null | [
"AutoModelForCausalLM",
"Qwen2ForCausalLM",
"qwen2"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
63526e37b2c3384b9fed5bd9 | 2d0341bd3a73bae6 | ef4806e2ade9022f | 32,100 | Unigram | null | true | true | true | true | 103 | Sequence | Metaspace | Metaspace | [
"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 32 | google/flan-t5-base | google | null | 1,191,745 | 55,446,360 | False | 2022-10-21T10:02:31Z | 2023-07-17T12:48:39Z | transformers | 1,059 | 0 | null | null | null | [
".gitattributes",
"README.md",
"config.json",
"flax_model.msgpack",
"generation_config.json",
"model.safetensors",
"pytorch_model.bin",
"special_tokens_map.json",
"spiece.model",
"tf_model.h5",
"tokenizer.json",
"tokenizer_config.json"
] | 7bcac572ce56db69c1ea7c8af255c5d7c9672fc2 | [
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"t5",
"text2text-generation",
"en",
"fr",
"ro",
"de",
"multilingual",
"dataset:svakulenk0/qrecc",
"dataset:taskmaster2",
"dataset:djaym7/wiki_dialog",
"dataset:deepmind/code_contests",
"dataset:lambada",
"dataset:gsm8k",
"dat... | null | {"architectures": ["T5ForConditionalGeneration"], "model_type": "t5", "tokenizer_config": {"eos_token": "</s>", "pad_token": "<pad>", "unk_token": "<unk>"}} | {
"auto_model": "AutoModelForSeq2SeqLM",
"custom_class": null,
"pipeline_tag": "text2text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["svakulenk0/qrecc", "taskmaster2", "djaym7/wiki_dialog", "deepmind/code_contests", "lambada", "gsm8k", "aqua_rat", "esnli", "quasc", "qed"], "eval_results": null, "language": ["en", "fr", "ro", "de", "multilingual"], "library_name": null, "license": "apache-2.0", "license_name": null, ... | # Model Card for FLAN-T5 base
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/flan2_architecture.jpg"
alt="drawing" width="600"/>
# Table of Contents
0. [TL;DR](#TL;DR)
1. [Model Details](#model-details)
2. [Usage](#usage)
3. [Uses](#uses)
4. [Bias, Ris... | null | [
"apache-2.0"
] | [
"svakulenk0/qrecc",
"taskmaster2",
"djaym7/wiki_dialog",
"deepmind/code_contests",
"lambada",
"gsm8k",
"aqua_rat",
"esnli",
"quasc",
"qed"
] | [
"en",
"fr",
"ro",
"de",
"multilingual"
] | null | null | null | [
"t5",
"T5ForConditionalGeneration",
"AutoModelForSeq2SeqLM"
] | [
"text2text-generation"
] | null | null | null |
621ffdc036468d709f174367 | 492a9f4f584c75aa | dd84a2b1ff97657d | 250,002 | Unigram | null | true | true | true | true | 5 | Precompiled | Sequence | Metaspace | [
"Precompiled"
] | [
"WhitespaceSplit",
"Metaspace"
] | [
"Metaspace"
] | 5 | FacebookAI/xlm-roberta-large-finetuned-conll03-english | FacebookAI | null | 88,231 | 51,181,739 | False | 2022-03-02T23:29:04Z | 2024-02-19T12:48:53Z | transformers | 183 | 0 | null | token-classification | {"parameters": {"F32": 559898632}, "total": 559898632} | [
".gitattributes",
"README.md",
"config.json",
"model.safetensors",
"onnx/added_tokens.json",
"onnx/config.json",
"onnx/model.onnx",
"onnx/model.onnx_data",
"onnx/sentencepiece.bpe.model",
"onnx/special_tokens_map.json",
"onnx/tokenizer.json",
"onnx/tokenizer_config.json",
"pytorch_model.bin"... | 18f95e9924f3f452df09cc90945073906ef18f1e | [
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"bg",
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"br",
"bs",
"ca",
"cs",
"cy",
"da",
"de",
"el",
"en",
"eo",
"es",
"et",
"eu",
"fa",
"fi",
"fr... | null | {"architectures": ["XLMRobertaForTokenClassification"], "model_type": "xlm-roberta", "tokenizer_config": {}} | {
"auto_model": "AutoModelForTokenClassification",
"custom_class": null,
"pipeline_tag": "token-classification",
"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-large-finetuned-conll03-english
# Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3. [Bias, Risks, and Limitations](#bias-risks-and-limitations)
4. [Training](#training)
5. [Evaluation](#evaluation)
6. [Environmental Impact](#environmental-impact)
7. [Technical Specifications](#te... | null | null | null | [
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"arxiv:2004.13922",
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] | null | {"architectures": ["BertForMaskedLM"], "model_type": "bert", "tokenizer_config": {}} | {
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<br>
<img src="https://github.com/ymcui/MacBERT/raw/master/pics/banner.png" width="500"/>
<br>
</p>
<p align="center">
<a href="https://github.com/ymcui/MacBERT/blob/master/LICENSE">
<img alt="GitHub" src="https://img.shields.io/github/license/ymcui/MacBERT.svg?color=blue&styl... | null | [
"apache-2.0"
] | null | [
"zh"
] | null | null | null | [
"AutoModelForMaskedLM",
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"BertForMaskedLM"
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"fill-mask"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
63974666f74f697677e32689 | 53ecf467267e2824 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 0 | Salesforce/blip-image-captioning-base | Salesforce | null | 3,166,587 | 56,146,154 | False | 2022-12-12T15:19:02Z | 2025-02-03T06:45:16Z | transformers | 846 | 0 | null | image-to-text | null | [
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"tf_model.h5",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | 82a37760796d32b1411fe092ab5d4e227313294b | [
"transformers",
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"tf",
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"image-text-to-text",
"image-captioning",
"image-to-text",
"arxiv:2201.12086",
"license:bsd-3-clause",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["BlipForConditionalGeneration"], "model_type": "blip", "tokenizer_config": {"cls_token": "[CLS]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
"auto_model": "AutoModelForImageTextToText",
"custom_class": null,
"pipeline_tag": "image-text-to-text",
"processor": "AutoProcessor"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": null, "license": "bsd-3-clause", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "image-to-text", "tags": ["image-captioning"], "languages": ["en"]} | # BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation
Model card for image captioning pretrained on COCO dataset - base architecture (with ViT base backbone).
| ![BLIP.gif](https://cdn-uploads.huggingface.co/production/uploads/1670928184033-62441d1d9fdefb55a0b7d12c... | null | [
"bsd-3-clause"
] | null | null | null | null | null | [
"blip",
"AutoModelForImageTextToText",
"BlipForConditionalGeneration"
] | [
"image-text-to-text",
"image-to-text"
] | [
"multimodal"
] | [
"text",
"image"
] | [
"text"
] |
621ffdc136468d709f1802d4 | c3198db55db7b028 | 84fb34b2e1636fc5 | 30,527 | WordPiece | null | true | true | true | true | 6 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 34 | sentence-transformers/multi-qa-mpnet-base-dot-v1 | sentence-transformers | null | 3,482,535 | 65,376,169 | False | 2022-03-02T23:29:05Z | 2025-08-19T10:21:26Z | sentence-transformers | 191 | 0 | null | sentence-similarity | {"parameters": {"I64": 514, "F32": 109486464}, "total": 109486978} | [
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"data_config.json",
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"en",
"dataset:flax-sentence-embeddings/stackexchange_xml",
"dataset:ms_marco",
"dataset:gooaq",
"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>"}} | {
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This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and was designed for **semantic search**. It has been trained on 215M (question, answer) pairs from diverse sources. For an introduction to semantic search,... | null | null | [
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"embedding-data/PAQ_pairs",
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"embedding-data/WikiAnswers"
] | [
"en"
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] | [
"logits",
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621ffdc036468d709f17434a | 30094a1eabdbef67 | db117e9883c4d0ab | 50,257 | BPE | 50,000 | false | true | true | true | 1 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 4 | openai-community/gpt2-large | openai-community | null | 3,114,433 | 51,120,858 | False | 2022-03-02T23:29:04Z | 2024-02-19T11:11:02Z | transformers | 348 | 0 | null | text-generation | {"parameters": {"F32": 811778816}, "total": 811778816} | [
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"en",
"arxiv:1910.09700",
"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": null} | # GPT-2 Large
## Table of Contents
- [Model Details](#model-details)
- [How To Get Started With the Model](#how-to-get-started-with-the-model)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [Training](#training)
- [Evaluation](#evaluation)
- [Environmental Impact](#environmental-imp... | null | [
"mit"
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"text"
] | [
"text"
] | [
"text"
] |
621ffdc136468d709f17b621 | 53ecf467267e2824 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 0 | google/electra-small-discriminator | google | null | 804,540 | 43,838,723 | False | 2022-03-02T23:29:05Z | 2024-02-29T10:20:20Z | transformers | 37 | 0 | null | null | null | [
".gitattributes",
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] | null | {"architectures": ["ElectraForPreTraining"], "model_type": "electra", "tokenizer_config": {}} | {
"auto_model": "AutoModelForPreTraining",
"custom_class": null,
"pipeline_tag": "pretraining",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "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": null, "thumbnail": "https://huggingface.co/front/thumbnails/google.png"} | ## ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators
**ELECTRA** is a new method for self-supervised language representation learning. It can be used to pre-train transformer networks using relatively little compute. ELECTRA models are trained to distinguish "real" input tokens vs "fake" inp... | null | [
"apache-2.0"
] | null | [
"en"
] | null | null | null | [
"ElectraForPreTraining",
"AutoModelForPreTraining",
"electra"
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"pretraining"
] | null | null | null |
66d99c7e7577df8b2d4cff73 | e68b0b111c49dac2 | dd84a2b1ff97657d | 250,002 | Unigram | null | true | true | true | true | 5 | Precompiled | Sequence | Metaspace | [
"Precompiled"
] | [
"WhitespaceSplit",
"Metaspace"
] | [
"Metaspace"
] | 28 | jinaai/jina-embeddings-v3 | jinaai | null | 2,934,837 | 63,301,798 | False | 2024-09-05T11:56:46Z | 2025-02-24T07:06:37Z | transformers | 1,135 | 1 | [{"name": "jina-embeddings-v3", "results": [{"dataset": {"config": "default", "name": "MTEB AFQMC (default)", "revision": "b44c3b011063adb25877c13823db83bb193913c4", "split": "validation", "type": "C-MTEB/AFQMC"}, "metrics": [{"type": "cosine_pearson", "value": 41.74237700998808, "verified": false}, {"type": "cosine_sp... | feature-extraction | {"parameters": {"BF16": 572310396}, "total": 572310396} | [
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"... | null | {"architectures": ["XLMRobertaModel"], "auto_map": {"AutoConfig": "jinaai/xlm-roberta-flash-implementation--configuration_xlm_roberta.XLMRobertaFlashConfig", "AutoModel": "jinaai/xlm-roberta-flash-implementation--modeling_lora.XLMRobertaLoRA", "AutoModelForMaskedLM": "jinaai/xlm-roberta-flash-implementation--modeling_x... | {
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} | {"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", "ja", "jv", "ka", "kk", "km", "kn", "ko", "ku", "ky", "la", "l... | <br><br>
<p align="center">
<img src="https://huggingface.co/datasets/jinaai/documentation-images/resolve/main/logo.webp" alt="Jina AI: Your Search Foundation, Supercharged!" width="150px">
</p>
<p align="center">
<b>The embedding model trained by <a href="https://jina.ai/"><b>Jina AI</b></a>.</b>
</p>
<p align="ce... | null | [
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65770c3426ef61bbf101d4da | d0082d9c422f1c41 | 0e7bc0977f200d1c | 32,000 | BPE | 58,980 | false | true | true | true | 3 | null | Metaspace | Sequence | null | [
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] | null | {"architectures": ["MistralForCausalLM"], "model_type": "mistral", "tokenizer_config": {"bos_token": "<s>", "chat_template": "{%- if messages[0]['role'] == 'system' %}\n {%- set system_message = messages[0]['content'] %}\n {%- set loop_messages = messages[1:] %}\n{%- else %}\n {%- set loop_messages = messages ... | {
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639861ac02d4b462c93d29f5 | 53ecf467267e2824 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
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] | 0 | Salesforce/blip-image-captioning-large | Salesforce | null | 1,572,616 | 45,731,127 | False | 2022-12-13T11:27:40Z | 2025-02-03T06:42:42Z | transformers | 1,460 | 3 | null | image-to-text | {"parameters": {"I64": 512, "F32": 469732924}, "total": 469733436} | [
".gitattributes",
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"tf_model.h5",
"tokenizer.json",
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"image-to-text",
"arxiv:2201.12086",
"license:bsd-3-clause",
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"region:us"
] | null | {"architectures": ["BlipForConditionalGeneration"], "model_type": "blip", "tokenizer_config": {"cls_token": "[CLS]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
"auto_model": "AutoModelForImageTextToText",
"custom_class": null,
"pipeline_tag": "image-text-to-text",
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} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": null, "license": "bsd-3-clause", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "image-to-text", "tags": ["image-captioning"], "languages": ["en"]} | # BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation
Model card for image captioning pretrained on COCO dataset - base architecture (with ViT large backbone).
| ![BLIP.gif](https://cdn-uploads.huggingface.co/production/uploads/1670928184033-62441d1d9fdefb55a0b7d12... | null | [
"bsd-3-clause"
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"blip",
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"text",
"image"
] | [
"text"
] |
63526f4c7e4cc3135fd0ff1a | 2d0341bd3a73bae6 | ef4806e2ade9022f | 32,100 | Unigram | null | true | true | true | true | 103 | Sequence | Metaspace | Metaspace | [
"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 32 | google/flan-t5-large | google | null | 407,757 | 39,796,665 | False | 2022-10-21T10:07:08Z | 2023-07-17T12:49:05Z | transformers | 877 | 0 | null | null | {"parameters": {"F32": 783150080}, "total": 783150080} | [
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"dat... | null | {"architectures": ["T5ForConditionalGeneration"], "model_type": "t5", "tokenizer_config": {"eos_token": "</s>", "pad_token": "<pad>", "unk_token": "<unk>"}} | {
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<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/flan2_architecture.jpg"
alt="drawing" width="600"/>
# Table of Contents
0. [TL;DR](#TL;DR)
1. [Model Details](#model-details)
2. [Usage](#usage)
3. [Uses](#uses)
4. [Bias, Ri... | null | [
"apache-2.0"
] | [
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"quasc",
"qed"
] | [
"en",
"fr",
"ro",
"de",
"multilingual"
] | 783,150,080 | null | null | [
"t5",
"T5ForConditionalGeneration",
"AutoModelForSeq2SeqLM"
] | [
"text2text-generation"
] | null | null | null |
621ffdc136468d709f17c9d0 | f0b30459222b62d4 | 70bd27c212bc339c | 50,265 | BPE | 50,000 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
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"ByteLevel"
] | 36 | j-hartmann/emotion-english-distilroberta-base | j-hartmann | null | 633,349 | 40,998,696 | False | 2022-03-02T23:29:05Z | 2023-01-02T13:03:10Z | transformers | 485 | 0 | null | text-classification | null | [
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# Description ℹ
With this model, you can classify emotions in English text data. The model was trained on 6 diverse datasets (see Appendix below) and predicts Ekman's 6 basic emotions, plus a neutral class:
1) anger 🤬
2) disgust 🤢
3) fear 😨
4) joy 😀
5) neutral 😐
6) sadness �... | null | null | null | [
"en"
] | null | null | null | [
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66232e66a69964fa9867af80 | 5b8a6156b5b6e013 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
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"BertPreTokenizer"
] | [
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] | 37 | Alibaba-NLP/gte-large-en-v1.5 | Alibaba-NLP | null | 1,299,064 | 49,909,840 | False | 2024-04-20T02:54:30Z | 2024-08-09T03:32:05Z | transformers | 234 | 0 | [{"name": "gte-large-en-v1.5", "results": [{"task": {"type": "Classification"}, "dataset": {"type": "mteb/amazon_counterfactual", "name": "MTEB AmazonCounterfactualClassification (en)", "config": "en", "split": "test", "revision": "e8379541af4e31359cca9fbcf4b00f2671dba205"}, "metrics": [{"type": "accuracy", "value": 73... | sentence-similarity | null | [
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# gte-large-en-v1.5
We introduce `gte-v1.5` series, upgraded `gte` embeddings that support the context length of up to **8192**, while further enhancing model performance.
The models are built upon the `transformer++` encoder [backbone](https://huggingface.co/Alibaba-NLP/ne... | null | [
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621ffdc036468d709f17433c | 53ecf467267e2824 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
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"endpoints_compatible",
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] | null | {"architectures": ["BertForQuestionAnswering"], "model_type": "bert", "tokenizer_config": {}} | {
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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": null} | # BERT large model (uncased) whole word masking finetuned on SQuAD
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 u... | null | [
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"processor": "AutoTokenizer"
} | {"language": ["en"], "license": "mit", "tags": ["mteb", "sentence_embedding", "feature_extraction", "sentence-transformers", "transformers", "transformers.js"], "model-index": [{"name": "UAE-Large-V1", "results": [{"task": {"type": "Classification"}, "dataset": {"name": "MTEB AmazonCounterfactualClassification (en)", "... | null | null | [
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] | 38 | smilegate-ai/kor_unsmile | smilegate-ai | null | 108,074 | 35,210,437 | False | 2022-03-28T01:03:23Z | 2022-03-28T01:34:57Z | transformers | 9 | 0 | null | text-classification | null | [
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6795ffcd88cd7c0294702a72 | 848b60b6cd304b65 | ffc911e785abe60e | 151,665 | BPE | 151,387 | true | true | true | true | 22 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 15 | Qwen/Qwen2.5-VL-7B-Instruct | Qwen | null | 4,611,358 | 51,675,220 | False | 2025-01-26T09:26:37Z | 2025-04-06T16:23:01Z | transformers | 1,476 | 8 | null | image-text-to-text | {"parameters": {"BF16": 8292166656}, "total": 8292166656} | [
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"chat_template.json",
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"model-00001-of-00005.safetensors",
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"model... | cc594898137f460bfe9f0759e9844b3ce807cfb5 | [
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"arxiv:2309.00071",
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"license:apache-2.0",
"eval-results",
"text-generation-inference",
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"deploy:azure",
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] | null | {"architectures": ["Qwen2_5_VLForConditionalGeneration"], "model_type": "qwen2_5_vl", "processor_config": {"chat_template": "{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% for message in messages %}{% if loop.first and message['role'] != 'system' %}<|im_start|>system\nYou are a h... | {
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"custom_class": null,
"pipeline_tag": "image-text-to-text",
"processor": "AutoProcessor"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "image-text-to-text", "tags": ["multimodal"]} | # Qwen2.5-VL-7B-Instruct
<a href="https://chat.qwenlm.ai/" target="_blank" style="margin: 2px;">
<img alt="Chat" src="https://img.shields.io/badge/%F0%9F%92%9C%EF%B8%8F%20Qwen%20Chat%20-536af5" style="display: inline-block; vertical-align: middle;"/>
</a>
## Introduction
In the past five months since Qwen2-VL’s r... | null | [
"apache-2.0"
] | null | [
"en"
] | 8,292,166,656 | null | null | [
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"image"
] | [
"text"
] |
63ee95d76349d1a6706e750f | 52bad4bf7b21579a | 70bd27c212bc339c | 50,265 | BPE | 50,000 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 39 | laion/clap-htsat-fused | laion | null | 26,226,836 | 126,555,152 | False | 2023-02-16T20:45:11Z | 2026-01-12T00:41:49Z | transformers | 65 | 2 | null | audio-classification | {"parameters": {"I64": 50180, "F32": 153508136}, "total": 153558316} | [
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"preprocessor_config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | 365dea6ef167def6676140ed93bbc43f84dabb28 | [
"transformers",
"pytorch",
"safetensors",
"clap",
"feature-extraction",
"zero-shot audio classification",
"zero-shot audio retrieval",
"audio-classification",
"en",
"arxiv:2211.06687",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["ClapModel"], "model_type": "clap", "tokenizer_config": {"bos_token": "<s>", "cls_token": "<s>", "eos_token": "</s>", "mask_token": "<mask>", "pad_token": "<pad>", "sep_token": "</s>", "unk_token": "<unk>"}} | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": "feature-extraction",
"processor": "AutoProcessor"
} | {"base_model": null, "datasets": null, "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": "audio-classification", "tags": ["zero-shot audio classification", "zero-shot audio retrieval"]... | # Model card for CLAP
Model card for CLAP: Contrastive Language-Audio Pretraining
# Table of Contents
0. [TL;DR](#TL;DR)
1. [Model Details](#model-details)
2. [Usage](#usage)
3. [Uses](#uses)
4. [Citation]... | null | [
"apache-2.0"
] | null | [
"en"
] | 153,558,316 | null | null | [
"clap",
"AutoModel",
"ClapModel"
] | [
"audio-classification",
"feature-extraction"
] | [
"multimodal",
"audio"
] | [
"text",
"audio"
] | [
"logits",
"embeddings"
] |
621ffdc136468d709f1815cc | 05a44a7965175fe3 | 0f37a3ecd59b06e8 | 50,265 | BPE | 50,004 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 40 | w11wo/indonesian-roberta-base-posp-tagger | w11wo | {
"models": [
{
"_id": "621ffdc136468d709f17b191",
"id": "flax-community/indonesian-roberta-base"
}
],
"relation": "finetune"
} | 2,815,953 | 47,474,579 | False | 2022-03-02T23:29:05Z | 2024-02-19T11:03:26Z | transformers | 10 | 0 | [{"name": "indonesian-roberta-base-posp-tagger", "results": [{"task": {"name": "Token Classification", "type": "token-classification"}, "dataset": {"name": "indonlu", "type": "indonlu", "config": "posp", "split": "test", "args": "posp"}, "metrics": [{"name": "Precision", "type": "precision", "value": 0.9625100240577386... | token-classification | {"parameters": {"F32": 124075034}, "total": 124075034} | [
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"runs/Feb19_11-00-22_bookbot-h100/events.out.tfevents.1708340574.bookbot-h100.8811.1",
"special_tokens_map.json... | 5067796360930a7b9c5e1d76e1ab2b8c6b4cd103 | [
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"ind",
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"base_model:flax-community/indonesian-roberta-base",
"base_model:finetune:flax-community/indonesian-roberta-base",
"license:mit",
"model-ind... | null | {"architectures": ["RobertaForTokenClassification"], "model_type": "roberta", "tokenizer_config": {"bos_token": "<s>", "cls_token": "<s>", "eos_token": "</s>", "mask_token": "<mask>", "pad_token": "<pad>", "sep_token": "</s>", "unk_token": "<unk>"}} | {
"auto_model": "AutoModelForTokenClassification",
"custom_class": null,
"pipeline_tag": "token-classification",
"processor": "AutoTokenizer"
} | {"base_model": "flax-community/indonesian-roberta-base", "datasets": ["indonlu"], "language": ["ind"], "license": "mit", "metrics": ["precision", "recall", "f1", "accuracy"], "tags": ["generated_from_trainer"], "model-index": [{"name": "indonesian-roberta-base-posp-tagger", "results": [{"task": {"type": "token-classifi... | <!-- 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. -->
# indonesian-roberta-base-posp-tagger
This model is a fine-tuned version of [flax-community/indonesian-roberta-base](https://huggin... | null | [
"mit"
] | [
"indonlu"
] | [
"ind"
] | 124,075,034 | null | [
"precision",
"recall",
"f1",
"accuracy"
] | [
"roberta",
"AutoModelForTokenClassification",
"RobertaForTokenClassification"
] | [
"token-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
6295e4fedebdf42eb5920eff | e68b0b111c49dac2 | dd84a2b1ff97657d | 250,002 | Unigram | null | true | true | true | true | 5 | Precompiled | Sequence | Metaspace | [
"Precompiled"
] | [
"WhitespaceSplit",
"Metaspace"
] | [
"Metaspace"
] | 28 | M-CLIP/XLM-Roberta-Large-Vit-B-32 | M-CLIP | null | 11,502 | 33,661,116 | False | 2022-05-31T09:50:54Z | 2022-09-15T10:45:49Z | transformers | 17 | 0 | null | null | null | [
".gitattributes",
"README.md",
"config.json",
"pytorch_model.bin",
"sentencepiece.bpe.model",
"special_tokens_map.json",
"tf_model.h5",
"tokenizer.json",
"tokenizer_config.json"
] | 85949a3ac1415027bdf7a5d0994bba8d992fe494 | [
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"tf",
"M-CLIP",
"multilingual",
"af",
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"am",
"ar",
"az",
"bn",
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"hi",
"hu",
"is",
"id",
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"ja",
"mk",
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"pt",
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... | null | {"architectures": ["SentenceModelWithLinearTransformation"], "model_type": "M-CLIP", "tokenizer_config": {"bos_token": "<s>", "eos_token": "</s>", "sep_token": "</s>", "cls_token": "<s>", "unk_token": "<unk>", "pad_token": "<pad>", "mask_token": {"content": "<mask>", "single_word": false, "lstrip": true, "rstrip": fals... | {
"auto_model": "SentenceModelWithLinearTransformation",
"custom_class": null,
"pipeline_tag": null,
"processor": null
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["multilingual", "af", "sq", "am", "ar", "az", "bn", "bs", "bg", "ca", "zh", "hr", "cs", "da", "nl", "en", "et", "fr", "de", "el", "hi", "hu", "is", "id", "it", "ja", "mk", "ml", "mr", "pl", "pt", "ro", "ru", "sr", "sl", "es", "sw", "sv", "tl", "t... | ## Multilingual-clip: XLM-Roberta-Large-Vit-B-32
Multilingual-CLIP extends OpenAI's English text encoders to multiple other languages. This model *only* contains the multilingual text encoder. The corresponding image model `ViT-B-32` can be retrieved via instructions found on OpenAI's [CLIP repository on Github](https... | null | null | null | [
"multilingual",
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"ml",
"mr",
"pl",
"pt",
"ro",
"ru",
"sr",
"sl",
"es",
"sw",
"sv",
"t... | null | null | null | [
"M-CLIP",
"SentenceModelWithLinearTransformation"
] | [
null
] | null | null | null |
621ffdc136468d709f1802eb | f02914cc3fa57434 | 84fb34b2e1636fc5 | 30,527 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 41 | sentence-transformers/paraphrase-mpnet-base-v2 | sentence-transformers | null | 2,094,626 | 38,727,192 | False | 2022-03-02T23:29:05Z | 2025-08-19T10:24:29Z | sentence-transformers | 48 | 0 | null | sentence-similarity | {"parameters": {"I64": 514, "F32": 109486464}, "total": 109486978} | [
".gitattributes",
"1_Pooling/config.json",
"README.md",
"config.json",
"config_sentence_transformers.json",
"model.safetensors",
"modules.json",
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"onnx/model_O1.onnx",
"onnx/model_O2.onnx",
"onnx/model_O3.onnx",
"onnx/model_O4.onnx",
"onnx/model_qint8_arm64.onnx",
"onnx/m... | 6cc9279c672dc57f94445ef259b28a1b736fec8f | [
"sentence-transformers",
"pytorch",
"tf",
"onnx",
"safetensors",
"openvino",
"mpnet",
"feature-extraction",
"sentence-similarity",
"transformers",
"text-embeddings-inference",
"arxiv:1908.10084",
"doi:10.57967/hf/2004",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["MPNetModel"], "model_type": "mpnet", "tokenizer_config": {"bos_token": {"content": "<s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "eos_token": {"content": "</s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, ... | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": "feature-extraction",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": "sentence-transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "sentence-similarity", "tags": ["sentence-transformers", "feature-extraction",... | # sentence-transformers/paraphrase-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 y... | null | [
"apache-2.0"
] | null | null | 109,486,978 | null | null | [
"MPNetModel",
"AutoModel",
"mpnet"
] | [
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
6795ff8f7534713f94918fc5 | 848b60b6cd304b65 | ffc911e785abe60e | 151,665 | BPE | 151,387 | true | true | true | true | 22 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 15 | Qwen/Qwen2.5-VL-3B-Instruct | Qwen | null | 6,652,979 | 72,496,091 | False | 2025-01-26T09:25:35Z | 2025-04-06T16:23:42Z | transformers | 629 | 3 | null | image-text-to-text | {"parameters": {"BF16": 3754622976}, "total": 3754622976} | [
".gitattributes",
"LICENSE",
"README.md",
"chat_template.json",
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"preprocessor_config.json",
"tokenizer.json",
"tokenizer_config.json"... | 66285546d2b821cf421d4f5eb2576359d3770cd3 | [
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"arxiv:2409.12191",
"arxiv:2308.12966",
"eval-results",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["Qwen2_5_VLForConditionalGeneration"], "model_type": "qwen2_5_vl", "processor_config": {"chat_template": "{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% for message in messages %}{% if loop.first and message['role'] != 'system' %}<|im_start|>system\nYou are a h... | {
"auto_model": "AutoModelForImageTextToText",
"custom_class": null,
"pipeline_tag": "image-text-to-text",
"processor": "AutoProcessor"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en"], "library_name": "transformers", "license": null, "license_name": "qwen-research", "license_link": "https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct/blob/main/LICENSE", "metrics": null, "model_name": null, "pipeline_tag": "image-text-to-t... | # Qwen2.5-VL-3B-Instruct
<a href="https://chat.qwenlm.ai/" target="_blank" style="margin: 2px;">
<img alt="Chat" src="https://img.shields.io/badge/%F0%9F%92%9C%EF%B8%8F%20Qwen%20Chat%20-536af5" style="display: inline-block; vertical-align: middle;"/>
</a>
## Introduction
In the past five months since Qwen2-VL’s r... | null | [
"qwen-research",
"https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct/blob/main/LICENSE"
] | null | [
"en"
] | 3,754,622,976 | null | null | [
"qwen2_5_vl",
"AutoModelForImageTextToText",
"Qwen2_5_VLForConditionalGeneration"
] | [
"image-text-to-text"
] | [
"multimodal"
] | [
"text",
"image"
] | [
"text"
] |
64d801d11a81ece17db72163 | 6949732424a8d297 | 8bd7d71767538b82 | 32,000 | BPE | 61,249 | true | false | true | true | 3 | Sequence | null | Sequence | [
"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 42 | petals-team/StableBeluga2 | petals-team | null | 2,711 | 33,281,594 | False | 2023-08-12T22:04:01Z | 2023-08-23T18:00:41Z | transformers | 20 | 0 | null | text-generation | {"parameters": {"BF16": 68976648192}, "total": 68976648192} | [
".gitattributes",
"LICENSE.txt",
"LLAMA 2 LICENSE/LICENSE.txt",
"LLAMA 2 LICENSE/Responsible-Use-Guide.pdf",
"LLAMA 2 LICENSE/USE_POLICY.md",
"README.md",
"config.json",
"generation_config.json",
"model.safetensors.index.json",
"model_00001-of-00081.safetensors",
"model_00002-of-00081.safetensor... | 5e8703519b40c4c6c924c3ebfad0f7571282ff37 | [
"transformers",
"safetensors",
"llama",
"text-generation",
"en",
"dataset:conceptofmind/cot_submix_original",
"dataset:conceptofmind/flan2021_submix_original",
"dataset:conceptofmind/t0_submix_original",
"dataset:conceptofmind/niv2_submix_original",
"arxiv:2307.09288",
"arxiv:2306.02707",
"tex... | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": {"__type": "AddedToken", "content": "<s>", "lstrip": false, "normalized": true, "rstrip": false, "single_word": false}, "eos_token": {"__type": "AddedToken", "content": "</s>", "lstrip": false, "normalized": true, "rstrip":... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["conceptofmind/cot_submix_original", "conceptofmind/flan2021_submix_original", "conceptofmind/t0_submix_original", "conceptofmind/niv2_submix_original"], "eval_results": null, "language": ["en"], "library_name": null, "license": null, "license_name": null, "license_link": null, "metric... | # Stable Beluga 2
## Changes in this fork
This repository contains the model from the [stabilityai/StableBeluga2](https://huggingface.co/stabilityai/StableBeluga2) repository with the following changes:
1. **Storing weights in `bfloat16` instead of `float32`.**
This leads to 2x smaller files and a small quality ... | null | null | [
"conceptofmind/cot_submix_original",
"conceptofmind/flan2021_submix_original",
"conceptofmind/t0_submix_original",
"conceptofmind/niv2_submix_original"
] | [
"en"
] | 68,976,648,192 | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
621ffdc036468d709f174342 | 634a9186a6003172 | 5f9f736c7dc6fbfa | 28,996 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 8 | distilbert/distilbert-base-cased | distilbert | null | 171,977 | 30,776,155 | False | 2022-03-02T23:29:04Z | 2024-05-06T13:46:22Z | transformers | 60 | 0 | null | fill-mask | {"parameters": {"F32": 65812036}, "total": 65812036} | [
".gitattributes",
"README.md",
"config.json",
"model.onnx",
"model.safetensors",
"pytorch_model.bin",
"tf_model.h5",
"tokenizer.json",
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] | 6ea81172465e8b0ad3fddeed32b986cdcdcffcf0 | [
"transformers",
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"tf",
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"safetensors",
"distilbert",
"fill-mask",
"en",
"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",
"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": null} | # Model Card for DistilBERT base model (cased)
This model is a distilled version of the [BERT base model](https://huggingface.co/bert-base-cased).
It was introduced in [this paper](https://arxiv.org/abs/1910.01108).
The code for the distillation process can be found
[here](https://github.com/huggingface/transformers/t... | null | [
"apache-2.0"
] | [
"bookcorpus",
"wikipedia"
] | [
"en"
] | 65,812,036 | null | null | [
"distilbert",
"AutoModelForMaskedLM",
"DistilBertForMaskedLM"
] | [
"fill-mask"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
67e71b429c8ea10f6052e127 | 848b60b6cd304b65 | ffc911e785abe60e | 151,665 | BPE | 151,387 | true | true | true | true | 22 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 15 | Gensyn/Qwen2.5-0.5B-Instruct | Gensyn | {
"models": [
{
"_id": "66e6cfeb6998c3d86c14e163",
"id": "Qwen/Qwen2.5-0.5B"
}
],
"relation": "finetune"
} | 232,548 | 33,156,884 | False | 2025-03-28T21:57:22Z | 2025-03-31T13:32:41Z | transformers | 33 | 0 | null | text-generation | {"parameters": {"BF16": 494032768}, "total": 494032768} | [
".gitattributes",
"LICENSE",
"README.md",
"config.json",
"generation_config.json",
"merges.txt",
"model.safetensors",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | 317b7eb96312eda0c431d1dab1af958a308cb35e | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"chat",
"rl-swarm",
"gensyn",
"conversational",
"en",
"base_model:Qwen/Qwen2.5-0.5B",
"base_model:finetune:Qwen/Qwen2.5-0.5B",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["Qwen2ForCausalLM"], "model_type": "qwen2", "tokenizer_config": {"bos_token": null, "chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- 'You are Qwen, created by Aliba... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "Qwen/Qwen2.5-0.5B", "datasets": null, "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": "https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct/blob/main/LICENSE", "metrics": null, "model_name": null, "pipeline_tag": "te... | # Qwen2.5-0.5B-Instruct
## Introduction
This model is intended for use in the [Gensyn RL Swarm](https://www.gensyn.ai/articles/rl-swarm), to finetune locally using peer-to-peer reinforcement learning post-training.
Once finetuned, the model can be used as normal in any workflow, for details on how to do this please r... | null | [
"apache-2.0",
"https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct/blob/main/LICENSE"
] | null | [
"en"
] | 494,032,768 | null | null | [
"AutoModelForCausalLM",
"Qwen2ForCausalLM",
"qwen2"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
621ffdc136468d709f17adb9 | 35f8a93a304f6b78 | 70bd27c212bc339c | 50,265 | BPE | 50,000 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 6 | facebook/bart-large | facebook | null | 187,577 | 30,064,785 | False | 2022-03-02T23:29:05Z | 2022-06-03T10:00:20Z | transformers | 199 | 0 | null | feature-extraction | null | [
".gitattributes",
"README.md",
"config.json",
"flax_model.msgpack",
"merges.txt",
"pytorch_model.bin",
"rust_model.ot",
"tf_model.h5",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | cb48c1365bd826bd521f650dc2e0940aee54720c | [
"transformers",
"pytorch",
"tf",
"jax",
"rust",
"bart",
"feature-extraction",
"en",
"arxiv:1910.13461",
"license:apache-2.0",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["BartModel"], "model_type": "bart", "tokenizer_config": {}} | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": "feature-extraction",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "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": null} | # BART (large-sized model)
BART model pre-trained on English language. It was introduced in the paper [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://arxiv.org/abs/1910.13461) by Lewis et al. and first released in [this repository](https://gi... | null | [
"apache-2.0"
] | null | [
"en"
] | null | null | null | [
"AutoModel",
"bart",
"BartModel"
] | [
"feature-extraction"
] | [
"multimodal"
] | [
"text"
] | [
"embeddings"
] |
6355f3a560c1b72f6269f44c | e68b0b111c49dac2 | dd84a2b1ff97657d | 250,002 | Unigram | null | true | true | true | true | 5 | Precompiled | Sequence | Metaspace | [
"Precompiled"
] | [
"WhitespaceSplit",
"Metaspace"
] | [
"Metaspace"
] | 28 | tsmatz/xlm-roberta-ner-japanese | tsmatz | {
"models": [
{
"_id": "621ffdc036468d709f174364",
"id": "FacebookAI/xlm-roberta-base"
}
],
"relation": "finetune"
} | 1,758 | 30,363,540 | False | 2022-10-24T02:08:37Z | 2024-09-28T19:41:39Z | transformers | 26 | 0 | [{"name": "xlm-roberta-ner-ja", "results": []}] | token-classification | {"parameters": {"I64": 514, "F32": 277459977}, "total": 277460491} | [
".gitattributes",
".gitignore",
"README.md",
"config.json",
"model.safetensors",
"pytorch_model.bin",
"sentencepiece.bpe.model",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"training_args.bin"
] | aba094e118d5ffc622e9b25e07edc49f9dd85feb | [
"transformers",
"pytorch",
"safetensors",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"ner",
"bert",
"ja",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"endpoints_compatible",
"deploy:azure",
"region:us"
... | null | {"architectures": ["RobertaForTokenClassification"], "model_type": "xlm-roberta", "tokenizer_config": {"bos_token": "<s>", "cls_token": "<s>", "eos_token": "</s>", "mask_token": {"__type": "AddedToken", "content": "<mask>", "lstrip": true, "normalized": true, "rstrip": false, "single_word": false}, "pad_token": "<pad>"... | {
"auto_model": "AutoModelForTokenClassification",
"custom_class": null,
"pipeline_tag": "token-classification",
"processor": "AutoTokenizer"
} | {"base_model": "xlm-roberta-base", "datasets": null, "eval_results": [], "language": ["ja"], "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": ["f1"], "model_name": "xlm-roberta-ner-ja", "pipeline_tag": null, "tags": ["generated_from_trainer", "ner", "bert"], "widget": [{"te... | <!-- 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-ner-japanese
(Japanese caption : 日本語の固有表現抽出のモデル)
This model is a fine-tuned version of [xlm-roberta-base](https://hu... | null | [
"mit"
] | null | [
"ja"
] | 277,460,491 | null | [
"f1"
] | [
"AutoModelForTokenClassification",
"RobertaForTokenClassification",
"xlm-roberta"
] | [
"token-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
67449267a8b7b9f766e74310 | 848b60b6cd304b65 | ffc911e785abe60e | 151,665 | BPE | 151,387 | true | true | true | true | 22 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 15 | trl-internal-testing/tiny-Qwen2ForCausalLM-2.5 | trl-internal-testing | null | 6,067,602 | 48,365,503 | False | 2024-11-25T15:06:15Z | 2025-12-19T19:10:57Z | transformers | 4 | 0 | null | text-generation | {"parameters": {"BF16": 2428632}, "total": 2428632} | [
".gitattributes",
"README.md",
"added_tokens.json",
"chat_template.jinja",
"config.json",
"generation_config.json",
"merges.txt",
"model.safetensors",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | 4b10ebee6e13a2669155516652960f50984399fd | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"trl",
"conversational",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["Qwen2ForCausalLM"], "model_type": "qwen2", "tokenizer_config": {"bos_token": null, "eos_token": "<|im_end|>", "pad_token": "<|endoftext|>", "unk_token": null}, "chat_template_jinja": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- mess... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": "transformers", "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["trl"]} | # Tiny Qwen2ForCausalLM
This is a minimal model built for unit tests in the [TRL](https://github.com/huggingface/trl) library. | null | null | null | null | 2,428,632 | null | null | [
"AutoModelForCausalLM",
"Qwen2ForCausalLM",
"qwen2"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
65f44de295b7d70871c368eb | ef709caed20f0111 | dd84a2b1ff97657d | 250,002 | Unigram | null | true | true | true | true | 5 | Sequence | Metaspace | Metaspace | [
"Precompiled",
"Strip",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 43 | BAAI/bge-reranker-v2-m3 | BAAI | null | 5,692,258 | 47,134,293 | False | 2024-03-15T13:32:18Z | 2024-06-24T14:08:45Z | sentence-transformers | 934 | 7 | null | text-classification | {"parameters": {"F32": 567755777}, "total": 567755777} | [
".gitattributes",
"README.md",
"assets/BEIR-bge-en-v1.5.png",
"assets/BEIR-e5-mistral.png",
"assets/CMTEB-retrieval-bge-zh-v1.5.png",
"assets/llama-index.png",
"assets/miracl-bge-m3.png",
"config.json",
"model.safetensors",
"sentencepiece.bpe.model",
"special_tokens_map.json",
"tokenizer.json"... | 953dc6f6f85a1b2dbfca4c34a2796e7dde08d41e | [
"sentence-transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"transformers",
"text-embeddings-inference",
"multilingual",
"arxiv:2312.15503",
"arxiv:2402.03216",
"license:apache-2.0",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["XLMRobertaForSequenceClassification"], "model_type": "xlm-roberta", "tokenizer_config": {"bos_token": "<s>", "cls_token": "<s>", "eos_token": "</s>", "mask_token": "<mask>", "pad_token": "<pad>", "sep_token": "</s>", "unk_token": "<unk>"}} | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["multilingual"], "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-classification", "tags": ["transformers", "sentence-transformers", "text-embedd... | # Reranker
**More details please refer to our Github: [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding/tree/master).**
- [Model List](#model-list)
- [Usage](#usage)
- [Fine-tuning](#fine-tune)
- [Evaluation](#evaluation)
- [Citation](#citation)
Different from embedding model, reranker uses question and docu... | null | [
"apache-2.0"
] | null | [
"multilingual"
] | 567,755,777 | null | null | [
"AutoModelForSequenceClassification",
"XLMRobertaForSequenceClassification",
"xlm-roberta"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
621ffdc136468d709f180295 | 5ef9eb7677e33416 | 70bd27c212bc339c | 50,265 | BPE | 50,000 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 19 | sentence-transformers/all-distilroberta-v1 | sentence-transformers | null | 890,069 | 30,235,483 | False | 2022-03-02T23:29:05Z | 2025-03-06T13:35:55Z | sentence-transformers | 42 | 0 | null | sentence-similarity | {"parameters": {"I64": 514, "F32": 82118400}, "total": 82118914} | [
".gitattributes",
"1_Pooling/config.json",
"README.md",
"config.json",
"config_sentence_transformers.json",
"data_config.json",
"merges.txt",
"model.safetensors",
"modules.json",
"onnx/model.onnx",
"onnx/model_O1.onnx",
"onnx/model_O2.onnx",
"onnx/model_O3.onnx",
"onnx/model_O4.onnx",
"o... | 842eaed40bee4d61673a81c92d5689a8fed7a09f | [
"sentence-transformers",
"pytorch",
"rust",
"onnx",
"safetensors",
"openvino",
"roberta",
"fill-mask",
"feature-extraction",
"sentence-similarity",
"transformers",
"en",
"dataset:s2orc",
"dataset:flax-sentence-embeddings/stackexchange_xml",
"dataset:ms_marco",
"dataset:gooaq",
"datas... | null | {"architectures": ["RobertaForMaskedLM"], "model_type": "roberta", "tokenizer_config": {"unk_token": "<unk>", "bos_token": "<s>", "eos_token": "</s>", "sep_token": "</s>", "cls_token": "<s>", "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-distilroberta-v1
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... | 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"
] | 82,118,914 | null | null | [
"roberta",
"AutoModelForMaskedLM",
"RobertaForMaskedLM"
] | [
"fill-mask",
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
664833b04fa4afb44609f6cc | 790e04669e5722ca | cad4902422b140fb | 64,000 | BPE | 110,029 | true | false | true | true | 5 | Sequence | null | Sequence | [
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse"
] | 44 | dphn/dolphin-2.9.1-yi-1.5-34b | dphn | {
"models": [
{
"_id": "663f2d01b8f1e7a6aa1f27cc",
"id": "01-ai/Yi-1.5-34B"
}
],
"relation": "finetune"
} | 4,532,575 | 49,421,290 | False | 2024-05-18T04:50:56Z | 2025-09-08T05:56:56Z | transformers | 58 | 0 | null | text-generation | {"parameters": {"BF16": 34388917248}, "total": 34388917248} | [
".gitattributes",
"README.md",
"config.json",
"generation_config.json",
"model-00001-of-00015.safetensors",
"model-00002-of-00015.safetensors",
"model-00003-of-00015.safetensors",
"model-00004-of-00015.safetensors",
"model-00005-of-00015.safetensors",
"model-00006-of-00015.safetensors",
"model-0... | 0141cba238d0faad09bc240ea7af14c9ea5aec44 | [
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"axolotl",
"conversational",
"dataset:cognitivecomputations/Dolphin-2.9",
"dataset:teknium/OpenHermes-2.5",
"dataset:m-a-p/CodeFeedback-Filtered-Instruction",
"dataset:cognitivecomputations/dolphin-coder",
"da... | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": "<|startoftext|>", "chat_template": "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content']... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "01-ai/Yi-1.5-34B", "datasets": ["cognitivecomputations/Dolphin-2.9", "teknium/OpenHermes-2.5", "m-a-p/CodeFeedback-Filtered-Instruction", "cognitivecomputations/dolphin-coder", "cognitivecomputations/samantha-data", "microsoft/orca-math-word-problems-200k", "Locutusque/function-calling-chatml", "internl... | # Dolphin 2.9.1 Yi 1.5 34b 🐬
Curated and trained by Eric Hartford, Lucas Atkins, and Fernando Fernandes, and Cognitive Computations
This is our most spectacular outcome ever. FFT, all parameters, 16bit. 77.4 MMLU on 34b. And it talks like a dream.
Although the max positional embeddings is 4k, we used rope theta o... | null | [
"apache-2.0"
] | [
"cognitivecomputations/Dolphin-2.9",
"teknium/OpenHermes-2.5",
"m-a-p/CodeFeedback-Filtered-Instruction",
"cognitivecomputations/dolphin-coder",
"cognitivecomputations/samantha-data",
"microsoft/orca-math-word-problems-200k",
"Locutusque/function-calling-chatml",
"internlm/Agent-FLAN"
] | null | 34,388,917,248 | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
6447714d3411a0902bad9607 | 3a732ab237c338af | c21f0239d1cf0f86 | 65,024 | BPE | 64,784 | false | true | false | true | 12 | null | Sequence | ByteLevel | null | [
"Punctuation",
"ByteLevel",
"Digits",
"Split"
] | [
"ByteLevel"
] | 45 | tiiuae/falcon-7b-instruct | tiiuae | null | 58,305 | 26,730,850 | False | 2023-04-25T06:21:01Z | 2024-10-12T13:20:03Z | transformers | 1,031 | 0 | null | text-generation | {"parameters": {"BF16": 7217189760}, "total": 7217189760} | [
".gitattributes",
"README.md",
"config.json",
"configuration_falcon.py",
"coreml/text-generation/falcon-7b-64-float32.mlpackage/Data/com.apple.CoreML/model.mlmodel",
"coreml/text-generation/falcon-7b-64-float32.mlpackage/Data/com.apple.CoreML/weights/weight.bin",
"coreml/text-generation/falcon-7b-64-flo... | 8782b5c5d8c9290412416618f36a133653e85285 | [
"transformers",
"pytorch",
"coreml",
"safetensors",
"falcon",
"text-generation",
"conversational",
"custom_code",
"en",
"dataset:tiiuae/falcon-refinedweb",
"arxiv:2205.14135",
"arxiv:1911.02150",
"arxiv:2005.14165",
"arxiv:2104.09864",
"arxiv:2306.01116",
"license:apache-2.0",
"text-... | null | {"architectures": ["FalconForCausalLM"], "auto_map": {"AutoConfig": "configuration_falcon.FalconConfig", "AutoModel": "modeling_falcon.FalconModel", "AutoModelForSequenceClassification": "modeling_falcon.FalconForSequenceClassification", "AutoModelForTokenClassification": "modeling_falcon.FalconForTokenClassification",... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["tiiuae/falcon-refinedweb"], "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": null, "inference": true, "new_version": "tiiuae/falcon-11... | # ✨ Falcon-7B-Instruct
**Falcon-7B-Instruct is a 7B parameters causal decoder-only model built by [TII](https://www.tii.ae) based on [Falcon-7B](https://huggingface.co/tiiuae/falcon-7b) and finetuned on a mixture of chat/instruct datasets. It is made available under the Apache 2.0 license.**
*Paper coming soon 😊.*
... | null | [
"apache-2.0"
] | [
"tiiuae/falcon-refinedweb"
] | [
"en"
] | 7,217,189,760 | null | null | [
"AutoModelForCausalLM",
"falcon",
"FalconForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
658fb85235c41262d661dc48 | e23b6bdbe047bb7e | 8bd7d71767538b82 | 32,000 | BPE | 61,249 | true | false | true | true | 3 | Sequence | null | Sequence | [
"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 46 | TinyLlama/TinyLlama-1.1B-Chat-v1.0 | TinyLlama | null | 2,817,808 | 35,656,120 | False | 2023-12-30T06:27:30Z | 2024-03-17T05:07:08Z | transformers | 1,556 | 5 | null | text-generation | {"parameters": {"BF16": 1100048384}, "total": 1100048384} | [
".gitattributes",
"README.md",
"config.json",
"eval_results.json",
"generation_config.json",
"model.safetensors",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer.model",
"tokenizer_config.json"
] | fe8a4ea1ffedaf415f4da2f062534de366a451e6 | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"en",
"dataset:cerebras/SlimPajama-627B",
"dataset:bigcode/starcoderdata",
"dataset:HuggingFaceH4/ultrachat_200k",
"dataset:HuggingFaceH4/ultrafeedback_binarized",
"license:apache-2.0",
"text-generation-inference",
... | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": "<s>", "chat_template": "{% for message in messages %}\n{% if message['role'] == 'user' %}\n{{ '<|user|>\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'system' %}\n{{ '<|system|>\n' + message['content']... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["cerebras/SlimPajama-627B", "bigcode/starcoderdata", "HuggingFaceH4/ultrachat_200k", "HuggingFaceH4/ultrafeedback_binarized"], "eval_results": null, "language": ["en"], "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_na... | <div align="center">
# TinyLlama-1.1B
</div>
https://github.com/jzhang38/TinyLlama
The TinyLlama project aims to **pretrain** a **1.1B Llama model on 3 trillion tokens**. With some proper optimization, we can achieve this within a span of "just" 90 days using 16 A100-40G GPUs 🚀🚀. The training has started on 2023-0... | null | [
"apache-2.0"
] | [
"cerebras/SlimPajama-627B",
"bigcode/starcoderdata",
"HuggingFaceH4/ultrachat_200k",
"HuggingFaceH4/ultrafeedback_binarized"
] | [
"en"
] | 1,100,048,384 | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
66e98d74d4219fc3f1db215f | 848b60b6cd304b65 | ffc911e785abe60e | 151,665 | BPE | 151,387 | true | true | true | true | 22 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 15 | Qwen/Qwen2.5-3B-Instruct | Qwen | {
"models": [
{
"_id": "66e6d03f69d1083dd982a18c",
"id": "Qwen/Qwen2.5-3B"
}
],
"relation": "finetune"
} | 7,604,571 | 70,809,504 | False | 2024-09-17T14:08:52Z | 2024-09-25T12:33:00Z | transformers | 426 | 9 | null | text-generation | {"parameters": {"BF16": 3085938688}, "total": 3085938688} | [
".gitattributes",
"LICENSE",
"README.md",
"config.json",
"generation_config.json",
"merges.txt",
"model-00001-of-00002.safetensors",
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] | aa8e72537993ba99e69dfaafa59ed015b17504d1 | [
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] | null | {"architectures": ["Qwen2ForCausalLM"], "model_type": "qwen2", "tokenizer_config": {"bos_token": null, "chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- 'You are Qwen, created by Aliba... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "Qwen/Qwen2.5-3B", "datasets": null, "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "other", "license_name": "qwen-research", "license_link": "https://huggingface.co/Qwen/Qwen2.5-3B-Instruct/blob/main/LICENSE", "metrics": null, "model_name": null, "pipeline_tag": "te... | # Qwen2.5-3B-Instruct
## Introduction
Qwen2.5 is the latest series of Qwen large language models. For Qwen2.5, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters. Qwen2.5 brings the following improvements upon Qwen2:
- Significantly **more know... | null | [
"other",
"qwen-research",
"https://huggingface.co/Qwen/Qwen2.5-3B-Instruct/blob/main/LICENSE"
] | null | [
"en"
] | 3,085,938,688 | null | null | [
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] | [
"text"
] | [
"text"
] |
680da718233834890aa01f51 | 7428ef9ae5ef868f | 67da9c236fa81925 | 151,669 | BPE | 151,387 | true | true | true | true | 26 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 47 | Qwen/Qwen3-0.6B | Qwen | {
"models": [
{
"_id": "680f0da2f99a20ddfc9a9f26",
"id": "Qwen/Qwen3-0.6B-Base"
}
],
"relation": "finetune"
} | 13,807,272 | 74,174,117 | False | 2025-04-27T03:40:08Z | 2025-07-26T03:46:27Z | transformers | 1,160 | 13 | null | text-generation | {"parameters": {"BF16": 751632384}, "total": 751632384} | [
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