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68116567b39cfb007b5af8bd | XiaomiMiMo/MiMo-7B-RL | XiaomiMiMo | null | 44,084 | 398,651 | False | 2025-04-29T23:48:55Z | 2025-06-05T15:55:53Z | transformers | 276 | 2 | null | text-generation | null | [
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<picture>
<source srcset="https://github.com/XiaomiMiMo/MiMo/raw/main/figures/Xiaomi_MiMo_darkmode.png?raw=true" media="(prefers-color-scheme: dark)">
<img src="https://github.com/XiaomiMiMo/MiMo/raw/main/figures/Xiaomi_MiMo.png?raw=true" width="60%" alt="Xiaomi-MiMo" />
</picture>
</di... | null | [
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681b4fce81dc6c21d23f39af | jinaai/jina-embeddings-v4 | jinaai | null | 353,078 | 1,173,731 | False | 2025-05-07T12:19:26Z | 2025-09-02T06:33:21Z | transformers | 493 | 2 | null | visual-document-retrieval | {"parameters": {"BF16": 3754885248}, "total": 3754885248} | [
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<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>
# Jina Embed... | null | null | null | [
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6836842b2c00148ea407aaba | GSAI-ML/LLaDA-V | GSAI-ML | null | 4,473 | 78,332 | False | 2025-05-28T03:34:03Z | 2026-03-23T13:08:23Z | transformers | 26 | 2 | null | image-text-to-text | {"parameters": {"F16": 8433894944}, "total": 8433894944} | [
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] | null | {"architectures": ["LlavaLLaDAModelLM"], "auto_map": {"AutoConfig": "configuration_llada.LLaDAConfig", "AutoModel": "modeling_llada.LLaDAModelLM", "AutoModelForCausalLM": "modeling_llada.LLaDAModelLM"}, "model_type": "llava_llada", "tokenizer_config": {"bos_token": "<|startoftext|>", "chat_template": "{% set loop_messa... | {
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We introduce LLaDA-V, a competitive diffusion-based vision-language model, outperforming other diffusion MLLMs.
It was presented in the paper [LLaDA-V: Large Language Diffusion Models with Visual Instruction Tuning](https://huggingface.co/papers/2505.16933).
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683f3c2354280d882006f816 | google/gemma-3n-E4B-it | google | {
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68546d515b3e1b0a94166b13 | google/t5gemma-9b-9b-ul2 | google | {
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68547314d92e90c07bdf3f4f | google/t5gemma-9b-2b-ul2 | google | {
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],
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} | 19 | 1,049 | manual | 2025-06-19T20:29:08Z | 2025-07-09T14:25:51Z | transformers | 2 | 2 | null | text-generation | {"parameters": {"BF16": 12292375296}, "total": 12292375296} | [
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6854a009ba212e1a250b0b43 | microsoft/Phi-4-mini-flash-reasoning | microsoft | null | 1,258 | 105,403 | False | 2025-06-19T23:40:57Z | 2025-12-10T20:24:55Z | transformers | 272 | 2 | null | text-generation | null | [
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"generation_config.json"... | 1dff8163d28ec880ca2411c474ddc0a927792810 | [
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Phi-4-mini-flash-reasoning is a lightweight open model built upon synthetic data with a focus on high-quality, reasoning dense data further finetuned for more advanced math reasoning capabilities.
The model belongs to the Phi-4 model family and supports 64K token context length.
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68589d405ecded3f803051fe | microsoft/Phi-mini-MoE-instruct | microsoft | null | 100,579 | 384,370 | False | 2025-06-23T00:18:08Z | 2025-12-10T18:20:28Z | transformers | 32 | 2 | null | text-generation | {"parameters": {"BF16": 7647632704}, "total": 7647632704} | [
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68589e60f9dc5990764cdafd | microsoft/Phi-tiny-MoE-instruct | microsoft | null | 535,065 | 1,390,061 | False | 2025-06-23T00:22:56Z | 2025-12-10T18:21:11Z | transformers | 35 | 2 | null | text-generation | {"parameters": {"BF16": 3755220288}, "total": 3755220288} | [
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685f880a35eb99ba16c7cfc9 | baidu/ERNIE-4.5-21B-A3B-PT | baidu | null | 31,826 | 471,803 | False | 2025-06-28T06:13:30Z | 2025-11-26T07:52:39Z | transformers | 167 | 2 | null | text-generation | null | [
".gitattributes",
"LICENSE",
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"added_tokens.json",
"chat_template.jinja",
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"model-00003-of-00009.safetensors",
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"model-00005-of-00009.s... | 87db95487941cb39592ee0abca3b9155a6d19c5c | [
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"conversational",
"en",
"zh",
"license:apache-2.0",
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"deploy:azure",
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] | null | {"architectures": ["Ernie4_5_MoeForCausalLM"], "model_type": "ernie4_5_moe", "tokenizer_config": {"bos_token": "<s>", "cls_token": "<|begin_of_sentence|>", "eos_token": "</s>", "mask_token": "<mask:1>", "pad_token": "<unk>", "sep_token": "<|end_of_sentence|>", "unk_token": "<unk>", "use_default_system_prompt": false}, ... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en", "zh"], "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-generation", "tags": ["ERNIE4.5"]} | <div align="center" style="line-height: 1;">
<a href="https://ernie.baidu.com/" target="_blank" style="margin: 2px;">
<img alt="Chat" src="https://img.shields.io/badge/🤖_Chat-ERNIE_Bot-blue" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://huggingface.co/baidu" target="_blank" s... | null | [
"apache-2.0"
] | null | [
"en",
"zh"
] | null | null | null | [
"Ernie4_5_MoeForCausalLM",
"AutoModelForCausalLM",
"ernie4_5_moe"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
686fab94475469075ea83464 | LiquidAI/LFM2-350M | LiquidAI | null | 36,289 | 199,976 | False | 2025-07-10T12:01:24Z | 2026-02-11T17:54:54Z | transformers | 241 | 2 | null | text-generation | {"parameters": {"BF16": 354483968}, "total": 354483968} | [
".gitattributes",
"LICENSE",
"README.md",
"chat_template.jinja",
"config.json",
"generation_config.json",
"model.safetensors",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json"
] | 349e9396d37db096629f34c3be94bbbc966ebfa5 | [
"transformers",
"safetensors",
"lfm2",
"text-generation",
"liquid",
"edge",
"conversational",
"en",
"ar",
"zh",
"fr",
"de",
"ja",
"ko",
"es",
"arxiv:2511.23404",
"license:other",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["Lfm2ForCausalLM"], "model_type": "lfm2", "tokenizer_config": {"bos_token": "<|startoftext|>", "eos_token": "<|im_end|>", "pad_token": "<|pad|>", "use_default_system_prompt": false}, "chat_template_jinja": "{{- bos_token -}}\n{%- set system_prompt = \"\" -%}\n{%- set ns = namespace(system_prompt=\"\"... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en", "ar", "zh", "fr", "de", "ja", "ko", "es"], "library_name": "transformers", "license": "other", "license_name": "lfm1.0", "license_link": "LICENSE", "metrics": null, "model_name": null, "pipeline_tag": "text-generation", "tags": ["liquid", "... | <center>
<div style="text-align: center;">
<img
src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/7_6D7rWrLxp2hb6OHSV1p.png"
alt="Liquid AI"
style="width: 100%; max-width: 66%; height: auto; display: inline-block; margin-bottom: 0.5em; margin-top: 0.5em;"
/>
</div>
<d... | null | [
"other",
"lfm1.0",
"LICENSE"
] | null | [
"en",
"ar",
"zh",
"fr",
"de",
"ja",
"ko",
"es"
] | 354,483,968 | null | null | [
"Lfm2ForCausalLM",
"AutoModelForCausalLM",
"lfm2"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
687de260234339fed21e768a | Qwen/Qwen3-235B-A22B-Instruct-2507 | Qwen | null | 184,380 | 1,064,329 | False | 2025-07-21T06:46:56Z | 2025-09-17T06:52:55Z | transformers | 770 | 2 | null | text-generation | null | [
".gitattributes",
"LICENSE",
"README.md",
"config.json",
"config_1m.json",
"generation_config.json",
"merges.txt",
"model-00001-of-00118.safetensors",
"model-00002-of-00118.safetensors",
"model-00003-of-00118.safetensors",
"model-00004-of-00118.safetensors",
"model-00005-of-00118.safetensors",... | ac9c66cc9b46af7306746a9250f23d47083d689e | [
"transformers",
"safetensors",
"qwen3_moe",
"text-generation",
"conversational",
"arxiv:2402.17463",
"arxiv:2407.02490",
"arxiv:2501.15383",
"arxiv:2404.06654",
"arxiv:2505.09388",
"license:apache-2.0",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["Qwen3MoeForCausalLM"], "model_type": "qwen3_moe", "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\\n' }}\n {%- endif %}\n {{- \"# Tools\\n\\nYou may... | {
"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": "apache-2.0", "license_name": null, "license_link": "https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507/blob/main/LICENSE", "metrics": null, "model_name": null, "pipeline_tag": "text-generati... | # Qwen3-235B-A22B-Instruct-2507
<a href="https://chat.qwen.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>
## Highlights
We introduce the updated version of the... | null | [
"apache-2.0",
"https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507/blob/main/LICENSE"
] | null | null | null | null | null | [
"qwen3_moe",
"AutoModelForCausalLM",
"Qwen3MoeForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
6887274f2626398dc2e9f540 | Qwen/Qwen3-30B-A3B-Instruct-2507 | Qwen | null | 1,004,466 | 9,618,078 | False | 2025-07-28T07:31:27Z | 2025-09-17T06:56:43Z | transformers | 791 | 2 | null | text-generation | null | [
".gitattributes",
"LICENSE",
"README.md",
"config.json",
"config_1m.json",
"generation_config.json",
"merges.txt",
"model-00001-of-00016.safetensors",
"model-00002-of-00016.safetensors",
"model-00003-of-00016.safetensors",
"model-00004-of-00016.safetensors",
"model-00005-of-00016.safetensors",... | 0d7cf23991f47feeb3a57ecb4c9cee8ea4a17bfe | [
"transformers",
"safetensors",
"qwen3_moe",
"text-generation",
"conversational",
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"arxiv:2407.02490",
"arxiv:2501.15383",
"arxiv:2404.06654",
"arxiv:2505.09388",
"license:apache-2.0",
"eval-results",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["Qwen3MoeForCausalLM"], "model_type": "qwen3_moe", "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\\n' }}\n {%- endif %}\n {{- \"# Tools\\n\\nYou may... | {
"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": "apache-2.0", "license_name": null, "license_link": "https://huggingface.co/Qwen/Qwen3-30B-A3B-Instruct-2507/blob/main/LICENSE", "metrics": null, "model_name": null, "pipeline_tag": "text-generation... | # Qwen3-30B-A3B-Instruct-2507
<a href="https://chat.qwen.ai/?model=Qwen3-30B-A3B-2507" 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>
## Highlights
We introduce the... | null | [
"apache-2.0",
"https://huggingface.co/Qwen/Qwen3-30B-A3B-Instruct-2507/blob/main/LICENSE"
] | null | null | null | null | null | [
"qwen3_moe",
"AutoModelForCausalLM",
"Qwen3MoeForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
688b25d301116efa6ae21836 | Qwen/Qwen3-Coder-30B-A3B-Instruct-FP8 | Qwen | null | 349,040 | 1,761,345 | False | 2025-07-31T08:14:11Z | 2025-12-03T08:20:23Z | transformers | 171 | 2 | null | text-generation | {"parameters": {"BF16": 636948480, "F8_E4M3": 29896998912}, "total": 30533947392} | [
".gitattributes",
"LICENSE",
"README.md",
"chat_template.jinja",
"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"... | dcaee4d4dfc5ee71ad501f01f530e5652438fde0 | [
"transformers",
"safetensors",
"qwen3_moe",
"text-generation",
"conversational",
"arxiv:2505.09388",
"license:apache-2.0",
"endpoints_compatible",
"fp8",
"deploy:azure",
"region:us"
] | null | {"architectures": ["Qwen3MoeForCausalLM"], "model_type": "qwen3_moe", "quantization_config": {"quant_method": "fp8"}, "tokenizer_config": {"bos_token": null, "chat_template": "{% macro render_extra_keys(json_dict, handled_keys) %}\n {%- if json_dict is mapping %}\n {%- for json_key in json_dict if json_key no... | {
"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": "apache-2.0", "license_name": null, "license_link": "https://huggingface.co/Qwen/Qwen3-Coder-30B-A3B-Instruct-FP8/blob/main/LICENSE", "metrics": null, "model_name": null, "pipeline_tag": "text-gener... | # Qwen3-Coder-30B-A3B-Instruct-FP8
<a href="https://chat.qwen.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>
## Highlights
**Qwen3-Coder** is available in mult... | null | [
"apache-2.0",
"https://huggingface.co/Qwen/Qwen3-Coder-30B-A3B-Instruct-FP8/blob/main/LICENSE"
] | null | null | 30,533,947,392 | null | null | [
"qwen3_moe",
"AutoModelForCausalLM",
"Qwen3MoeForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
688bcfca08ac46775b63b089 | nvidia/Llama-3_3-Nemotron-Super-49B-v1_5-FP8 | nvidia | null | 52,499 | 91,696 | False | 2025-07-31T20:19:22Z | 2025-10-15T16:21:30Z | transformers | 26 | 2 | null | text-generation | {"parameters": {"BF16": 2102411264, "F8_E4M3": 47764733952}, "total": 49867145216} | [
".gitattributes",
"BIAS.md",
"EXPLAINABILITY.md",
"PRIVACY.md",
"README.md",
"SAFETY&SECURITY.md",
"accuracy_chart.png",
"block_config.py",
"config.json",
"configuration_decilm.py",
"generation_config.json",
"hf_quant_config.json",
"llama_nemotron_toolcall_parser_no_streaming.py",
"model-0... | 04822723e77e036ddf2d24e83c6d469d3b009252 | [
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"nemotron-nas",
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"llama-3",
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"custom_code",
"en",
"arxiv:2411.19146",
"arxiv:2505.00949",
"arxiv:2502.00203",
"license:other",
"region:us"
] | null | {"architectures": ["DeciLMForCausalLM"], "auto_map": {"AutoConfig": "configuration_decilm.DeciLMConfig", "AutoModelForCausalLM": "modeling_decilm.DeciLMForCausalLM"}, "model_type": "nemotron-nas", "tokenizer_config": {"bos_token": "<|begin_of_text|>", "chat_template": "{% set bos = \"<|begin_of_text|>\" %}{%- set enabl... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": "modeling_decilm.DeciLMForCausalLM",
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"processor": null
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "other", "license_name": "nvidia-open-model-license", "license_link": "https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/", "metrics": null, "model_name": null,... | null | null | [
"other",
"nvidia-open-model-license",
"https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/"
] | null | [
"en"
] | 49,867,145,216 | null | null | [
"DeciLMForCausalLM",
"AutoModelForCausalLM",
"modeling_decilm.DeciLMForCausalLM",
"nemotron-nas"
] | [
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] | [
"text"
] | [
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688c2557d2f309e75f3b69f1 | cyankiwi/Qwen3-Coder-30B-A3B-Instruct-AWQ-4bit | cyankiwi | {
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{
"_id": "688b1597e5e83e19d1b3238a",
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} | 148,105 | 1,118,049 | False | 2025-08-01T02:24:23Z | 2026-03-23T07:20:34Z | transformers | 48 | 2 | null | text-generation | {"parameters": {"I64": 37248, "I32": 29896998912, "BF16": 1569404928}, "total": 5306567040} | [
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] | null | null | 5,306,567,040 | null | null | [
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688cf5afd1ff9e3396fbafb2 | btbtyler09/Qwen3-Coder-30B-A3B-Instruct-gptq-8bit | btbtyler09 | {
"models": [
{
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"id": "Qwen/Qwen3-Coder-30B-A3B-Instruct"
}
],
"relation": "quantized"
} | 370,094 | 371,503 | False | 2025-08-01T17:13:19Z | 2025-08-02T01:25:53Z | transformers | 5 | 2 | null | text-generation | {"parameters": {"F16": 1557215232, "I32": 7741538304}, "total": 9298753536} | [
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... | null | {"architectures": ["Qwen3MoeForCausalLM"], "model_type": "qwen3_moe", "quantization_config": {"bits": 8, "quant_method": "gptq"}, "tokenizer_config": {"bos_token": null, "eos_token": "<|im_end|>", "pad_token": "<unk>", "unk_token": null}, "chat_template_jinja": "{% macro render_item_list(item_list, tag_name='required')... | {
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} | {"base_model": "Qwen/Qwen3-Coder-30B-A3B-Instruct", "datasets": null, "eval_results": null, "language": null, "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": "https://huggingface.co/Qwen/Qwen3-Coder-30B-A3B/blob/main/LICENSE", "metrics": null, "model_name": null, "pipeline... | null | null | [
"apache-2.0",
"https://huggingface.co/Qwen/Qwen3-Coder-30B-A3B/blob/main/LICENSE"
] | null | null | 9,298,753,536 | null | null | [
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688d688965f4fb022741b249 | btbtyler09/Qwen3-Coder-30B-A3B-Instruct-gptq-4bit | btbtyler09 | {
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{
"_id": "688b1597e5e83e19d1b3238a",
"id": "Qwen/Qwen3-Coder-30B-A3B-Instruct"
}
],
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} | 75,207 | 95,180 | False | 2025-08-02T01:23:21Z | 2025-08-02T01:27:47Z | transformers | 3 | 2 | null | text-generation | {"parameters": {"I32": 29909581824, "F16": 622540800}, "total": 30532122624} | [
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"model-00004-of-00010.safetensors",
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... | null | {"architectures": ["Qwen3MoeForCausalLM"], "model_type": "qwen3_moe", "quantization_config": {"bits": 4, "quant_method": "gptq"}, "tokenizer_config": {"bos_token": null, "eos_token": "<|im_end|>", "pad_token": "<unk>", "unk_token": null}, "chat_template_jinja": "{% macro render_item_list(item_list, tag_name='required')... | {
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689478e7ed9a1b852584f01d | XiaomiMiMo/MiMo-VL-7B-RL-2508 | XiaomiMiMo | {
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} | 136,040 | 180,290 | False | 2025-08-07T09:59:03Z | 2025-08-21T08:09:45Z | transformers | 91 | 2 | null | image-text-to-text | {"parameters": {"BF16": 8306217216}, "total": 8306217216} | [
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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 MiM... | {
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"pipeline_tag": "image-text-to-text",
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} | {"base_model": ["XiaomiMiMo/MiMo-VL-7B-RL-2508"], "datasets": null, "eval_results": null, "language": null, "library_name": "transformers", "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "image-text-to-text", "tags": null} | <div align="center">
<picture>
<source srcset="https://github.com/XiaomiMiMo/MiMo-VL/raw/main/figures/Xiaomi_MiMo_darkmode.png?raw=true" media="(prefers-color-scheme: dark)">
<img src="https://github.com/XiaomiMiMo/MiMo-VL/raw/main/figures/Xiaomi_MiMo.png?raw=true" width="60%" alt="Xiaomi-MiMo" />
</picture... | null | [
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] | null | null | 8,306,217,216 | null | null | [
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689c5b2fb34c2fc90e285872 | swiss-ai/Apertus-8B-Instruct-2509 | swiss-ai | {
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} | 168,153 | 2,069,548 | False | 2025-08-13T09:30:23Z | 2025-11-14T11:00:08Z | transformers | 443 | 2 | null | text-generation | null | [
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... | null | {"architectures": ["ApertusForCausalLM"], "model_type": "apertus", "tokenizer_config": {"bos_token": "<s>", "eos_token": "<|assistant_end|>", "pad_token": "<pad>", "unk_token": "<unk>"}, "chat_template_jinja": "{%- macro render_typescript_type(param_spec, required_params, is_nullable=false) -%}\n {%- if param_spec.t... | {
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68a43e24da6d88eb52a7cebe | cartesia/azzurra-voice | cartesia | {
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],
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} | 2,276 | 13,869 | False | 2025-08-19T09:04:36Z | 2026-02-12T16:14:25Z | transformers | 16 | 2 | null | text-to-speech | null | [
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"token... | f41947b939470b49181f77d8d89827938a2a915b | [
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] | {"total": 1648942945, "architecture": "csm"} | {"architectures": ["CsmForConditionalGeneration"], "model_type": "csm", "tokenizer_config": {"bos_token": "<|begin_of_text|>", "eos_token": "<|end_of_text|>", "pad_token": "<|end_of_text|>"}, "chat_template_jinja": "{%- if messages | length != 1 -%}\n {{- raise_exception(\"This chat template requires exactly one mes... | {
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"pipeline_tag": "text-to-audio",
"processor": "AutoFeatureExtractor"
} | {"base_model": ["sesame/csm-1b"], "datasets": null, "eval_results": null, "language": ["it"], "library_name": "transformers", "license": "cc", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-to-speech", "tags": null} | # azzurra-voice 🇮🇹
`azzurra-voice` is a state-of-the-art, highly expressive text-to-speech (TTS) model for the Italian language, developed by [Cartesia](https://cartesia.one/).
Check out some audio samples in our [blog post](https://blog.cartesia.one/posts/introducing-azzurra-voice/).
This model is the first relea... | null | [
"cc"
] | null | [
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68aaebfbfe684542cfc51e66 | openbmb/MiniCPM-V-4_5 | openbmb | null | 102,524 | 459,686 | False | 2025-08-24T10:39:55Z | 2026-03-10T08:55:03Z | transformers | 1,076 | 2 | null | image-text-to-text | {"parameters": {"BF16": 8695895280}, "total": 8695895280} | [
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"dataset:openbmb/RLAIF-V-Dataset",
"arxiv:2509.18154",
"arxiv:2403.11703",
"license:apache-2.0... | null | {"architectures": ["MiniCPMV"], "auto_map": {"AutoConfig": "configuration_minicpm.MiniCPMVConfig", "AutoModel": "modeling_minicpmv.MiniCPMV", "AutoModelForCausalLM": "modeling_minicpmv.MiniCPMV"}, "model_type": "minicpmv", "tokenizer_config": {"bos_token": "<|im_start|>", "chat_template": "{%- if tools %}\n {{- '<|i... | {
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} | {"base_model": null, "datasets": ["openbmb/RLAIF-V-Dataset"], "eval_results": null, "language": ["multilingual"], "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": ["minicpm-v", "vision"... | <h1>A GPT-4o Level MLLM for Single Image, Multi Image and High-FPS Video Understanding on Your Phone</h1>
[GitHub](https://github.com/OpenBMB/MiniCPM-o) | [CookBook](https://github.com/OpenSQZ/MiniCPM-V-CookBook) | [Demo](http://101.126.42.235:30910/)</a>
## MiniCPM-V 4.5
**MiniCPM-V 4.5** is the latest and most ... | null | [
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68c971af2513da0b99b89f70 | baidu/Qianfan-VL-8B | baidu | null | 598 | 2,418 | False | 2025-09-16T14:18:23Z | 2025-09-19T10:32:29Z | transformers | 36 | 2 | null | image-text-to-text | {"parameters": {"BF16": 8808322048}, "total": 8808322048} | [
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68e431cb85dce231911b62fe | allenai/olmOCR-2-7B-1025 | allenai | {
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} | 379,270 | 848,048 | False | 2025-10-06T21:16:59Z | 2025-10-22T15:31:06Z | transformers | 141 | 2 | null | image-text-to-text | {"parameters": {"BF16": 8292166656}, "total": 8292166656} | [
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68ea220c98287fc5e7b33985 | Qwen/Qwen3-VL-4B-Instruct-FP8 | Qwen | {
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} | 26,180 | 250,866 | False | 2025-10-11T09:23:24Z | 2025-10-15T16:08:37Z | transformers | 55 | 2 | null | image-text-to-text | {"parameters": {"F32": 221760, "BF16": 1193456128, "F8_E4M3": 3633315840}, "total": 4826993728} | [
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68f2f124969f9c4368d222b8 | maya-research/maya1 | maya-research | null | 45,055 | 309,213 | False | 2025-10-18T01:45:08Z | 2025-11-12T00:53:50Z | transformers | 874 | 2 | null | text-to-speech | null | [
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"t... | 1acef7e9abb8212bf2991256d438d4f173e48992 | [
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] | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": "<|begin_of_text|>", "eos_token": "<|eot_id|>", "pad_token": "<custom_token_7>"}, "chat_template_jinja": "{{- bos_token }}\n{%- if custom_tools is defined %}\n {%- set tools = custom_tools %}\n{%- endif %}\n{%- if not to... | {
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68f4d9f1645feb80d4568236 | Qwen/Qwen3-VL-32B-Instruct | Qwen | null | 1,074,627 | 4,440,023 | False | 2025-10-19T12:30:41Z | 2025-10-21T18:26:34Z | transformers | 192 | 2 | null | image-text-to-text | {"parameters": {"BF16": 33357390064}, "total": 33357390064} | [
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] | null | {"architectures": ["Qwen3VLForConditionalGeneration"], "model_type": "qwen3_vl", "processor_config": {"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0].role == 'system' %}\n {%- if messages[0].content is string %}\n {{- messages[0].content }}\n {%- el... | {
"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": "transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "image-text-to-text", "tags": null} | null | null | [
"apache-2.0"
] | null | null | 33,357,390,064 | null | null | [
"AutoModelForImageTextToText",
"qwen3_vl",
"Qwen3VLForConditionalGeneration"
] | [
"image-text-to-text"
] | [
"multimodal"
] | [
"text",
"image"
] | [
"text"
] |
68f4dbcef80f98b8e573fab7 | Qwen/Qwen3-VL-32B-Thinking | Qwen | null | 113,961 | 764,877 | False | 2025-10-19T12:38:38Z | 2025-10-21T18:23:29Z | transformers | 88 | 2 | null | image-text-to-text | {"parameters": {"BF16": 33357390064}, "total": 33357390064} | [
".gitattributes",
"README.md",
"chat_template.json",
"config.json",
"generation_config.json",
"merges.txt",
"model-00001-of-00014.safetensors",
"model-00002-of-00014.safetensors",
"model-00003-of-00014.safetensors",
"model-00004-of-00014.safetensors",
"model-00005-of-00014.safetensors",
"model... | 7edd10ffd1196091948fb245ff63e406ccb2d4d1 | [
"transformers",
"safetensors",
"qwen3_vl",
"image-text-to-text",
"conversational",
"arxiv:2505.09388",
"arxiv:2502.13923",
"arxiv:2409.12191",
"arxiv:2308.12966",
"license:apache-2.0",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["Qwen3VLForConditionalGeneration"], "model_type": "qwen3_vl", "processor_config": {"chat_template": "{%- set image_count = namespace(value=0) %}\n{%- set video_count = namespace(value=0) %}\n{%- macro render_content(content, do_vision_count) %}\n {%- if content is string %}\n {{- content }}... | {
"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": "transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "image-text-to-text", "tags": null} | null | null | [
"apache-2.0"
] | null | null | 33,357,390,064 | null | null | [
"AutoModelForImageTextToText",
"qwen3_vl",
"Qwen3VLForConditionalGeneration"
] | [
"image-text-to-text"
] | [
"multimodal"
] | [
"text",
"image"
] | [
"text"
] |
68f7ccb92baab5efd84e5de8 | nvidia/NVIDIA-Nemotron-Nano-12B-v2-VL-BF16 | nvidia | null | 95,213 | 421,881 | False | 2025-10-21T18:11:05Z | 2025-12-02T18:50:47Z | transformers | 81 | 2 | null | image-text-to-text | {"parameters": {"F32": 6, "BF16": 13181860352}, "total": 13181860358} | [
".gitattributes",
"README.md",
"bias.md",
"chat_template.jinja",
"config.json",
"configuration.py",
"configuration_nemotron_h.py",
"configuration_radio.py",
"evs.py",
"explainability.md",
"generation_config.json",
"image_processing.py",
"images/demo.mp4",
"images/demo_frames/frame_0000.jpg... | 5d250e2e111dc5e1434131bdf3d590c27a878ade | [
"transformers",
"safetensors",
"nvidia",
"VLM",
"image-text-to-text",
"conversational",
"arxiv:2501.14818",
"arxiv:2511.03929",
"license:other",
"endpoints_compatible",
"region:us"
] | null | {"tokenizer_config": {"bos_token": "<s>", "chat_template": "{%- set ns = namespace(enable_thinking=false, has_sys_prompt=false, non_tool_system_content='', has_video=false, explicit_think_requested=false) -%}{%- set msg = namespace(content='') -%}{%- for message in messages -%}{%- if message['role'] == 'system' -%}{%- ... | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": null,
"processor": null
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": "transformers", "license": "other", "license_name": "nvidia-open-model-license", "license_link": "https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/", "metrics": null, "model_name": null, "... | null | null | [
"other",
"nvidia-open-model-license",
"https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/"
] | null | null | 13,181,860,358 | null | null | [
"AutoModel"
] | [
"image-text-to-text",
null
] | [
"multimodal"
] | [
"text",
"image"
] | [
"text"
] |
68f8dfe68cb208be9702aa87 | MiniMaxAI/MiniMax-M2 | MiniMaxAI | null | 116,505 | 1,869,243 | False | 2025-10-22T13:45:10Z | 2025-12-23T08:37:43Z | transformers | 1,488 | 2 | null | text-generation | {"parameters": {"F32": 62654720, "BF16": 1230021632, "F8_E4M3": 227410968576}, "total": 228703644928} | [
".gitattributes",
"README.md",
"chat_template.jinja",
"config.json",
"configuration_minimax_m2.py",
"docs/mlx_deploy_guide.md",
"docs/sglang_deploy_guide.md",
"docs/sglang_deploy_guide_cn.md",
"docs/tool_calling_guide.md",
"docs/tool_calling_guide_cn.md",
"docs/transformers_deploy_guide.md",
"... | 757303d492a50514c312788b5247a4f696a4c6a3 | [
"transformers",
"safetensors",
"minimax_m2",
"text-generation",
"conversational",
"custom_code",
"arxiv:2504.07164",
"arxiv:2509.06501",
"arxiv:2509.13160",
"license:other",
"eval-results",
"endpoints_compatible",
"fp8",
"deploy:azure",
"region:us"
] | null | {"architectures": ["MiniMaxM2ForCausalLM"], "auto_map": {"AutoConfig": "configuration_minimax_m2.MiniMaxM2Config", "AutoModelForCausalLM": "modeling_minimax_m2.MiniMaxM2ForCausalLM"}, "model_type": "minimax_m2", "quantization_config": {"quant_method": "fp8"}, "tokenizer_config": {"bos_token": "]~!b[", "eos_token": "[e~... | {
"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": "other", "license_name": "modified-mit", "license_link": "https://github.com/MiniMax-AI/MiniMax-M2/blob/main/LICENSE", "metrics": null, "model_name": null, "pipeline_tag": "text-generation", "tags":... | null | null | [
"other",
"modified-mit",
"https://github.com/MiniMax-AI/MiniMax-M2/blob/main/LICENSE"
] | null | null | 228,703,644,928 | null | null | [
"MiniMaxM2ForCausalLM",
"AutoModelForCausalLM",
"minimax_m2"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
621ffdc036468d709f174331 | 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"
] | [
"fill-mask"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
621ffdc036468d709f174341 | distilbert/distilbert-base-cased-distilled-squad | distilbert | null | 218,356 | 25,027,555 | False | 2022-03-02T23:29:04Z | 2024-05-06T13:46:31Z | transformers | 266 | 1 | [{"name": "distilbert-base-cased-distilled-squad", "results": [{"task": {"type": "question-answering", "name": "Question Answering"}, "dataset": {"name": "squad", "type": "squad", "config": "plain_text", "split": "validation"}, "metrics": [{"type": "exact_match", "value": 79.5998, "name": "Exact Match", "verified": tru... | question-answering | {"parameters": {"F32": 65192450}, "total": 65192450} | [
".gitattributes",
"README.md",
"config.json",
"model.safetensors",
"openvino_model.bin",
"openvino_model.xml",
"pytorch_model.bin",
"rust_model.ot",
"saved_model.tar.gz",
"tf_model.h5",
"tfjs.tar.gz",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | 564e9b582944a57a3e586bbb98fd6f0a4118db7f | [
"transformers",
"pytorch",
"tf",
"rust",
"safetensors",
"openvino",
"distilbert",
"question-answering",
"en",
"dataset:squad",
"arxiv:1910.01108",
"arxiv:1910.09700",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["DistilBertForQuestionAnswering"], "model_type": "distilbert", "tokenizer_config": {}} | {
"auto_model": "AutoModelForQuestionAnswering",
"custom_class": null,
"pipeline_tag": "question-answering",
"processor": "AutoTokenizer"
} | {"datasets": ["squad"], "language": "en", "license": "apache-2.0", "metrics": ["squad"], "model-index": [{"name": "distilbert-base-cased-distilled-squad", "results": [{"task": {"type": "question-answering", "name": "Question Answering"}, "dataset": {"name": "squad", "type": "squad", "config": "plain_text", "split": "va... | # DistilBERT base cased distilled SQuAD
## 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 ... | null | [
"apache-2.0"
] | [
"squad"
] | [
"en"
] | 65,192,450 | null | [
"squad"
] | [
"AutoModelForQuestionAnswering",
"distilbert",
"DistilBertForQuestionAnswering"
] | [
"question-answering"
] | [
"text"
] | [
"text"
] | [
"text"
] |
621ffdc036468d709f174348 | 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} | [
".gitattributes",
"64.tflite",
"README.md",
"config.json",
"coreml/text-generation/float32_model.mlpackage/Data/com.apple.CoreML/model.mlmodel",
"coreml/text-generation/float32_model.mlpackage/Data/com.apple.CoreML/weights/weight.bin",
"coreml/text-generation/float32_model.mlpackage/Manifest.json",
"c... | 2290a62682d06624634c1f46a6ad5be0f47f38aa | [
"transformers",
"pytorch",
"tf",
"jax",
"tflite",
"rust",
"coreml",
"safetensors",
"gpt2",
"text-generation",
"exbert",
"en",
"dataset:openwebtext",
"arxiv:1910.01108",
"arxiv:2201.08542",
"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 | [
"apache-2.0"
] | [
"openwebtext"
] | [
"en"
] | 88,204,032 | null | null | [
"GPT2LMHeadModel",
"AutoModelForCausalLM",
"gpt2"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
621ffdc036468d709f174350 | 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"
] |
621ffdc036468d709f174353 | 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"
] |
621ffdc036468d709f174369 | 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} | [
".gitattributes",
"README.md",
"config.json",
"flax_model.msgpack",
"model.safetensors",
"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",
... | c23d21b0620b635a76227c604d44e43a9f0ee389 | [
"transformers",
"pytorch",
"tf",
"jax",
"onnx",
"safetensors",
"xlm-roberta",
"fill-mask",
"exbert",
"multilingual",
"af",
"am",
"ar",
"as",
"az",
"be",
"bg",
"bn",
"br",
"bs",
"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 (large-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",
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"gl",
"gu",
"ha",
"he",
"hi",
"hr",
"hu",
"hy",
"id",
"is",
"i... | 561,192,082 | null | null | [
"AutoModelForMaskedLM",
"xlm-roberta",
"XLMRobertaForMaskedLM"
] | [
"fill-mask"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
621ffdc036468d709f1743ec | AI-Growth-Lab/PatentSBERTa | AI-Growth-Lab | null | 15,121 | 694,621 | False | 2022-03-02T23:29:04Z | 2023-02-16T18:25:30Z | sentence-transformers | 54 | 1 | null | sentence-similarity | null | [
".gitattributes",
"1_Pooling/config.json",
"README.md",
"config.json",
"config_sentence_transformers.json",
"modules.json",
"pytorch_model.bin",
"sentence_bert_config.json",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | 3ff1d553c861d8f5bfd902333d97fc95eb6b4c8f | [
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:2103.11933",
"text-embeddings-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["MPNetModel"], "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": "AutoModel",
"custom_class": null,
"pipeline_tag": "feature-extraction",
"processor": "AutoTokenizer"
} | {"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": "sentence-similarity", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "tr... | # PatentSBERTa
## PatentSBERTa: A Deep NLP based Hybrid Model for Patent Distance and Classification using Augmented SBERT
### Aalborg University Business School, AI: Growth-Lab
https://arxiv.org/abs/2103.11933
https://github.com/AI-Growth-Lab/PatentSBERTa
This is a [sentence-transformers](https://www.SBERT.net... | null | null | null | null | null | null | null | [
"MPNetModel",
"AutoModel",
"mpnet"
] | [
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
621ffdc036468d709f174c63 | Babelscape/rebel-large | Babelscape | null | 253,188 | 11,649,328 | False | 2022-03-02T23:29:04Z | 2023-06-20T10:17:00Z | transformers | 235 | 1 | [{"name": "REBEL", "results": [{"task": {"name": "Relation Extraction", "type": "Relation-Extraction"}, "dataset": {"name": "CoNLL04", "type": "CoNLL04"}, "metrics": [{"name": "RE+ Macro F1", "type": "re+ macro f1", "value": 76.65, "verified": false}]}, {"task": {"name": "Relation Extraction", "type": "Relation-Extract... | null | {"parameters": {"F32": 406348896}, "total": 406348896} | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"merges.txt",
"model.safetensors",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | 44eb6cb4585df284ce6c4d6a7013f83fe473c052 | [
"transformers",
"pytorch",
"safetensors",
"bart",
"text2text-generation",
"seq2seq",
"relation-extraction",
"en",
"dataset:Babelscape/rebel-dataset",
"license:cc-by-nc-sa-4.0",
"model-index",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["BartForConditionalGeneration"], "model_type": "bart", "tokenizer_config": {"unk_token": {"content": "<unk>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "bos_token": {"content": "<s>", "single_word": false, "lstrip": false, "rstrip": false, "n... | {
"auto_model": "AutoModelForSeq2SeqLM",
"custom_class": null,
"pipeline_tag": "text2text-generation",
"processor": "AutoTokenizer"
} | {"datasets": ["Babelscape/rebel-dataset"], "language": ["en"], "license": "cc-by-nc-sa-4.0", "tags": ["seq2seq", "relation-extraction"], "widget": [{"text": "Punta Cana is a resort town in the municipality of Higuey, in La Altagracia Province, the eastern most province of the Dominican Republic"}], "model-index": [{"na... | [](https://paperswithcode.com/sota/relation-extraction-on-nyt?p=rebel-relation-extraction-by-end-to-end)
[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithco... | null | [
"cc-by-nc-sa-4.0"
] | [
"Babelscape/rebel-dataset"
] | [
"en"
] | 406,348,896 | null | null | [
"bart",
"BartForConditionalGeneration",
"AutoModelForSeq2SeqLM"
] | [
"text2text-generation"
] | null | null | null |
621ffdc036468d709f1753ba | EleutherAI/gpt-j-6b | EleutherAI | null | 114,485 | 7,527,948 | False | 2022-03-02T23:29:04Z | 2023-06-21T14:33:36Z | transformers | 1,523 | 1 | null | text-generation | null | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"flax_model.msgpack",
"merges.txt",
"pytorch_model.bin",
"special_tokens_map.json",
"tf_model.h5",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | 47e169305d2e8376be1d31e765533382721b2cc1 | [
"transformers",
"pytorch",
"tf",
"jax",
"gptj",
"text-generation",
"causal-lm",
"en",
"dataset:EleutherAI/pile",
"arxiv:2104.09864",
"arxiv:2101.00027",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["GPTJForCausalLM"], "model_type": "gptj", "tokenizer_config": {"unk_token": {"content": "<|endoftext|>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "bos_token": {"content": "<|endoftext|>", "single_word": false, "lstrip": false, "rstrip": fals... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["EleutherAI/pile"], "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": ["pytorch", "causal-lm"]} | # GPT-J 6B
## Model Description
GPT-J 6B is a transformer model trained using Ben Wang's [Mesh Transformer JAX](https://github.com/kingoflolz/mesh-transformer-jax/). "GPT-J" refers to the class of model, while "6B" represents the number of trainable parameters.
<figure>
| Hyperparameter | Value |
|------... | null | [
"apache-2.0"
] | [
"EleutherAI/pile"
] | [
"en"
] | null | null | null | [
"gptj",
"AutoModelForCausalLM",
"GPTJForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
621ffdc036468d709f176b7f | Musixmatch/umberto-commoncrawl-cased-v1 | Musixmatch | null | 11,803 | 474,004 | False | 2022-03-02T23:29:04Z | 2021-02-12T11:31:59Z | transformers | 18 | 1 | null | fill-mask | null | [
".gitattributes",
"README.md",
"config.json",
"pytorch_model.bin",
"sentencepiece.bpe.model",
"tokenizer.json"
] | fe7b14808cccbbe2984b05e6fbfd71127f11008f | [
"transformers",
"pytorch",
"camembert",
"fill-mask",
"it",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["CamembertForMaskedLM"], "model_type": "camembert"} | {
"auto_model": "AutoModelForMaskedLM",
"custom_class": null,
"pipeline_tag": "fill-mask",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": "it", "library_name": null, "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | # UmBERTo Commoncrawl Cased
[UmBERTo](https://github.com/musixmatchresearch/umberto) is a Roberta-based Language Model trained on large Italian Corpora and uses two innovative approaches: SentencePiece and Whole Word Masking. Now available at [github.com/huggingface/transformers](https://huggingface.co/Musixmatch/umbe... | null | null | null | [
"it"
] | null | null | null | [
"CamembertForMaskedLM",
"AutoModelForMaskedLM",
"camembert"
] | [
"fill-mask"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
621ffdc136468d709f178e76 | 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 |
621ffdc136468d709f17a20c | cross-encoder/ms-marco-MiniLM-L2-v2 | cross-encoder | {
"models": [
{
"_id": "621ffdc136468d709f17a20b",
"id": "cross-encoder/ms-marco-MiniLM-L12-v2"
}
],
"relation": "quantized"
} | 1,008,759 | 14,722,282 | False | 2022-03-02T23:29:05Z | 2025-08-29T14:36:35Z | sentence-transformers | 14 | 1 | null | text-ranking | {"parameters": {"I64": 512, "F32": 15615745}, "total": 15616257} | [
".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... | 1b5cd67b15209f24824c50370e0397743aa9b787 | [
"sentence-transformers",
"pytorch",
"jax",
"onnx",
"safetensors",
"openvino",
"bert",
"text-classification",
"transformers",
"text-ranking",
"en",
"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"
] | 15,616,257 | null | null | [
"BertForSequenceClassification",
"bert",
"AutoModelForSequenceClassification"
] | [
"text-ranking",
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
621ffdc136468d709f17a20e | 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} | [
".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... | c5ee24cb16019beea0893ab7796b1df96625c6b8 | [
"sentence-transformers",
"pytorch",
"jax",
"onnx",
"safetensors",
"openvino",
"bert",
"text-classification",
"transformers",
"text-ranking",
"en",
"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"
] | [
"text-ranking",
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
621ffdc136468d709f17a330 | dandelin/vilt-b32-mlm | dandelin | null | 14,705 | 14,062,745 | False | 2022-03-02T23:29:05Z | 2022-07-06T12:18:37Z | transformers | 13 | 1 | null | fill-mask | null | [
".gitattributes",
"README.md",
"config.json",
"preprocessor_config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | 9507e9c3da12076e10f272e942569dc5190edc1c | [
"transformers",
"pytorch",
"vilt",
"fill-mask",
"arxiv:2102.03334",
"license:apache-2.0",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["ViltForMaskedLM"], "model_type": "vilt", "tokenizer_config": {"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}} | {
"auto_model": "AutoModelForMaskedLM",
"custom_class": null,
"pipeline_tag": "fill-mask",
"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": null} | # Vision-and-Language Transformer (ViLT), pre-trained only
Vision-and-Language Transformer (ViLT) model pre-trained on GCC+SBU+COCO+VG (200k steps). It was introduced in the paper [ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision](https://arxiv.org/abs/2102.03334) by Kim et al. and first... | null | [
"apache-2.0"
] | null | null | null | null | null | [
"AutoModelForMaskedLM",
"vilt",
"ViltForMaskedLM"
] | [
"fill-mask"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
621ffdc136468d709f17adbf | facebook/contriever-msmarco | facebook | null | 22,491 | 9,280,033 | False | 2022-03-02T23:29:05Z | 2022-06-25T17:19:59Z | transformers | 33 | 1 | null | feature-extraction | null | [
".gitattributes",
"README.md",
"config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | abe8c1493371369031bcb1e02acb754cf4e162fa | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"arxiv:2112.09118",
"text-embeddings-inference",
"endpoints_compatible",
"deploy:azure",
"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"
} | {"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": "feature-extraction", "tags": ["feature-extraction"]} | This model is the finetuned version of the pre-trained contriever model available here https://huggingface.co/facebook/contriever, following the approach described in [Towards Unsupervised Dense Information Retrieval with Contrastive Learning](https://arxiv.org/abs/2112.09118). The associated GitHub repository is avail... | null | null | null | null | null | null | null | [
"Contriever",
"bert"
] | [
null,
"feature-extraction"
] | [
"multimodal"
] | [
"text"
] | [
"embeddings"
] |
621ffdc136468d709f17b61d | 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",
"pytorch",
"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 |
621ffdc136468d709f17bab4 | hfl/chinese-macbert-large | hfl | null | 4,943 | 518,081 | False | 2022-03-02T23:29:05Z | 2021-05-19T19:14:18Z | transformers | 52 | 1 | null | fill-mask | null | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"flax_model.msgpack",
"pytorch_model.bin",
"special_tokens_map.json",
"tf_model.h5",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | 1cf2677c782975600ce58e2961656b1b29eddbae | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"zh",
"arxiv:2004.13922",
"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": ["bert"]} | <p align="center">
<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",
"bert",
"BertForMaskedLM"
] | [
"fill-mask"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
621ffdc136468d709f17d289 | klue/bert-base | klue | null | 43,856 | 11,227,878 | False | 2022-03-02T23:29:05Z | 2023-06-12T12:30:04Z | transformers | 62 | 1 | null | fill-mask | {"parameters": {"I64": 512, "F32": 111243010}, "total": 111243522} | [
".gitattributes",
"README.md",
"config.json",
"model.safetensors",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | 77c8b3d707df785034b4e50f2da5d37be5f0f546 | [
"transformers",
"pytorch",
"safetensors",
"bert",
"fill-mask",
"korean",
"klue",
"ko",
"arxiv:2105.09680",
"arxiv:1910.09700",
"license:cc-by-sa-4.0",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["BertForMaskedLM"], "model_type": "bert", "tokenizer_config": {"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}} | {
"auto_model": "AutoModelForMaskedLM",
"custom_class": null,
"pipeline_tag": "fill-mask",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": "ko", "library_name": null, "license": "cc-by-sa-4.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["korean", "klue"], "mask_token": "[MASK]", "widget": [{"text": "\ub300\ud55c\ubb... | # KLUE BERT base
## 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-... | null | [
"cc-by-sa-4.0"
] | null | [
"ko"
] | 111,243,522 | null | null | [
"AutoModelForMaskedLM",
"bert",
"BertForMaskedLM"
] | [
"fill-mask"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
621ffdc136468d709f17e2c1 | 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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"config.json",
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"runs/Sep16_18-26-05_ed005835f859/events.out.tfevents.1631816775.ed005835f85... | ae0eab9ad336d7d548e0efe394b07c04bcaf6e91 | [
"transformers",
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"tensorboard",
"safetensors",
"roberta",
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"generated_from_trainer",
"financial",
"stocks",
"sentiment",
"dataset:financial_phrasebank",
"license:apache-2.0",
"model-index",
"text-embeddings-inference",
"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",
"processor": "AutoTokenizer"
} | {"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"
] | null | 82,121,221 | null | [
"accuracy"
] | [
"roberta",
"AutoModelForSequenceClassification",
"RobertaForSequenceClassification"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
621ffdc136468d709f1802ed | 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 | [
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"feature-extraction",
"sentence-similarity",
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"ar",
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"ca",
"cs",
"da",
"de",
"el",
"en",
"es",
"et",
"fa",
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... | 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",
"es",
"et",
"fa",
"fi",
"fr",
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"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"
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621ffdc136468d709f180ed9 | thunlp/Lawformer | thunlp | null | 432 | 46,385 | False | 2022-03-02T23:29:05Z | 2022-07-12T06:23:13Z | transformers | 23 | 1 | null | fill-mask | null | [
".gitattributes",
"README.md",
"config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | d2452823634a0c5aff74b894c8b86f5ed346b964 | [
"transformers",
"pytorch",
"longformer",
"fill-mask",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["LongformerForMaskedLM"], "model_type": "longformer", "tokenizer_config": {"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}} | {
"auto_model": "AutoModelForMaskedLM",
"custom_class": null,
"pipeline_tag": "fill-mask",
"processor": "AutoTokenizer"
} | null | ## Lawformer
### Introduction
This repository provides the source code and checkpoints of the paper "Lawformer: A Pre-trained Language Model forChinese Legal Long Documents". You can download the checkpoint from the [huggingface model hub](https://huggingface.co/xcjthu/Lawformer) or from [here](https://data.thunlp.org... | null | null | null | null | null | null | null | [
"longformer",
"LongformerForMaskedLM",
"AutoModelForMaskedLM"
] | [
"fill-mask"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
62489157d43f923e55535854 | UrukHan/t5-russian-summarization | UrukHan | null | 1,916 | 193,440 | False | 2022-04-02T18:09:27Z | 2023-04-05T10:11:59Z | transformers | 45 | 1 | [{"name": "t5-russian-summarization", "results": []}] | null | {"parameters": {"F32": 222903552}, "total": 222903552} | [
".gitattributes",
".gitignore",
"README.md",
"config.json",
"model.safetensors",
"pytorch_model.bin",
"runs/Apr02_18-10-03_3e52398588a8/1648923049.4639742/events.out.tfevents.1648923049.3e52398588a8.2241.1",
"runs/Apr02_18-10-03_3e52398588a8/1648923853.5563185/events.out.tfevents.1648923853.3e52398588... | aa8ca340a0695ad751e719c6e9b7f4d4f1c65c2b | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:UrukHan/wav2vec2-russian",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 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": "UrukHan/wav2vec2-russian", "eval_results": [], "language": null, "library_name": null, "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": "t5-russian-summarization", "pipeline_tag": null, "tags": ["generated_from_trainer"], "widget": [{"text": "... | <!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
---
# t5-russian-summarization
---
модель для исправление текста из распознаного аудио. моя модлеь для распознования аудио https://... | null | null | [
"UrukHan/wav2vec2-russian"
] | null | 222,903,552 | null | null | [
"t5",
"T5ForConditionalGeneration",
"AutoModelForSeq2SeqLM"
] | [
"text2text-generation"
] | null | null | null |
624d55bab58d1313f8b775ff | MilaNLProc/xlm-emo-t | MilaNLProc | null | 2,310 | 928,050 | False | 2022-04-06T08:56:26Z | 2023-03-27T17:52:36Z | transformers | 11 | 1 | null | text-classification | null | [
".gitattributes",
"README.md",
"config.json",
"pytorch_model.bin",
"sentencepiece.bpe.model",
"tokenizer.json",
"training_args.bin"
] | a6ee7c9fad08d60204e7ae437d41d392381496f0 | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"emotion",
"emotion-analysis",
"multilingual",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["XLMRobertaForSequenceClassification"], "model_type": "xlm-roberta"} | {
"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": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["emotion", "emotion-analysis", "multilingual"], "widget": [{"text": "Guarda! ci ... | #
[Federico Bianchi](https://federicobianchi.io/) •
[Debora Nozza](http://dnozza.github.io/) •
[Dirk Hovy](http://www.dirkhovy.com/)
## Abstract
Detecting emotion in text allows social and computational scientists to study how people behave and react to online events. However, developing these tools for different lan... | null | null | null | [
"multilingual"
] | null | null | null | [
"AutoModelForSequenceClassification",
"XLMRobertaForSequenceClassification",
"xlm-roberta"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
62561e7fb26568008af13222 | Salesforce/codegen-6B-mono | Salesforce | null | 994 | 86,370 | False | 2022-04-13T00:51:11Z | 2025-01-31T21:27:06Z | transformers | 40 | 1 | null | text-generation | null | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"merges.txt",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | 62dfb58dbc7b5f04a3bc9b3ce0786fc82f1871b8 | [
"transformers",
"pytorch",
"codegen",
"text-generation",
"arxiv:2203.13474",
"license:bsd-3-clause",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["CodeGenForCausalLM"], "model_type": "codegen", "tokenizer_config": {"unk_token": "<|endoftext|>", "bos_token": "<|endoftext|>", "eos_token": "<|endoftext|>"}} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"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": null, "tags": null} | # CodeGen (CodeGen-Mono 6B)
## Model description
CodeGen is a family of autoregressive language models for **program synthesis** from the paper: [A Conversational Paradigm for Program Synthesis](https://arxiv.org/abs/2203.13474) by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savare... | null | [
"bsd-3-clause"
] | null | null | null | null | null | [
"AutoModelForCausalLM",
"codegen",
"CodeGenForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
6262c267cbebf7e1ac29e364 | 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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"tokenizer.json",
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"vocab.json"
] | ce19dc912ca5cd21c8a653c79e251e808ccabcd1 | [
"transformers",
"pytorch",
"tf",
"clip",
"zero-shot-image-classification",
"generated_from_keras_callback",
"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": [], "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"
] | [
"text",
"image"
] | [
"logits"
] |
627baf3b3974b0ed6b29cde1 | AnnaWegmann/Style-Embedding | AnnaWegmann | {
"models": [
{
"_id": "621ffdc036468d709f174350",
"id": "FacebookAI/roberta-base"
}
],
"relation": "finetune"
} | 8,500 | 1,750,191 | False | 2022-05-11T12:42:35Z | 2025-10-23T08:53:05Z | sentence-transformers | 23 | 1 | null | sentence-similarity | null | [
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"pytorch_model.bin",
"sentence_bert_config.json",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.js... | d7d0f5ca829316a8f5695e49dfce80b86db5e76c | [
"sentence-transformers",
"pytorch",
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"dataset:AnnaWegmann/StyleEmbeddingData",
"arxiv:2204.04907",
"base_model:FacebookAI/roberta-base",
"base_model:finetune:FacebookAI/roberta-base",
"text-embeddings-inference",
"endpoints... | null | {"architectures": ["RobertaModel"], "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": "AutoModel",
"custom_class": null,
"pipeline_tag": "feature-extraction",
"processor": "AutoTokenizer"
} | {"base_model": ["FacebookAI/roberta-base"], "datasets": ["AnnaWegmann/StyleEmbeddingData"], "eval_results": null, "language": null, "library_name": null, "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "sentence-similarity", "tags": ["sentence-transforme... | # Style Embedding
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.
for more info see [Style-Embeddings](https://github.com/nlpsoc/Style-Embeddings)
see published paper a... | null | null | [
"AnnaWegmann/StyleEmbeddingData"
] | null | null | null | null | [
"roberta",
"AutoModel",
"RobertaModel"
] | [
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
62862fbd504d37700308a82e | bigscience/bloom | bigscience | null | 7,485 | 4,829,937 | False | 2022-05-19T11:53:33Z | 2023-07-28T17:50:20Z | transformers | 4,988 | 1 | [{"name": "bloom", "results": [{"task": {"type": "text-generation"}, "dataset": {"type": "openai_humaneval", "name": "humaneval"}, "metrics": [{"name": "pass@1", "type": "pass@1", "value": 0.15542682926829265, "verified": false}, {"name": "pass@10", "type": "pass@10", "value": 0.3278356276947017, "verified": false}, {"... | text-generation | {"parameters": {"BF16": 176247271424}, "total": 176247271424} | [
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"model_00004-of-00072.safetensors",
"model_00005-of-00072.safetensors",
"model_00006-of-00072.safetensors",
"m... | 053d9cd9fbe814e091294f67fcfedb3397b954bb | [
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"ne",
"nso",
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... | null | {"architectures": ["BloomForCausalLM"], "model_type": "bloom", "tokenizer_config": {"unk_token": "<unk>", "eos_token": "</s>", "bos_token": "<s>", "pad_token": "<pad>"}} | {
"auto_model": "AutoModelForCausalLM",
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"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"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", "zu"], "license": "bigscience-b... | <img src="https://cdn-uploads.huggingface.co/production/uploads/1657124309515-5f17f0a0925b9863e28ad517.png" alt="BigScience Logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
BigScience Large Open-science Open-access Multilingual Language Model
Version 1.3 / 6 July 2022
Current Check... | null | [
"bigscience-bloom-rail-1.0"
] | null | [
"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",
... | 176,247,271,424 | null | null | [
"AutoModelForCausalLM",
"BloomForCausalLM",
"bloom"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
628fe15f3da3545d1463b208 | witiko/mathberta | witiko | null | 193 | 13,598 | False | 2022-05-26T20:21:51Z | 2022-08-12T20:32:04Z | transformers | 19 | 1 | null | fill-mask | null | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"learning-curves.png",
"merges.txt",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | 4cb18380847a27c6d0d1d3db3459a78cd1b602cd | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"en",
"dataset:arxmliv",
"dataset:math-stackexchange",
"license:mit",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["RobertaForMaskedLM"], "model_type": "roberta", "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": ["arxmliv", "math-stackexchange"], "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} | # MathBERTa model
Pretrained model on English language and LaTeX using a masked language modeling
(MLM) objective. It was introduced in [this paper][1] and first released in
[this repository][2]. This model is case-sensitive: it makes a difference
between english and English.
[1]: http://ceur-ws.org/Vol-3180/paper-0... | null | [
"mit"
] | [
"arxmliv",
"math-stackexchange"
] | [
"en"
] | null | null | null | [
"roberta",
"AutoModelForMaskedLM",
"RobertaForMaskedLM"
] | [
"fill-mask"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
629e473a46b4826be2c81e93 | MoritzLaurer/DeBERTa-v3-large-mnli-fever-anli-ling-wanli | MoritzLaurer | null | 41,950 | 4,396,211 | False | 2022-06-06T18:28:10Z | 2024-04-11T13:49:10Z | transformers | 120 | 1 | [{"name": "DeBERTa-v3-large-mnli-fever-anli-ling-wanli", "results": [{"task": {"type": "text-classification", "name": "Natural Language Inference"}, "dataset": {"name": "MultiNLI-matched", "type": "multi_nli", "split": "validation_matched"}, "metrics": [{"type": "accuracy", "value": "0,912", "verified": false}]}, {"tas... | zero-shot-classification | {"parameters": {"I64": 512, "F16": 435064835}, "total": 435065347} | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"model.safetensors",
"onnx/model.onnx",
"onnx/model_quantized.onnx",
"pytorch_model.bin",
"special_tokens_map.json",
"spm.model",
"tokenizer.json",
"tokenizer_config.json"
] | b3546ea6b0346eb6f8d5d68b13c7dc6d0376b3d7 | [
"transformers",
"pytorch",
"onnx",
"safetensors",
"deberta-v2",
"text-classification",
"zero-shot-classification",
"en",
"dataset:multi_nli",
"dataset:facebook/anli",
"dataset:fever",
"dataset:lingnli",
"dataset:alisawuffles/WANLI",
"arxiv:2104.07179",
"arxiv:2111.09543",
"license:mit"... | null | {"architectures": ["DebertaV2ForSequenceClassification"], "model_type": "deberta-v2", "tokenizer_config": {"bos_token": "[CLS]", "eos_token": "[SEP]", "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"
} | {"datasets": ["multi_nli", "facebook/anli", "fever", "lingnli", "alisawuffles/WANLI"], "language": ["en"], "license": "mit", "metrics": ["accuracy"], "pipeline_tag": "zero-shot-classification", "tags": ["text-classification", "zero-shot-classification"], "model-index": [{"name": "DeBERTa-v3-large-mnli-fever-anli-ling-w... | # DeBERTa-v3-large-mnli-fever-anli-ling-wanli
## Model description
This model was fine-tuned on the [MultiNLI](https://huggingface.co/datasets/multi_nli), [Fever-NLI](https://github.com/easonnie/combine-FEVER-NSMN/blob/master/other_resources/nli_fever.md), Adversarial-NLI ([ANLI](https://huggingface.co/datasets/anli)),... | null | [
"mit"
] | [
"multi_nli",
"facebook/anli",
"fever",
"lingnli",
"alisawuffles/WANLI"
] | [
"en"
] | 435,065,347 | null | [
"accuracy"
] | [
"AutoModelForSequenceClassification",
"deberta-v2",
"DebertaV2ForSequenceClassification"
] | [
"zero-shot-classification",
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
62ab5bd2e3cc78f1b1767485 | ml6team/keyphrase-extraction-kbir-kptimes | ml6team | null | 55 | 2,723 | False | 2022-06-16T16:35:30Z | 2023-05-06T08:48:47Z | transformers | 2 | 1 | [{"name": "ml6team/keyphrase-extraction-distilbert-kptimes", "results": [{"task": {"type": "keyphrase-extraction", "name": "Keyphrase Extraction"}, "dataset": {"type": "midas/kptimes", "name": "kptimes"}, "metrics": [{"type": "F1 (Seqeval)", "value": 0, "name": "F1 (Seqeval)", "verified": false}, {"type": "F1@M", "valu... | token-classification | null | [
".gitattributes",
"README.md",
"config.json",
"merges.txt",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"training_args.bin",
"vocab.json"
] | 8df6c5d59caf3383e9b0e5c7629d4b48f183e67a | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"keyphrase-extraction",
"en",
"dataset:midas/kptimes",
"arxiv:2112.08547",
"arxiv:1911.12559",
"license:mit",
"model-index",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["RobertaForTokenClassification"], "model_type": "roberta", "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,... | {
"auto_model": "AutoModelForTokenClassification",
"custom_class": null,
"pipeline_tag": "token-classification",
"processor": "AutoTokenizer"
} | {"datasets": ["midas/kptimes"], "language": "en", "license": "mit", "metrics": ["seqeval"], "tags": ["keyphrase-extraction"], "widget": [{"text": "Keyphrase extraction is a technique in text analysis where you extract the important keyphrases from a document. Thanks to these keyphrases humans can understand the conten... | # 🔑 Keyphrase Extraction Model: KBIR-KPTimes
Keyphrase extraction is a technique in text analysis where you extract the important keyphrases from a document. Thanks to these keyphrases humans can understand the content of a text very quickly and easily without reading it completely. Keyphrase extraction was first done... | null | [
"mit"
] | [
"midas/kptimes"
] | [
"en"
] | null | null | [
"seqeval"
] | [
"roberta",
"AutoModelForTokenClassification",
"RobertaForTokenClassification"
] | [
"token-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
62afdb861f7044b25f1b629a | ElKulako/cryptobert | ElKulako | null | 160,342 | 3,766,389 | False | 2022-06-20T02:29:26Z | 2025-05-26T11:52:00Z | transformers | 186 | 1 | null | text-classification | {"parameters": {"I64": 514, "F32": 124647939}, "total": 124648453} | [
".gitattributes",
"README.md",
"config.json",
"merges.txt",
"model.safetensors",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | 9e37c910fe87727cb842a9ac55c6388256fe0f15 | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"text-classification",
"cryptocurrency",
"crypto",
"BERT",
"sentiment classification",
"NLP",
"bitcoin",
"ethereum",
"shib",
"social media",
"sentiment analysis",
"cryptocurrency sentiment analysis",
"en",
"dataset:ElKulako/stock... | null | {"architectures": ["RobertaForSequenceClassification"], "model_type": "roberta", "tokenizer_config": {"bos_token": {"__type": "AddedToken", "content": "<s>", "lstrip": false, "normalized": true, "rstrip": false, "single_word": false}, "cls_token": {"__type": "AddedToken", "content": "<s>", "lstrip": false, "normalized"... | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["ElKulako/stocktwits-crypto"], "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": ["cryptocurrency", "crypto", "BERT", "sentiment classification... | For academic reference, cite the following paper: https://ieeexplore.ieee.org/document/10223689
# CryptoBERT
CryptoBERT is a pre-trained NLP model to analyse the language and sentiments of cryptocurrency-related social media posts and messages. It was built by further training the [vinai's bertweet-base](https://huggi... | null | [
"mit"
] | [
"ElKulako/stocktwits-crypto"
] | [
"en"
] | 124,648,453 | null | null | [
"roberta",
"AutoModelForSequenceClassification",
"RobertaForSequenceClassification"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
62bd5692bb71f7f0d8f81b7a | projecte-aina/roberta-base-ca-v2-cased-ner | projecte-aina | null | 39,097 | 91,130 | False | 2022-06-30T07:53:54Z | 2024-04-11T07:07:40Z | transformers | 2 | 1 | [{"name": "roberta-base-ca-v2-cased-ner", "results": [{"task": {"type": "token-classification"}, "dataset": {"type": "projecte-aina/ancora-ca-ner", "name": "Ancora-ca-NER"}, "metrics": [{"name": "F1", "type": "f1", "value": 0.8929, "verified": false}]}]}] | token-classification | null | [
".gitattributes",
"README.md",
"config.json",
"merges.txt",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | 4b68591b8bee05ce36222d76e000ad6c5edd730a | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"catalan",
"named entity recognition",
"ner",
"CaText",
"Catalan Textual Corpus",
"ca",
"dataset:projecte-aina/ancora-ca-ner",
"arxiv:1907.11692",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"deploy:azure",
... | null | {"architectures": ["RobertaForTokenClassification"], "model_type": "roberta", "tokenizer_config": {"unk_token": {"content": "<unk>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "bos_token": {"content": "<s>", "single_word": false, "lstrip": false, "rstrip": false... | {
"auto_model": "AutoModelForTokenClassification",
"custom_class": null,
"pipeline_tag": "token-classification",
"processor": "AutoTokenizer"
} | {"datasets": ["projecte-aina/ancora-ca-ner"], "language": ["ca"], "license": "apache-2.0", "metrics": ["f1"], "tags": ["catalan", "named entity recognition", "ner", "CaText", "Catalan Textual Corpus"], "widget": [{"text": "Em dic Llu\u00efsa i visc a Santa Maria del Cam\u00ed."}, {"text": "L'Aina, la Berta i la Norma s... | # Catalan BERTa-v2 (roberta-base-ca-v2) finetuned for Named Entity Recognition.
## Table of Contents
<details>
<summary>Click to expand</summary>
- [Model description](#model-description)
- [Intended uses and limitations](#intended-use)
- [How to use](#how-to-use)
- [Limitations and bias](#limitations-and-bias)
- [Tr... | null | [
"apache-2.0"
] | [
"projecte-aina/ancora-ca-ner"
] | [
"ca"
] | null | null | [
"f1"
] | [
"roberta",
"AutoModelForTokenClassification",
"RobertaForTokenClassification"
] | [
"token-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
62d2ec9b26213de379a359ee | dlicari/Italian-Legal-BERT | dlicari | null | 1,369 | 185,209 | False | 2022-07-16T16:51:39Z | 2023-08-28T17:54:43Z | transformers | 45 | 1 | null | fill-mask | {"parameters": {"I64": 512, "F32": 110729318}, "total": 110729830} | [
".gitattributes",
"ITALIAN_LEGAL_BERT.jpg",
"README.md",
"abbreviazioni.csv",
"config.json",
"model.safetensors",
"ner_it_legalbert.cfg",
"pytorch_model.bin",
"semantic_text_similarity.jpg",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"training_args.bin",
"vocab.t... | 1dfd1e43de31ecdbb5c075d0bac6c89ca951c486 | [
"transformers",
"pytorch",
"safetensors",
"bert",
"fill-mask",
"it",
"license:afl-3.0",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["BertForMaskedLM"], "model_type": "bert", "tokenizer_config": {"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}} | {
"auto_model": "AutoModelForMaskedLM",
"custom_class": null,
"pipeline_tag": "fill-mask",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": "it", "library_name": null, "license": "afl-3.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null, "widget": [{"text": "Il [MASK] ha chiesto revocarsi l'obbligo di pagamento"}]} | <img src="https://huggingface.co/dlicari/Italian-Legal-BERT/resolve/main/ITALIAN_LEGAL_BERT.jpg" width="600"/>
<h1> ITALIAN-LEGAL-BERT:A pre-trained Transformer Language Model for Italian Law </h1>
ITALIAN-LEGAL-BERT is based on <a href="https://huggingface.co/dbmdz/bert-base-italian-xxl-cased">bert-base-italian-xxl... | null | [
"afl-3.0"
] | null | [
"it"
] | 110,729,830 | null | null | [
"AutoModelForMaskedLM",
"bert",
"BertForMaskedLM"
] | [
"fill-mask"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
62d6b87ef00ba76e7a691e9e | naver-clova-ix/donut-base-finetuned-docvqa | naver-clova-ix | null | 18,199 | 2,179,208 | False | 2022-07-19T13:58:22Z | 2024-03-09T13:01:37Z | transformers | 273 | 1 | null | document-question-answering | null | [
".gitattributes",
".gitignore",
"README.md",
"added_tokens.json",
"config.json",
"preprocessor_config.json",
"pytorch_model.bin",
"sentencepiece.bpe.model",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json"
] | b19d2e332684b0e2d35d9144ce34047767335cf8 | [
"transformers",
"pytorch",
"vision-encoder-decoder",
"image-text-to-text",
"donut",
"image-to-text",
"vision",
"document-question-answering",
"arxiv:2111.15664",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["VisionEncoderDecoderModel"], "model_type": "vision-encoder-decoder", "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": ... | {
"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": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "document-question-answering", "tags": ["donut", "image-to-text", "vision"], "widget": [{"text": "What i... | # Donut (base-sized model, fine-tuned on DocVQA)
Donut model fine-tuned on DocVQA. It was introduced in the paper [OCR-free Document Understanding Transformer](https://arxiv.org/abs/2111.15664) by Geewok et al. and first released in [this repository](https://github.com/clovaai/donut).
Disclaimer: The team releasing ... | null | [
"mit"
] | null | null | null | null | null | [
"AutoModelForImageTextToText",
"vision-encoder-decoder",
"VisionEncoderDecoderModel"
] | [
"image-text-to-text",
"document-question-answering",
"image-to-text"
] | [
"multimodal"
] | [
"text",
"image"
] | [
"text"
] |
62d71ccd4fe7c96c5ff47de0 | praeclarum/cuneiform | praeclarum | null | 233 | 13,136 | False | 2022-07-19T21:06:21Z | 2023-06-08T13:31:35Z | transformers | 21 | 1 | null | null | null | [
".gitattributes",
"README.md",
"config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"training_args.bin"
] | 02d6e0940c949f88c70ac3e49dbbf072cf645b92 | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"cuneiform",
"akkadian",
"sumerian",
"license:mit",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["T5ForConditionalGeneration"], "model_type": "t5", "tokenizer_config": {"eos_token": "</s>", "unk_token": "<unk>", "pad_token": "<pad>"}} | {
"auto_model": "AutoModelForSeq2SeqLM",
"custom_class": null,
"pipeline_tag": "text2text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["cuneiform", "akkadian", "sumerian"]} | # Sumerian and Akkadian Cuneiform Language Translator
This is a translation network that understands Sumerian and Akkadian languages written in cuneiform.
It was trained on cuneiform transcribed in the CDLI ATF format. For example:
```text
translate Akkadian to English: 1(disz){d}szul3-ma-nu-_sag man gal?_-u2 _man_ ... | null | [
"mit"
] | null | null | null | null | null | [
"t5",
"T5ForConditionalGeneration",
"AutoModelForSeq2SeqLM"
] | [
"text2text-generation"
] | null | null | null |
62d9d087cfed764363b43770 | succinctly/text2image-prompt-generator | succinctly | null | 7,298 | 1,616,932 | False | 2022-07-21T22:17:43Z | 2022-08-20T06:01:10Z | transformers | 312 | 1 | null | text-generation | null | [
".gitattributes",
".gitignore",
"README.md",
"config.json",
"merges.txt",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"training_args.bin",
"vocab.json"
] | b7e96e38b77149daaded8f5101cdc81482330b4b | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"text2image",
"prompting",
"en",
"dataset:succinctly/midjourney-prompts",
"license:cc-by-2.0",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["GPT2LMHeadModel"], "model_type": "gpt2", "tokenizer_config": {"bos_token": "<|endoftext|>", "eos_token": "<|endoftext|>", "unk_token": "<|endoftext|>"}} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["succinctly/midjourney-prompts"], "eval_results": null, "language": ["en"], "library_name": null, "license": "cc-by-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["text2image", "prompting"], "thumbnail": "https://dr... | This is a GPT-2 model fine-tuned on the [succinctly/midjourney-prompts](https://huggingface.co/datasets/succinctly/midjourney-prompts) dataset, which contains 250k text prompts that users issued to the [Midjourney](https://www.midjourney.com/) text-to-image service over a month period. For more details on how this data... | null | [
"cc-by-2.0"
] | [
"succinctly/midjourney-prompts"
] | [
"en"
] | null | null | null | [
"GPT2LMHeadModel",
"AutoModelForCausalLM",
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632322d59e488d65bf2f4742 | 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 | [
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"... | 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>"}} | {
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} | {"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 | [
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] | [
"go_emotions"
] | [
"en"
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633307fcb0b628f4b3229600 | projecte-aina/roberta-large-ca-v2 | projecte-aina | null | 10 | 2,347 | False | 2022-09-27T14:26:04Z | 2022-12-23T13:04:49Z | transformers | 1 | 1 | null | fill-mask | null | [
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"CaText",
"Catalan Textual Corpus",
"ca",
"license:apache-2.0",
"endpoints_compatible",
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"region:us"
] | null | {"architectures": ["RobertaForMaskedLM"], "model_type": "roberta", "tokenizer_config": {"bos_token": {"__type": "AddedToken", "content": "<s>", "lstrip": false, "normalized": true, "rstrip": false, "single_word": false}, "cls_token": {"__type": "AddedToken", "content": "<s>", "lstrip": false, "normalized": true, "rstri... | {
"auto_model": "AutoModelForMaskedLM",
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"pipeline_tag": "fill-mask",
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} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["ca"], "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["catalan", "masked-lm", "RoBERTa-large-ca-v2", "CaText", "Catalan Textual Corpus... | # Catalan BERTa (roberta-large-ca-v2) large model
## Table of Contents
<details>
<summary>Click to expand</summary>
- [Model description](#model-description)
- [Intended uses and limitations](#intended-use)
- [How to use](#how-to-use)
- [Limitations and bias](#limitations-and-bias)
- [Training](#training)
- [Traini... | null | [
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63368c1431efcb5647ee4a94 | nielsr/lilt-roberta-en-base-finetuned-funsd | nielsr | {
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"_id": "6335a6689dd6d8e9c3a10811",
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} | 163 | 16,984 | False | 2022-09-30T06:26:28Z | 2024-12-09T09:17:44Z | transformers | 3 | 1 | [{"name": "lilt-roberta-en-base-finetuned-funsd", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "funsd-layoutlmv3", "type": "funsd-layoutlmv3", "config": "funsd", "split": "train", "args": "funsd"}, "metrics": [{"type": "precision", "value": 0.876167076167076... | token-classification | {"parameters": {"I64": 514, "F32": 130169799}, "total": 130170313} | [
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"auto_model": "AutoModelForTokenClassification",
"custom_class": null,
"pipeline_tag": "token-classification",
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} | {"base_model": "nielsr/lilt-roberta-en-base", "datasets": ["funsd-layoutlmv3"], "metrics": ["precision", "recall", "f1", "accuracy"], "tags": ["generated_from_trainer"], "inference": false, "model-index": [{"name": "lilt-roberta-en-base-finetuned-funsd", "results": [{"task": {"type": "token-classification", "name": "To... | <!-- 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. -->
# lilt-roberta-en-base-finetuned-funsd
This model is a fine-tuned version of [nielsr/lilt-roberta-en-base](https://huggingface.co/n... | null | null | [
"funsd-layoutlmv3"
] | null | 130,170,313 | null | [
"precision",
"recall",
"f1",
"accuracy"
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"lilt",
"LiltForTokenClassification"
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"token-classification"
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"text"
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63526d7c7e4cc3135fd0f17c | google/flan-t5-small | google | null | 578,708 | 14,936,788 | False | 2022-10-21T09:59:24Z | 2023-10-10T18:01:54Z | transformers | 469 | 1 | null | null | {"parameters": {"F32": 76961152}, "total": 76961152} | [
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"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>"}} | {
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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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"en",
"fr",
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"de",
"multilingual"
] | 76,961,152 | null | null | [
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"AutoModelForSeq2SeqLM"
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6352c0d3507b679c3c5f41f4 | google/flan-t5-xxl | google | null | 18,855 | 13,000,659 | False | 2022-10-21T15:54:59Z | 2023-07-27T11:42:14Z | transformers | 1,279 | 1 | null | null | {"parameters": {"F32": 11266928640}, "total": 11266928640} | [
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"flax_model.msgpack.index.json",
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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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"pipeline_tag": "text2text-generation",
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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, Risk... | null | [
"apache-2.0"
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] | [
"en",
"fr",
"ro",
"de",
"multilingual"
] | 11,266,928,640 | null | null | [
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63597f2360e2f140f44c6057 | rufimelo/bert-large-portuguese-cased-sts | rufimelo | null | 1,113 | 51,209 | False | 2022-10-26T18:40:35Z | 2024-12-15T23:07:55Z | sentence-transformers | 15 | 1 | [{"name": "BERTimbau", "results": [{"task": {"name": "STS", "type": "STS"}, "metrics": [{"name": "Pearson Correlation - assin Dataset", "type": "Pearson Correlation", "value": 0.81758, "verified": false}, {"name": "Pearson Correlation - assin2 Dataset", "type": "Pearson Correlation", "value": 0.83784, "verified": false... | sentence-similarity | {"parameters": {"I64": 512, "F32": 334396416}, "total": 334396928} | [
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"pytorch_model.bin",
"sentence_bert_config.json",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"voc... | e5c615d0dd46078764b7f58835b95af95f153e95 | [
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"model-index",
"text-embeddings-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | 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"
} | {"base_model": null, "datasets": ["assin", "assin2", "stsb_multi_mt"], "eval_results": null, "language": ["pt"], "library_name": null, "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "sentence-similarity", "tags": ["sentence-transformers", "sentence-simi... | # rufimelo/bert-large-portuguese-cased-sts2
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.
rufimelo/bert-large-portuguese-cased-sts derives from [BERTimbau](https://hug... | null | null | [
"assin",
"assin2",
"stsb_multi_mt"
] | [
"pt"
] | 334,396,928 | null | null | [
"BertModel",
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63cdde28e75230ae94efc7fd | CarperAI/diff-codegen-6b-v2 | CarperAI | null | 40 | 1,650 | False | 2023-01-23T01:08:56Z | 2024-12-03T04:47:47Z | transformers | 40 | 1 | null | text-generation | {"parameters": {"F16": 7056798839, "U8": 138412032}, "total": 7195210871} | [
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] | 9ece3f21ec9de946f22fe8a3d83458c3aec745f4 | [
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"The Pile",
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"arxiv:2201.07311",
"arxiv:2101.00027",
"arxiv:2203.13474",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["CodeGenForCausalLM"], "model_type": "codegen", "tokenizer_config": {"bos_token": "<|endoftext|>", "eos_token": "<|endoftext|>", "unk_token": "<|endoftext|>"}} | {
"auto_model": "AutoModelForCausalLM",
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"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en", "code"], "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["Diff Model", "pytorch", "causal-lm", "code-generation", "The Pile"]} | # Diff-Codegen-6B v2 Model Card
## Model Description
diff-codegen-6b-v2 is a diff model for code generation, released by [CarperAI](http://carper.ai/). A diff model is an autoregressive language model trained on edits to a piece of text, formatted in [Unified Diff Format](https://en.wikipedia.org/wiki/Diff#Unified_fo... | null | [
"mit"
] | null | [
"en",
"code"
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63d2c362c3eb22526cada733 | geolocal/StreetCLIP | geolocal | null | 10,278 | 321,582 | False | 2023-01-26T18:16:02Z | 2023-09-13T00:03:57Z | transformers | 105 | 1 | null | zero-shot-image-classification | null | [
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] | null | {"architectures": ["CLIPModel"], "model_type": "clip", "tokenizer_config": {"bos_token": {"__type": "AddedToken", "content": "<|startoftext|>", "lstrip": false, "normalized": true, "rstrip": false, "single_word": false}, "eos_token": {"__type": "AddedToken", "content": "<|endoftext|>", "lstrip": false, "normalized": tr... | {
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"pipeline_tag": "zero-shot-image-classification",
"processor": "AutoProcessor"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "cc-by-nc-4.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "zero-shot-image-classification", "tags": ["geolocalization", "geolocation", "geogr... | # Model Card for StreetCLIP
StreetCLIP is a robust foundation model for open-domain image geolocalization and other
geographic and climate-related tasks.
Trained on an original dataset of 1.1 million street-level urban and rural geo-tagged images, it achieves
state-of-the-art performance on multiple open-domain image... | null | [
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63dd0870723732ce42d8a512 | Writer/palmyra-base | Writer | null | 928 | 64,101 | False | 2023-02-03T13:13:20Z | 2024-12-24T06:01:28Z | transformers | 46 | 1 | null | text-generation | null | [
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} | {"base_model": null, "datasets": ["English"], "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": "text-generation", "tags": ["text generation", "pytorch", "causal-lm", "Writer... | **DEPRECATED MODEL NOTICE**
==========================
Please note that this model is no longer maintained or supported by our team. We strongly advise against using it in production or for any critical applications.
Instead, we recommend using our latest and greatest models, which can be found at:
https://huggingfa... | null | [
"apache-2.0"
] | [
"English"
] | [
"en"
] | null | null | null | [
"GPT2LMHeadModel",
"AutoModelForCausalLM",
"gpt2"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
63ea5ee51edeb3fe8f1f0aa2 | EleutherAI/pythia-70m-deduped | EleutherAI | null | 1,004,826 | 7,603,824 | False | 2023-02-13T16:01:41Z | 2023-07-09T16:07:33Z | transformers | 28 | 1 | null | text-generation | {"parameters": {"F16": 70426672, "U8": 25165824}, "total": 95592496} | [
".gitattributes",
"README.md",
"config.json",
"model.safetensors",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json"
] | e93a9faa9c77e5d09219f6c868bfc7a1bd65593c | [
"transformers",
"pytorch",
"safetensors",
"gpt_neox",
"text-generation",
"causal-lm",
"pythia",
"en",
"dataset:EleutherAI/the_pile_deduplicated",
"arxiv:2304.01373",
"arxiv:2101.00027",
"arxiv:2201.07311",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"depl... | null | {"architectures": ["GPTNeoXForCausalLM"], "model_type": "gpt_neox", "tokenizer_config": {"bos_token": "<|endoftext|>", "eos_token": "<|endoftext|>", "unk_token": "<|endoftext|>"}} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["EleutherAI/the_pile_deduplicated"], "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": ["pytorch", "causal-lm", "pythia"]} | The *Pythia Scaling Suite* is a collection of models developed to facilitate
interpretability research [(see paper)](https://arxiv.org/pdf/2304.01373.pdf).
It contains two sets of eight models of sizes
70M, 160M, 410M, 1B, 1.4B, 2.8B, 6.9B, and 12B. For each size, there are two
models: one trained on the Pile, and ... | null | [
"apache-2.0"
] | [
"EleutherAI/the_pile_deduplicated"
] | [
"en"
] | 95,592,496 | null | null | [
"gpt_neox",
"AutoModelForCausalLM",
"GPTNeoXForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
63f83217a6db61fe73683c1f | kevinscaria/joint_tk-instruct-base-def-pos-neg-neut-combined | kevinscaria | null | 118 | 34,124 | False | 2023-02-24T03:42:15Z | 2023-02-24T04:27:38Z | transformers | 2 | 1 | null | text-generation | null | [
".gitattributes",
"README.md",
"config.json",
"generation_config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"spiece.model",
"tokenizer.json",
"tokenizer_config.json",
"training_args.bin"
] | 0361fcd2c40d26d2b810f4cb60c938b3a4580967 | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"NLP",
"dataset:Yaxin/SemEval2014Task4Raw",
"arxiv:2302.08624",
"license:mit",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 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": ["Yaxin/SemEval2014Task4Raw"], "eval_results": null, "language": null, "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": ["f1", "precision", "recall"], "model_name": null, "pipeline_tag": "text2text-generation", "tags": ["NLP"]} | # joint_tk-instruct-base-def-pos-neg-neut-combined
This model is finetuned for the Joint Task. The finetuning was carried out by adding prompts of the form:
- definition + 2 positive examples + 2 negative examples + 2 neutral examples
The prompt is prepended onto each input review. It is important to note that **thi... | null | [
"mit"
] | [
"Yaxin/SemEval2014Task4Raw"
] | null | null | null | [
"f1",
"precision",
"recall"
] | [
"t5",
"T5ForConditionalGeneration",
"AutoModelForSeq2SeqLM"
] | [
"text2text-generation",
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
6406013ccd20b4953cd6110f | Narsil/finbert | Narsil | null | 9 | 878 | False | 2023-03-06T15:05:32Z | 2023-03-06T23:22:30Z | transformers | 1 | 1 | null | text-classification | {"parameters": {"I64": 512, "F32": 109484547}, "total": 109485059} | [
".gitattributes",
"README.md",
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"flax_model.msgpack",
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"pytorch_model.bin",
"special_tokens_map.json",
"tf_model.h5",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | e8a9eb90fb0b7eeb09f889ba220a48e77233f2e4 | [
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"text-classification",
"financial-sentiment-analysis",
"sentiment-analysis",
"en",
"arxiv:1908.10063",
"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": null, "language": "en", "library_name": null, "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["financial-sentiment-analysis", "sentiment-analysis"], "widget": [{"text": "Stocks rallied... | FinBERT is a pre-trained NLP model to analyze sentiment of financial text. It is built by further training the BERT language model in the finance domain, using a large financial corpus and thereby fine-tuning it for financial sentiment classification. [Financial PhraseBank](https://www.researchgate.net/publication/2512... | null | null | null | [
"en"
] | 109,485,059 | null | null | [
"BertForSequenceClassification",
"bert",
"AutoModelForSequenceClassification"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
640971fc60fc65165c547137 | ddobokki/ko-trocr | ddobokki | null | 792 | 81,335 | False | 2023-03-09T05:43:24Z | 2024-10-22T14:54:04Z | transformers | 33 | 1 | null | image-to-text | {"parameters": {"I64": 512, "F16": 213693224}, "total": 213693736} | [
".gitattributes",
"README.md",
"config.json",
"model.safetensors",
"preprocessor_config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | fe4f13c7e32b6a0c964b0f120f1cc80845b426f9 | [
"transformers",
"pytorch",
"safetensors",
"vision-encoder-decoder",
"image-text-to-text",
"ocr",
"image-to-text",
"ko",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["VisionEncoderDecoderModel"], "model_type": "vision-encoder-decoder", "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": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["ko"], "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "image-to-text", "tags": ["ocr"], "widget": [{"src": "https://raw.githubusercontent.com/ddobokk... | # korean trocr model
- trocr 모델은 디코더의 토크나이저에 없는 글자는 ocr 하지 못하기 때문에, 초성을 사용하는 토크나이저를 사용하는 디코더 모델을 사용하여 초성도 UNK로 나오지 않게 만든 trocr 모델입니다.
- [2023 교원그룹 AI OCR 챌린지](https://dacon.io/competitions/official/236042/overview/description) 에서 얻었던 노하우를 활용하여 제작하였습니다.
## train datasets
AI Hub
- [다양한 형태의 한글 문자 OCR](https://aihub.or.kr/... | null | [
"apache-2.0"
] | null | [
"ko"
] | 213,693,736 | null | null | [
"AutoModelForImageTextToText",
"vision-encoder-decoder",
"VisionEncoderDecoderModel"
] | [
"image-text-to-text",
"image-to-text"
] | [
"multimodal"
] | [
"text",
"image"
] | [
"text"
] |
6421582194fb039f53684d57 | sileod/deberta-v3-large-tasksource-nli | sileod | null | 4,475 | 113,379 | False | 2023-03-27T08:47:29Z | 2024-02-17T05:12:52Z | transformers | 39 | 1 | null | zero-shot-classification | {"parameters": {"I64": 512, "F32": 435064835}, "total": 435065347} | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"model.safetensors",
"pytorch_model.bin",
"special_tokens_map.json",
"spm.model",
"tokenizer.json",
"tokenizer_config.json"
] | 212de447184bda8fb9415a2e5697846864ddf304 | [
"transformers",
"pytorch",
"safetensors",
"deberta-v2",
"text-classification",
"deberta-v3-large",
"nli",
"natural-language-inference",
"multitask",
"multi-task",
"pipeline",
"extreme-multi-task",
"extreme-mtl",
"tasksource",
"zero-shot",
"rlhf",
"zero-shot-classification",
"en",
... | 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"
} | {"base_model": null, "datasets": ["glue", "super_glue", "anli", "metaeval/babi_nli", "sick", "snli", "scitail", "hans", "alisawuffles/WANLI", "metaeval/recast", "sileod/probability_words_nli", "joey234/nan-nli", "pietrolesci/nli_fever", "pietrolesci/breaking_nli", "pietrolesci/conj_nli", "pietrolesci/fracas", "pietrole... | # Model Card for DeBERTa-v3-large-tasksource-nli
DeBERTa-v3-large fine-tuned with multi-task learning on 600 tasks of the [tasksource collection](https://github.com/sileod/tasksource/)
You can further fine-tune this model to use it for any classification or multiple-choice task.
This checkpoint has strong zero-shot va... | null | [
"apache-2.0"
] | [
"glue",
"super_glue",
"anli",
"metaeval/babi_nli",
"sick",
"snli",
"scitail",
"hans",
"alisawuffles/WANLI",
"metaeval/recast",
"sileod/probability_words_nli",
"joey234/nan-nli",
"pietrolesci/nli_fever",
"pietrolesci/breaking_nli",
"pietrolesci/conj_nli",
"pietrolesci/fracas",
"pietro... | [
"en"
] | 435,065,347 | null | [
"accuracy"
] | [
"AutoModelForSequenceClassification",
"deberta-v2",
"DebertaV2ForSequenceClassification"
] | [
"zero-shot-classification",
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
643507079f2ac2e2135e0c81 | zaemyung/DElIteraTeR-PEGASUS-Multi-Sent-Revision-Generator | zaemyung | null | 17 | 513 | False | 2023-04-11T07:06:47Z | 2023-10-06T01:07:07Z | transformers | 1 | 1 | null | text-generation | {"parameters": {"F32": 570905459}, "total": 570905459} | [
".gitattributes",
"README.md",
"added_tokens.json",
"all_results.json",
"config.json",
"eval_results.json",
"model.safetensors",
"pytorch_model.bin",
"special_tokens_map.json",
"spiece.model",
"tokenizer.json",
"tokenizer_config.json",
"train_results.json",
"trainer_state.json",
"trainin... | 3da459958ec3c4217cdc8e4737618d27534f8d0c | [
"transformers",
"pytorch",
"safetensors",
"pegasus",
"text2text-generation",
"en",
"dataset:zaemyung/IteraTeR_plus",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["PegasusForConditionalGeneration"], "model_type": "pegasus", "tokenizer_config": {"pad_token": "<pad>", "eos_token": "</s>", "unk_token": "<unk>", "mask_token": "<mask_2>"}} | {
"auto_model": "AutoModelForSeq2SeqLM",
"custom_class": null,
"pipeline_tag": "text2text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["zaemyung/IteraTeR_plus"], "eval_results": null, "language": ["en"], "library_name": null, "license": "cc-by-nc-4.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text2text-generation", "tags": null} | # DElIteraTeR-PEGASUS-Multi-Sent-Revision-Generator
This model was obtained by fine-tuning [google/pegasus-large](https://huggingface.co/google/pegasus-large) on [IteraTeR+](https://huggingface.co/datasets/zaemyung/IteraTeR_plus) `multi_sent` dataset.
Paper: [Improving Iterative Text Revision by Learning Where to Edi... | null | [
"cc-by-nc-4.0"
] | [
"zaemyung/IteraTeR_plus"
] | [
"en"
] | 570,905,459 | null | null | [
"pegasus",
"PegasusForConditionalGeneration",
"AutoModelForSeq2SeqLM"
] | [
"text2text-generation",
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
6454b9c0f61f10d69dbfd49f | TheBloke/WizardLM-7B-uncensored-GPTQ | TheBloke | {
"models": [
{
"_id": "6454163772d331dec8a15584",
"id": "QuixiAI/WizardLM-7B-Uncensored"
}
],
"relation": "quantized"
} | 771 | 134,603 | False | 2023-05-05T08:09:36Z | 2023-10-26T09:39:38Z | transformers | 195 | 1 | null | text-generation | {"parameters": {"F32": 2048, "I32": 6476005376, "F16": 262418432}, "total": 6738425856} | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
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"model.safetensors",
"quantize_config.json",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer.model",
"tokenizer_config.json"
] | 4a524bec59b89e995583018b718c3c7394cade8a | [
"transformers",
"safetensors",
"llama",
"text-generation",
"uncensored",
"dataset:ehartford/WizardLM_alpaca_evol_instruct_70k_unfiltered",
"base_model:QuixiAI/WizardLM-7B-Uncensored",
"base_model:quantized:QuixiAI/WizardLM-7B-Uncensored",
"license:other",
"text-generation-inference",
"4-bit",
... | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "quantization_config": {"bits": 4, "quant_method": "gptq"}, "tokenizer_config": {"bos_token": {"__type": "AddedToken", "content": "<s>", "lstrip": false, "normalized": true, "rstrip": false, "single_word": false}, "eos_token": {"__type": "AddedToken", "cont... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "ehartford/WizardLM-7B-Uncensored", "datasets": ["ehartford/WizardLM_alpaca_evol_instruct_70k_unfiltered"], "eval_results": null, "language": null, "library_name": null, "license": "other", "license_name": null, "license_link": null, "metrics": null, "model_name": "Wizardlm 7B Uncensored", "pipeline_tag"... | <!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<d... | null | [
"other"
] | [
"ehartford/WizardLM_alpaca_evol_instruct_70k_unfiltered"
] | null | 6,738,425,856 | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
64552d5fd55525a4fee98c2a | 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",
"README.md",
"config.json",
"model.safetensors",
"onnx/config.json",
"onnx/model.onnx",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"training_args.bin",
"vocab.txt"
] | cf991100d706c13c0a080c097134c05b7f436c45 | [
"transformers",
"pytorch",
"onnx",
"safetensors",
"distilbert",
"text-classification",
"sentiment-analysis",
"zero-shot-distillation",
"distillation",
"zero-shot-classification",
"debarta-v3",
"en",
"ar",
"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"
] | [
"zero-shot-classification",
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
645c365111b04b05ad07460d | QuixiAI/Wizard-Vicuna-13B-Uncensored | QuixiAI | null | 1,204 | 78,339 | False | 2023-05-11T00:26:57Z | 2023-11-18T03:51:02Z | transformers | 321 | 1 | null | text-generation | null | [
".gitattributes",
".gitignore",
"README.md",
"config.json",
"generation_config.json",
"pytorch_model-00001-of-00006.bin",
"pytorch_model-00002-of-00006.bin",
"pytorch_model-00003-of-00006.bin",
"pytorch_model-00004-of-00006.bin",
"pytorch_model-00005-of-00006.bin",
"pytorch_model-00006-of-00006.... | 682f6583699ecd916a7d106393f68c44a1c7abf2 | [
"transformers",
"pytorch",
"llama",
"text-generation",
"uncensored",
"en",
"dataset:ehartford/wizard_vicuna_70k_unfiltered",
"license:other",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 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": ["ehartford/wizard_vicuna_70k_unfiltered"], "eval_results": null, "language": ["en"], "library_name": null, "license": "other", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["uncensored"]} | null | null | [
"other"
] | [
"ehartford/wizard_vicuna_70k_unfiltered"
] | [
"en"
] | null | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
645e8d1e6320b0efe40adaf6 | roneneldan/TinyStories-1M | roneneldan | null | 86,710 | 979,823 | False | 2023-05-12T19:01:50Z | 2025-12-18T19:43:10Z | transformers | 62 | 1 | null | text-generation | null | [
".gitattributes",
"README.md",
"config.json",
"merges.txt",
"pytorch_model.bin",
"readme.md",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | 77f1b168e219585646439073245fe87e56b3023e | [
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"dataset:roneneldan/TinyStories",
"arxiv:2305.07759",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["GPTNeoForCausalLM"], "model_type": "gpt_neo", "tokenizer_config": {"bos_token": {"__type": "AddedToken", "content": "<|endoftext|>", "lstrip": false, "normalized": true, "rstrip": false, "single_word": false}, "eos_token": {"__type": "AddedToken", "content": "<|endoftext|>", "lstrip": false, "normal... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["roneneldan/TinyStories"], "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": null} | Model trained on the TinyStories Dataset, see https://arxiv.org/abs/2305.07759
------ EXAMPLE USAGE ---
from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
model = AutoModelForCausalLM.from_pretrained('roneneldan/TinyStories-1M')
tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-n... | null | null | [
"roneneldan/TinyStories"
] | null | null | null | null | [
"gpt_neo",
"AutoModelForCausalLM",
"GPTNeoForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
646e4b5c7942c36e9da4d5eb | bowphs/GreBerta | bowphs | null | 58 | 27,948 | False | 2023-05-24T17:37:32Z | 2024-11-12T16:11:52Z | transformers | 8 | 1 | null | fill-mask | {"parameters": {"I64": 514, "F32": 126031648}, "total": 126032162} | [
".gitattributes",
"README.md",
"config.json",
"merges.txt",
"model.safetensors",
"pytorch_model.bin",
"special_tokens_map.json",
"tf_model.h5",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | 3dce05464f1f429d68acd9b09e117632490c92d4 | [
"transformers",
"pytorch",
"tf",
"safetensors",
"roberta",
"fill-mask",
"grc",
"dataset:bowphs/internet_archive_filtered",
"arxiv:2305.13698",
"license:apache-2.0",
"deploy:azure",
"region:us"
] | null | {"architectures": ["RobertaForMaskedLM"], "model_type": "roberta", "tokenizer_config": {"bos_token": {"__type": "AddedToken", "content": "<s>", "lstrip": false, "normalized": true, "rstrip": false, "single_word": false}, "cls_token": {"__type": "AddedToken", "content": "<s>", "lstrip": false, "normalized": true, "rstri... | {
"auto_model": "AutoModelForMaskedLM",
"custom_class": null,
"pipeline_tag": "fill-mask",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["bowphs/internet_archive_filtered"], "eval_results": null, "language": "grc", "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null, "inference": false} | # GrεBerta
The paper [Exploring Language Models for Classical Philology](https://todo.com) is the first effort to systematically provide state-of-the-art language models for Classical Philology. GrεBerta is a RoBerta-base sized, monolingual, encoder-only variant. Further information can be found in our paper or in our... | null | [
"apache-2.0"
] | [
"bowphs/internet_archive_filtered"
] | [
"grc"
] | 126,032,162 | null | null | [
"roberta",
"AutoModelForMaskedLM",
"RobertaForMaskedLM"
] | [
"fill-mask"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
64820fa10aad5153844b45ed | facebook/musicgen-small | facebook | null | 113,691 | 10,340,334 | False | 2023-06-08T17:28:01Z | 2023-11-17T13:56:10Z | transformers | 480 | 1 | null | text-to-audio | null | [
".gitattributes",
"README.md",
"compression_state_dict.bin",
"config.json",
"generation_config.json",
"model.safetensors",
"preprocessor_config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"spiece.model",
"state_dict.bin",
"tokenizer.json",
"tokenizer_config.json"
] | 4c8334b02c6ec4e8664a91979669a501ec497792 | [
"transformers",
"pytorch",
"safetensors",
"musicgen",
"text-to-audio",
"arxiv:2306.05284",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["MusicgenForConditionalGeneration"], "model_type": "musicgen", "tokenizer_config": {"eos_token": "</s>", "pad_token": "<pad>", "unk_token": "<unk>"}} | {
"auto_model": "AutoModelForTextToWaveform",
"custom_class": null,
"pipeline_tag": "text-to-audio",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": null, "license": "cc-by-nc-4.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-to-audio", "tags": ["musicgen"], "inference": true, "widget": [{"text": "a funky house wi... | # MusicGen - Small - 300M
MusicGen is a text-to-music model capable of genreating high-quality music samples conditioned on text descriptions or audio prompts.
It is a single stage auto-regressive Transformer model trained over a 32kHz EnCodec tokenizer with 4 codebooks sampled at 50 Hz.
Unlike existing methods, like... | null | [
"cc-by-nc-4.0"
] | null | null | null | null | null | [
"musicgen",
"MusicgenForConditionalGeneration",
"AutoModelForTextToWaveform"
] | [
"text-to-audio"
] | [
"text"
] | [
"text"
] | [
"audio"
] |
64821504707a292dcb1cea79 | facebook/musicgen-large | facebook | null | 19,778 | 627,284 | False | 2023-06-08T17:51:00Z | 2023-11-17T15:25:38Z | transformers | 525 | 1 | null | text-to-audio | null | [
".gitattributes",
"README.md",
"compression_state_dict.bin",
"config.json",
"generation_config.json",
"preprocessor_config.json",
"pytorch_model-00001-of-00002.bin",
"pytorch_model-00002-of-00002.bin",
"pytorch_model.bin.index.json",
"special_tokens_map.json",
"spiece.model",
"state_dict.bin",... | 15ccdc92099879e47b6da12c350cdb71d4eab3ca | [
"transformers",
"pytorch",
"musicgen",
"text-to-audio",
"arxiv:2306.05284",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["MusicgenForConditionalGeneration"], "model_type": "musicgen", "tokenizer_config": {"eos_token": "</s>", "pad_token": "<pad>", "unk_token": "<unk>"}} | {
"auto_model": "AutoModelForTextToWaveform",
"custom_class": null,
"pipeline_tag": "text-to-audio",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": null, "license": "cc-by-nc-4.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["musicgen"], "inference": true} | # MusicGen - Large - 3.3B
MusicGen is a text-to-music model capable of genreating high-quality music samples conditioned on text descriptions or audio prompts.
It is a single stage auto-regressive Transformer model trained over a 32kHz EnCodec tokenizer with 4 codebooks sampled at 50 Hz.
Unlike existing methods, like... | null | [
"cc-by-nc-4.0"
] | null | null | null | null | null | [
"musicgen",
"MusicgenForConditionalGeneration",
"AutoModelForTextToWaveform"
] | [
"text-to-audio"
] | [
"text"
] | [
"text"
] | [
"audio"
] |
6487204b252cd3fb59ca6485 | Babelscape/mrebel-large | Babelscape | null | 779 | 615,476 | False | 2023-06-12T13:40:27Z | 2023-06-20T15:40:58Z | transformers | 78 | 1 | null | translation | {"parameters": {"F32": 611146967}, "total": 611146967} | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"model.safetensors",
"pytorch_model.bin",
"sentencepiece.bpe.model",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json"
] | 50e0587ac7ac87d28b9abd069d72333528a5aa09 | [
"transformers",
"pytorch",
"safetensors",
"mbart",
"text2text-generation",
"seq2seq",
"relation-extraction",
"translation",
"ar",
"ca",
"de",
"el",
"en",
"es",
"fr",
"hi",
"it",
"ja",
"ko",
"nl",
"pl",
"pt",
"ru",
"sv",
"vi",
"zh",
"dataset:Babelscape/SREDFM",
"... | null | {"architectures": ["MBartForConditionalGeneration"], "model_type": "mbart", "tokenizer_config": {"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": true, "__typ... | {
"auto_model": "AutoModelForSeq2SeqLM",
"custom_class": null,
"pipeline_tag": "text2text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["Babelscape/SREDFM"], "eval_results": null, "language": ["ar", "ca", "de", "el", "en", "es", "fr", "hi", "it", "ja", "ko", "nl", "pl", "pt", "ru", "sv", "vi", "zh"], "library_name": null, "license": "cc-by-nc-sa-4.0", "license_name": null, "license_link": null, "metrics": null, "model_... | # RED<sup>FM</sup>: a Filtered and Multilingual Relation Extraction Dataset
This is a multilingual version of [REBEL](https://huggingface.co/Babelscape/rebel-large). It can be used as a standalone multulingual Relation Extraction system, or as a pretrained system to be tuned on multilingual Relation Extraction dataset... | null | [
"cc-by-nc-sa-4.0"
] | [
"Babelscape/SREDFM"
] | [
"ar",
"ca",
"de",
"el",
"en",
"es",
"fr",
"hi",
"it",
"ja",
"ko",
"nl",
"pl",
"pt",
"ru",
"sv",
"vi",
"zh"
] | 611,146,967 | null | null | [
"AutoModelForSeq2SeqLM",
"MBartForConditionalGeneration",
"mbart"
] | [
"text2text-generation",
"translation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
64a0dda4602340a14257e23d | google/umt5-xxl | google | null | 62,064 | 234,910 | False | 2023-07-02T02:15:00Z | 2023-07-03T05:37:17Z | transformers | 59 | 1 | null | null | null | [
".gitattributes",
"README.md",
"config.json",
"generation_config.json",
"pytorch_model-00001-of-00006.bin",
"pytorch_model-00002-of-00006.bin",
"pytorch_model-00003-of-00006.bin",
"pytorch_model-00004-of-00006.bin",
"pytorch_model-00005-of-00006.bin",
"pytorch_model-00006-of-00006.bin",
"pytorch... | 66cb9e7e85526fe440a945569e42c72fb6cbc0ad | [
"transformers",
"pytorch",
"text2text-generation",
"multilingual",
"af",
"am",
"ar",
"az",
"be",
"bg",
"bn",
"ca",
"ceb",
"co",
"cs",
"cy",
"da",
"de",
"el",
"en",
"eo",
"es",
"et",
"eu",
"fa",
"fi",
"fil",
"fr",
"fy",
"ga",
"gd",
"gl",
"gu",
"ha",
... | null | {"architectures": ["UMT5ForConditionalGeneration"], "tokenizer_config": {"bos_token": "<s>", "eos_token": "</s>", "pad_token": "<pad>", "unk_token": "<unk>"}} | {
"auto_model": "AutoModelForSeq2SeqLM",
"custom_class": null,
"pipeline_tag": "text2text-generation",
"processor": null
} | {"base_model": null, "datasets": ["mc4"], "eval_results": null, "language": ["multilingual", "af", "am", "ar", "az", "be", "bg", "bn", "ca", "ceb", "co", "cs", "cy", "da", "de", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fil", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "haw", "hi", "hmn", "ht", "hu", "hy", "ig", "... | [Google's UMT5](https://github.com/google-research/multilingual-t5)
UMT5 is pretrained on the an updated version of [mC4](https://www.tensorflow.org/datasets/catalog/c4#c4multilingual) corpus, covering 107 languages:
Afrikaans, Albanian, Amharic, Arabic, Armenian, Azerbaijani, Basque, Belarusian, Bengali, Bulgarian, ... | null | [
"apache-2.0"
] | [
"mc4"
] | [
"multilingual",
"af",
"am",
"ar",
"az",
"be",
"bg",
"bn",
"ca",
"ceb",
"co",
"cs",
"cy",
"da",
"de",
"el",
"en",
"eo",
"es",
"et",
"eu",
"fa",
"fi",
"fil",
"fr",
"fy",
"ga",
"gd",
"gl",
"gu",
"ha",
"haw",
"hi",
"hmn",
"ht",
"hu",
"hy",
"ig",
... | null | null | null | [
"AutoModelForSeq2SeqLM",
"UMT5ForConditionalGeneration"
] | [
"text2text-generation"
] | null | null | null |
64b7ebe290154e1f1d08b56b | EleutherAI/pythia-14m-deduped | EleutherAI | null | 19,470 | 3,648,585 | False | 2023-07-19T13:57:54Z | 2026-02-12T04:16:13Z | transformers | 29 | 1 | null | text-generation | {"parameters": {"F16": 14067736, "BOOL": 25165824}, "total": 39233560} | [
".gitattributes",
"README.md",
"config.json",
"generation_config.json",
"model.safetensors",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json"
] | 7386d9a4ae45aef494a6e704910394def3037fc5 | [
"transformers",
"pytorch",
"safetensors",
"gpt_neox",
"text-generation",
"causal-lm",
"pythia",
"en",
"dataset:EleutherAI/pile",
"arxiv:2304.01373",
"arxiv:2101.00027",
"arxiv:2201.07311",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
"reg... | null | {"architectures": ["GPTNeoXForCausalLM"], "model_type": "gpt_neox", "tokenizer_config": {"bos_token": "<|endoftext|>", "eos_token": "<|endoftext|>", "unk_token": "<|endoftext|>"}} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["EleutherAI/pile"], "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": null, "tags": ["pytorch", "causal-lm", "pythia"]} | null | null | [
"apache-2.0"
] | [
"EleutherAI/pile"
] | [
"en"
] | 39,233,560 | null | null | [
"gpt_neox",
"AutoModelForCausalLM",
"GPTNeoXForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
64b9c26596676e40d0f3983d | NousResearch/Nous-Hermes-Llama2-13b | NousResearch | null | 1,549 | 1,265,907 | False | 2023-07-20T23:25:25Z | 2024-04-23T23:18:53Z | transformers | 321 | 1 | null | text-generation | {"parameters": {"F32": 2560, "BF16": 13016192000}, "total": 13016194560} | [
".gitattributes",
"Example1.png",
"README.md",
"config.json",
"example2.png",
"example3.png",
"example5.png",
"generation_config.json",
"model-00001-of-00003.safetensors",
"model-00002-of-00003.safetensors",
"model-00003-of-00003.safetensors",
"model.safetensors.index.json",
"pytorch_model-0... | a5787bbb8ed8d322f3b1f91f9afd7fe07e7f041a | [
"transformers",
"pytorch",
"safetensors",
"llama",
"text-generation",
"llama-2",
"self-instruct",
"distillation",
"synthetic instruction",
"en",
"license:mit",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | 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": 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": ["llama-2", "self-instruct", "distillation", "synthetic instruction"]} | # Model Card: Nous-Hermes-Llama2-13b
Compute provided by our project sponsor Redmond AI, thank you! Follow RedmondAI on Twitter @RedmondAI.
## Model Description
Nous-Hermes-Llama2-13b is a state-of-the-art language model fine-tuned on over 300,000 instructions. This model was fine-tuned by Nous Research, with Tekniu... | null | [
"mit"
] | null | [
"en"
] | 13,016,194,560 | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
64ca02286a26cddbecdc9132 | BAAI/bge-large-zh | BAAI | null | 13,085 | 853,457 | False | 2023-08-02T07:13:44Z | 2023-10-12T03:38:28Z | transformers | 345 | 1 | null | feature-extraction | {"parameters": {"I64": 512, "F32": 325522432}, "total": 325522944} | [
".gitattributes",
"1_Pooling/config.json",
"README.md",
"config.json",
"config_sentence_transformers.json",
"model.safetensors",
"modules.json",
"pytorch_model.bin",
"sentence_bert_config.json",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | b5d9f5c027e87b6f0b6fa4b614f8f9cdc45ce0e8 | [
"transformers",
"pytorch",
"safetensors",
"bert",
"feature-extraction",
"zh",
"arxiv:2310.07554",
"arxiv:2309.07597",
"license:mit",
"text-embeddings-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | 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"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["zh"], "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | **Recommend switching to newest [BAAI/bge-large-zh-v1.5](https://huggingface.co/BAAI/bge-large-zh-v1.5), which has more reasonable similarity distribution and same method of usage.**
<h1 align="center">FlagEmbedding</h1>
<h4 align="center">
<p>
<a href=#model-list>Model List</a> |
<a href=#frequ... | null | [
"mit"
] | null | [
"zh"
] | 325,522,944 | null | null | [
"BertModel",
"AutoModel",
"bert"
] | [
"feature-extraction"
] | [
"multimodal"
] | [
"text"
] | [
"embeddings"
] |
64d6ae4e54bb9eb70414d321 | defog/sqlcoder | defog | null | 407 | 68,061 | False | 2023-08-11T21:55:26Z | 2024-03-01T09:38:12Z | transformers | 325 | 1 | null | text-generation | null | [
".gitattributes",
"LICENSE",
"README.md",
"config.json",
"generation_config.json",
"inference.py",
"merges.txt",
"pytorch_model-00001-of-00004.bin",
"pytorch_model-00002-of-00004.bin",
"pytorch_model-00003-of-00004.bin",
"pytorch_model-00004-of-00004.bin",
"pytorch_model.bin.index.json",
"sp... | 4945642bc91bf56c051f8288d76a684566837d15 | [
"transformers",
"pytorch",
"gpt_bigcode",
"text-generation",
"code",
"en",
"license:other",
"text-generation-inference",
"region:us"
] | null | {"architectures": ["GPTBigCodeForCausalLM"], "model_type": "gpt_bigcode", "tokenizer_config": {"bos_token": "<|endoftext|>", "eos_token": "<|endoftext|>", "unk_token": "<|endoftext|>"}} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "other", "license_name": null, "license_link": null, "metrics": ["code_eval"], "model_name": null, "pipeline_tag": "text-generation", "tags": ["code"], "inference": false} | # ARCHIVE NOTICE
This repository is now significantly outdated. You should use the repository at [sqlcoder-7b-2](https://huggingface.co/defog/sqlcoder-7b-2) instead. It is significantly better and consumes fewer GPU resources.
# Defog SQLCoder
Defog's SQLCoder is a state-of-the-art LLM for converting natural language ... | null | [
"other"
] | null | [
"en"
] | null | null | [
"code_eval"
] | [
"gpt_bigcode",
"AutoModelForCausalLM",
"GPTBigCodeForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
64f451f9fd7ec9f827d6ac47 | gotutiyan/gector-deberta-large-5k | gotutiyan | null | 2,904 | 31,730 | False | 2023-09-03T09:29:29Z | 2025-12-02T09:57:57Z | transformers | 3 | 1 | null | null | {"parameters": {"I64": 512, "F32": 410292107}, "total": 410292619} | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"merges.txt",
"model.safetensors",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | 5fa80d75504eaf7c867a0d4c5a26752df6585aa1 | [
"transformers",
"pytorch",
"safetensors",
"GECToR_gotutiyan",
"grammatical error correction",
"en",
"endpoints_compatible",
"region:us"
] | null | {"tokenizer_config": {"bos_token": {"__type": "AddedToken", "content": "[CLS]", "lstrip": false, "normalized": true, "rstrip": false, "single_word": false}, "cls_token": {"__type": "AddedToken", "content": "[CLS]", "lstrip": false, "normalized": true, "rstrip": false, "single_word": false}, "eos_token": {"__type": "Add... | {
"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": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["GECToR_gotutiyan", "grammatical error correction"]} | Only non-commercial purposes.
# gector sample
This is an unofficial pretrained model of GECToR ([Omelianchuk+ 2020](https://aclanthology.org/2020.bea-1.16/)).
### How to use
The code is avaliable from https://github.com/gotutiyan/gector.
CLI
```sh
python predict.py --input <raw text file> --restore_dir gotutiyan... | null | null | null | [
"en"
] | 410,292,619 | null | null | [
"AutoModel"
] | [
null
] | null | null | null |
64f66afbb8cc49b41409c549 | gabrielkytz/finetuning-sentiment-model-3000-samples | gabrielkytz | {
"models": [
{
"_id": "622fea36174feb5439c2e4be",
"id": "cardiffnlp/twitter-roberta-base-sentiment-latest"
}
],
"relation": "finetune"
} | 31 | 671 | False | 2023-09-04T23:40:43Z | 2023-09-13T19:45:29Z | transformers | 1 | 1 | [{"name": "finetuning-sentiment-model-3000-samples", "results": []}] | text-classification | null | [
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"bpe.codes",
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"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"training_args.bin",
"vocab.json",
"vocab.txt"
] | 9c5c36267ffee2da0dc8c20f425eb92d5b90703a | [
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"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["RobertaForSequenceClassification"], "model_type": "roberta", "tokenizer_config": {"bos_token": {"__type": "AddedToken", "content": "<s>", "lstrip": false, "normalized": true, "rstrip": false, "single_word": false}, "cls_token": {"__type": "AddedToken", "content": "<s>", "lstrip": false, "normalized"... | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | {"base_model": "cardiffnlp/twitter-roberta-base-sentiment-latest", "datasets": null, "eval_results": [], "language": null, "library_name": null, "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": "finetuning-sentiment-model-3000-samples", "pipeline_tag": null, "tags": ["generate... | <!-- 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. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-sentiment-latest](... | null | null | null | null | null | null | null | [
"roberta",
"AutoModelForSequenceClassification",
"RobertaForSequenceClassification"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
64fd4022e0dc35986bd9d402 | microsoft/phi-1_5 | microsoft | null | 105,058 | 3,376,387 | False | 2023-09-10T04:03:46Z | 2025-11-24T16:58:09Z | transformers | 1,355 | 1 | null | text-generation | {"parameters": {"F16": 1418270720}, "total": 1418270720} | [
".gitattributes",
"CODE_OF_CONDUCT.md",
"LICENSE",
"NOTICE.md",
"README.md",
"SECURITY.md",
"added_tokens.json",
"config.json",
"data_summary_card.md",
"generation_config.json",
"merges.txt",
"model.safetensors",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vo... | 77aa61eeac94fbf33d492b9f2744c98b42d5b5eb | [
"transformers",
"safetensors",
"phi",
"text-generation",
"nlp",
"code",
"en",
"arxiv:2309.05463",
"license:mit",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["PhiForCausalLM"], "model_type": "phi", "tokenizer_config": {"bos_token": "<|endoftext|>", "eos_token": "<|endoftext|>", "unk_token": "<|endoftext|>"}} | {
"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": "https://huggingface.co/microsoft/phi-1_5/resolve/main/LICENSE", "metrics": null, "model_name": null, "pipeline_tag": "text-generation", "tags": ["nlp", "code"]} | ## Model Summary
The language model Phi-1.5 is a Transformer with **1.3 billion** parameters. It was trained using the same data sources as [phi-1](https://huggingface.co/microsoft/phi-1), augmented with a new data source that consists of various NLP synthetic texts. When assessed against benchmarks testing common sen... | null | [
"mit",
"https://huggingface.co/microsoft/phi-1_5/resolve/main/LICENSE"
] | null | [
"en"
] | 1,418,270,720 | null | null | [
"AutoModelForCausalLM",
"phi",
"PhiForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
64ff2c767a4a6ae49afa72b5 | 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} | [
".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... | a5beb1e3e68b9ab74eb54cfd186867f64f240e1a | [
"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-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"
] |
64fff58369219ce3e48e1a1a | BAAI/bge-large-zh-v1.5 | BAAI | null | 628,227 | 20,295,579 | False | 2023-09-12T05:22:11Z | 2024-04-02T14:00:04Z | sentence-transformers | 616 | 1 | null | feature-extraction | null | [
".gitattributes",
"1_Pooling/config.json",
"README.md",
"config.json",
"config_sentence_transformers.json",
"modules.json",
"pytorch_model.bin",
"sentence_bert_config.json",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | 79e7739b6ab944e86d6171e44d24c997fc1e0116 | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"zh",
"arxiv:2401.03462",
"arxiv:2312.15503",
"arxiv:2311.13534",
"arxiv:2310.07554",
"arxiv:2309.07597",
"license:mit",
"text-embeddings-inference",
"endpoints_compatible",
"deploy... | 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"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["zh"], "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"]} | <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 | [
"zh"
] | null | null | null | [
"BertModel",
"AutoModel",
"bert"
] | [
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
650015a612c1442d9930ad41 | BAAI/bge-reranker-large | BAAI | null | 813,841 | 22,878,119 | False | 2023-09-12T07:39:18Z | 2024-05-11T13:39:02Z | transformers | 454 | 1 | [{"name": "bge-reranker-base", "results": [{"task": {"type": "Reranking"}, "dataset": {"type": "C-MTEB/CMedQAv1-reranking", "name": "MTEB CMedQAv1", "config": "default", "split": "test", "revision": "None"}, "metrics": [{"type": "map", "value": 81.27206722525007, "verified": false}, {"type": "mrr", "value": 84.14238095... | feature-extraction | {"parameters": {"I64": 514, "F32": 559891457}, "total": 559891971} | [
".gitattributes",
"README.md",
"config.json",
"model.safetensors",
"onnx/model.onnx",
"onnx/model.onnx_data",
"pytorch_model.bin",
"sentencepiece.bpe.model",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json"
] | 55611d7bca2a7133960a6d3b71e083071bbfc312 | [
"transformers",
"pytorch",
"onnx",
"safetensors",
"xlm-roberta",
"text-classification",
"mteb",
"feature-extraction",
"en",
"zh",
"arxiv:2401.03462",
"arxiv:2312.15503",
"arxiv:2311.13534",
"arxiv:2310.07554",
"arxiv:2309.07597",
"license:mit",
"model-index",
"text-embeddings-infer... | null | {"architectures": ["XLMRobertaForSequenceClassification"], "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": "... | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | {"language": ["en", "zh"], "license": "mit", "pipeline_tag": "feature-extraction", "tags": ["mteb"], "model-index": [{"name": "bge-reranker-base", "results": [{"task": {"type": "Reranking"}, "dataset": {"name": "MTEB CMedQAv1", "type": "C-MTEB/CMedQAv1-reranking", "config": "default", "split": "test", "revision": "None... | **We have updated the [new reranker](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_reranker), supporting larger lengths, more languages, and achieving better performance.**
<h1 align="center">FlagEmbedding</h1>
<h4 align="center">
<p>
<a href=#model-list>Model List</a> |
... | null | [
"mit"
] | null | [
"en",
"zh"
] | 559,891,971 | null | null | [
"AutoModelForSequenceClassification",
"XLMRobertaForSequenceClassification",
"xlm-roberta"
] | [
"feature-extraction",
"text-classification"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
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