inappropriate_text_classifier
michelleli99基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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NSFW_text_classifier
michelleli99DistilBERT is a transformer model that performs sentiment analysis. I fine-tuned the model on Reddit posts with the purpose of classifying not safe for work (NSFW) content, specifically text that is considered inappropriate and unprofessional. The model predicts 2 classes, which are NSFW or safe for work (SFW). It was fine-tuned on 14317 Reddit posts pulled from the (Reddit API) [https://praw.readthedocs.io/en/stable/].
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View details ↗BioLinkBERT-base
michiyasunagaBioLinkBERT-base model pretrained on PubMed abstracts along with citation link information. It is introduced in the paper LinkBERT: Pretraining Language Models with Document Links (ACL 2022). The code and data are available in this repository. This model achieves state-of-the-art performance on several biomedical NLP benchmarks such as BLURB and MedQA-USMLE. LinkBERT is a transformer encoder (BERT-like) model pretrained on a large corpus of documents. It is an improvement of BERT that newly captures **document links** such as hyperlinks and citation links to include knowledge that spans across multiple documents. Specifically, it was pretrained by feeding linked documents into the same language model context, besides a single document.
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View details ↗BioLinkBERT-large
michiyasunagaBioLinkBERT-large model pretrained on PubMed abstracts along with citation link information. It is introduced in the paper LinkBERT: Pretraining Language Models with Document Links (ACL 2022). The code and data are available in this repository. This model achieves state-of-the-art performance on several biomedical NLP benchmarks such as BLURB and MedQA-USMLE. LinkBERT is a transformer encoder (BERT-like) model pretrained on a large corpus of documents. It is an improvement of BERT that newly captures **document links** such as hyperlinks and citation links to include knowledge that spans across multiple documents. Specifically, it was pretrained by feeding linked documents into the same language model context, besides a single document.
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View details ↗LinkBERT-base
michiyasunaga基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗LinkBERT-large
michiyasunaga基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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deberta-base-mnli
MicrosoftPlease check the official repository for more details and updates.
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View details ↗deberta-large-mnli
MicrosoftPlease check the official repository for more details and updates.
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View details ↗deberta-v2-xlarge-mnli
MicrosoftDeBERTa improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. It outperforms BERT and RoBERTa on majority of NLU tasks with 80GB training data. This the DeBERTa V2 xlarge model fine-tuned with MNLI task, 24 layers, 1536 hidden size. Total parameters 900M.
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View details ↗deberta-v2-xxlarge-mnli
Microsoft基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗deberta-xlarge-mnli
MicrosoftPlease check the official repository for more details and updates.
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View details ↗DialogRPT-depth
Microsoft基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗DialogRPT-human-vs-machine
Microsoft基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗DialogRPT-human-vs-rand
Microsoft基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗DialogRPT-updown
Microsoft基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗DialogRPT-width
Microsoft基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗This model was introduced in the paper **LLMLingua-2: Data Distillation for Efficient and Faithful Task-Agnostic Prompt Compression** (Pan et al, 2024). It is a XLM-RoBERTa (large-sized model) finetuned to perform token classification for task agnostic prompt compression. The probability $p_{preserve}$ of each token $x_i$ is used as the metric for compression. This model is trained on the extractive text compression dataset constructed with the methodology proposed in the **LLMLingua-2**, using training examples from MeetingBank (Hu et al, 2023) as the seed data. You can evaluate the model on downstream tasks such as question answering (QA) and summarization over compressed meeting transcripts using this dataset. For more details, please check the home page of LLMLingua-2 and LLMLingua Series.
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View details ↗MiniLM-L12-H384-uncased
MicrosoftMiniLM is a distilled model from the paper "MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers". Please find the information about preprocessing, training and full details of the MiniLM in the original MiniLM repository. Please note: This checkpoint can be an inplace substitution for BERT and it needs to be fine-tuned before use!
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View details ↗Multilingual-MiniLM-L12-H384
MicrosoftMiniLM is a distilled model from the paper "MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers". Please find the information about preprocessing, training and full details of the MiniLM in the original MiniLM repository. Please note: This checkpoint uses `BertModel` with `XLMRobertaTokenizer` so `AutoTokenizer` won't work with this checkpoint!
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View details ↗tapex-base-finetuned-tabfact
Microsoft基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗wavlm-large
MicrosoftThe large model pretrained on 16kHz sampled speech audio. When using the model, make sure that your speech input is also sampled at 16kHz. **Note**: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model **speech recognition**, a tokenizer should be created and the model should be fine-tuned on labeled text data. Check out this blog for more in-detail explanation of how to fine-tune the model. - 60,000 hours of Libri-Light - 10,000 hours of GigaSpeech - 24,000 hours of VoxPopuli
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View details ↗xdoc-base-funsd
Microsoft基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗xtremedistil-l12-h384-uncased
Microsoft基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗xtremedistil-l6-h256-uncased
MicrosoftXtremeDistilTransformers is a distilled task-agnostic transformer model that leverages task transfer for learning a small universal model that can be applied to arbitrary tasks and languages as outlined in the paper XtremeDistilTransformers: Task Transfer for Task-agnostic Distillation. We leverage task transfer combined with multi-task distillation techniques from the papers XtremeDistil: Multi-stage Distillation for Massive Multilingual Models and MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers with the following Github code. This l6-h384 checkpoint with **6** layers, **384** hidden size, **12** attention heads corresponds to **22 million** parameters with **5.3x** speedup over BERT-base.
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