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**⚠️ Disclaimer:** The huggingface models currently give different results to the detoxify library (see issue here). For the most up to date models we recommend using the models from https://github.com/unitaryai/detoxify Trained models & code to predict toxic comments on 3 Jigsaw challenges: Toxic comment classification, Unintended Bias in Toxic comments, Multilingual toxic comment classification. Built by Laura Hanu at Unitary, where we are working to stop harmful content online by interpreting visual content in context.

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toxic-bert

unitary

**⚠️ Disclaimer:** The huggingface models currently give different results to the detoxify library (see issue here). For the most up to date models we recommend using the models from https://github.com/unitaryai/detoxify Trained models & code to predict toxic comments on 3 Jigsaw challenges: Toxic comment classification, Unintended Bias in Toxic comments, Multilingual toxic comment classification. Built by Laura Hanu at Unitary, where we are working to stop harmful content online by interpreting visual content in context.

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**⚠️ Disclaimer:** The huggingface models currently give different results to the detoxify library (see issue here). For the most up to date models we recommend using the models from https://github.com/unitaryai/detoxify Trained models & code to predict toxic comments on 3 Jigsaw challenges: Toxic comment classification, Unintended Bias in Toxic comments, Multilingual toxic comment classification. Built by Laura Hanu at Unitary, where we are working to stop harmful content online by interpreting visual content in context.

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unslothai

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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repeat

unslothai

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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GLiNER is a Named Entity Recognition (NER) model capable of identifying any entity type using a bidirectional transformer encoder (BERT-like). It provides a practical alternative to traditional NER models, which are limited to predefined entities, and Large Language Models (LLMs) that, despite their flexibility, are costly and large for resource-constrained scenarios. This version has been trained on the **NuNER** dataset (commercially permissive). * Paper: https://arxiv.org/abs/2311.08526 * Repository: https://github.com/urchade/GLiNER

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GLiNER is a Named Entity Recognition (NER) model capable of identifying any entity type using a bidirectional transformer encoder (BERT-like). It provides a practical alternative to traditional NER models, which are limited to predefined entities, and Large Language Models (LLMs) that, despite their flexibility, are costly and large for resource-constrained scenarios. Once you've downloaded the GLiNER library, you can import the GLiNER class. You can then load this model using `GLiNER.from_pretrained` and predict entities with `predict_entities`.

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GLiNER is a Named Entity Recognition (NER) model capable of identifying any entity type using a bidirectional transformer encoder (BERT-like). It provides a practical alternative to traditional NER models, which are limited to predefined entities, and Large Language Models (LLMs) that, despite their flexibility, are costly and large for resource-constrained scenarios. * Paper: https://arxiv.org/abs/2311.08526 * Repository: https://github.com/urchade/GLiNER Once you've downloaded the GLiNER library, you can import the GLiNER class. You can then load this model using `GLiNER.from_pretrained` and predict entities with `predict_entities`.

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GLiNER is a Named Entity Recognition (NER) model capable of identifying any entity type using a bidirectional transformer encoder (BERT-like). It provides a practical alternative to traditional NER models, which are limited to predefined entities, and Large Language Models (LLMs) that, despite their flexibility, are costly and large for resource-constrained scenarios. This version has been trained on the **Pile-NER** dataset (Research purpose). Commercially permission versions are available (**urchade/gliner_smallv2**, **urchade/gliner_mediumv2**, **urchade/gliner_largev2**) Once you've downloaded the GLiNER library, you can import the GLiNER class. You can then load this model using `GLiNER.from_pretrained` and predict entities with `predict_entities`.

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GLiNER is a Named Entity Recognition (NER) model capable of identifying any entity type using a bidirectional transformer encoder (BERT-like). It provides a practical alternative to traditional NER models, which are limited to predefined entities, and Large Language Models (LLMs) that, despite their flexibility, are costly and large for resource-constrained scenarios. This model has been trained by fine-tuning `urchade/gliner_multi-v2.1` on the `urchade/synthetic-pii-ner-mistral-v1` dataset. This model is capable of recognizing various types of *personally identifiable information* (PII), including but not limited to these entity types: `person`, `organization`, `phone number`, `address`, `passport number`, `email`, `credit card number`, `social security number`, `health insurance id number`, `date of birth`, `mobile phone number`, `bank account number`, `medication`, `cpf`, `driver's license number`, `tax identification number`, `medical condition`, `identity card number`, `national id number`, `ip address`, `email address`, `iban`, `credit card expiration date`, `username`, `health insurance number`, `registration number`, `student id number`, `insurance number`, `flight number`, `landline phone number`, `blood type`, `cvv`, `reservation number`, `digital signature`, `social media handle`, `license plate number`, `cnpj`, `postal code`, `passport_number`, `serial number`, `vehicle registration number`, `credit card brand`, `fax number`, `visa number`, `insurance company`, `identity document number`, `transaction number`, `national health insurance number`, `cvc`, `birth certificate number`, `train ticket number`, `passport expiration date`, and `social_security_number`.

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GLiNER is a Named Entity Recognition (NER) model capable of identifying any entity type using a bidirectional transformer encoder (BERT-like). It provides a practical alternative to traditional NER models, which are limited to predefined entities, and Large Language Models (LLMs) that, despite their flexibility, are costly and large for resource-constrained scenarios. * Paper: https://arxiv.org/abs/2311.08526 * Repository: https://github.com/urchade/GLiNER Once you've downloaded the GLiNER library, you can import the GLiNER class. You can then load this model using `GLiNER.from_pretrained` and predict entities with `predict_entities`.

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GLiNER is a Named Entity Recognition (NER) model capable of identifying any entity type using a bidirectional transformer encoder (BERT-like). It provides a practical alternative to traditional NER models, which are limited to predefined entities, and Large Language Models (LLMs) that, despite their flexibility, are costly and large for resource-constrained scenarios. * Paper: https://arxiv.org/abs/2311.08526 * Repository: https://github.com/urchade/GLiNER Once you've downloaded the GLiNER library, you can import the GLiNER class. You can then load this model using `GLiNER.from_pretrained` and predict entities with `predict_entities`.

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This model is based on distilroberta-base pretrained weights, with a classification head fine-tuned to classify text into 2 categories (neutral, biased). The dataset used to fine-tune the model is wikirev-bias, extracted from English wikipedia revisions, see https://github.com/rpryzant/neutralizing-bias for details on the WNC wiki edits corpus.

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👉 **Launch Interactive Demo** - No setup required, runs in your browser. Paste a source text and an LLM response to get a verdict, a 0-1 consistency score, and per-sentence scores. 💡 **Quick test**: Try "The capital of France is Berlin" as the source and "The capital of France is Paris" as the response to see HHEM detect this factual but hallucinated case. HHEM-2.1-Open is the latest open source version of Vectara's HHEM series models for detecting hallucinations in LLMs. These are particularly useful in the context of building retrieval-augmented-generation (RAG) applications or Agentic workflows, where a set of facts is summarized by an LLM, and HHEM can be used to measure the extent to which this summary is factually consistent with the facts.

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月間トークン数を調整してAPI費用を比較。

1 M = 100万トークン・米ドル
同一チャネルの基本テキスト料金を比較

収集済みの比較可能な100万トークン当たり米ドル料金です。キャッシュ割引、段階料金、追加推論出力、税金は含みません。最新料金は出典で確認してください。

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