Julia-1-GGUF
ggml-orgThis is a decision model, to be used via `/v1/systemone` API. See https://github.com/ggml-org/llama.cpp/pull/29818
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Kev-0.8B-GGUF
ggml-orgThis is a decision model, to be used via `/v1/systemone` API. See https://github.com/ggml-org/llama.cpp/pull/29818
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View details ↗Kev-4B-GGUF
ggml-orgThis is a decision model, to be used via `/v1/systemone` API. See https://github.com/ggml-org/llama.cpp/pull/29818 - https://huggingface.co/jaredpalmer/kev-4b - https://huggingface.co/Qwen/Qwen3.5-4B-Base > [!IMPORTANT] > This model is automatically converted using https://github.com/ggml-org/convert
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View details ↗Kev-9B-GGUF
ggml-orgThis is a decision model, to be used via `/v1/systemone` API. See https://github.com/ggml-org/llama.cpp/pull/29818
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View details ↗Laya-GGUF
ggml-orgThis is a decision model, to be used via `/v1/systemone` API. See https://github.com/ggml-org/llama.cpp/pull/29818 > [!IMPORTANT] > This model is automatically converted using https://github.com/ggml-org/convert
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View details ↗BC4CHEMD-WLT-512-BioBERT-latest
ghadeermobasher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 ↗BC5CDR-Chemical-Disease-balanced-biobert-base-cased-v1.2
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View details ↗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 ↗BC5CDR-Chemical-Disease-balanced-pubmedbert
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View details ↗BC5CDR-Chemical-Disease-balanced-scibert_scivocab_cased
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View details ↗CRAFT-Original-BioBERT-512
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View details ↗Original-BioBERT-BC2GM
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View details ↗bert-base-NER-Russian
Gherman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 ↗The *french-camembert-postag-model* is a part of speech tagging model for French that was trained on the *free-french-treebank* dataset available on github. The base tokenizer and model used for training is *'camembert-base'*. http://alpage.inria.fr/statgram/frdep/Publications/crabbecandi-taln2008-final.pdf The usage of this model follows the common transformers patterns. Here is a short example of its usage:
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View details ↗phibert-finetuned-ner
girinlp-i2i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 ↗gliner_large-v2.5
gliner-community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 ↗gliner_medium-v2.5
gliner-community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 ↗gliner_small-v2.5
gliner-communityGLiNER 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`. The model authors are: * Urchade Zaratiana * Ihor Stepanov * Nadi Tomeh * Pierre Holat * Thierry Charnois
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View details ↗deberta-v3-japanese-base
globis-university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 ↗BERT-tiny-sst2
gokulsThis model is a fine-tuned version of google/bert_uncased_L-2_H-128_A-2 on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.4422 - Accuracy: 0.8372 The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 33 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 - mixed_precision_training: Native AMP
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View details ↗spam-mail-classifier
Goodmotion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 ↗
embeddinggemma-300m
Google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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