Decision-WAF-V1-0.1B
0xBrandon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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Explore AI models 2428
GLiNER2.5-Decide-GGUF
3ntr0py-t4m3r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 ↗roberta-large-NER
51la51. Model Details 2. Uses 3. Bias, Risks, and Limitations 4. Training 5. Evaluation 6. Environmental Impact 7. Technical Specifications 8. Citation 9. Model Card Authors 10. How To Get Started With the Model The XLM-RoBERTa model was proposed in Unsupervised Cross-lingual Representation Learning at Scale by Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov. It is based on Facebook's RoBERTa model released in 2019. It is a large multi-lingual language model, trained on 2.5TB of filtered CommonCrawl data. This model is XLM-RoBERTa-large fine-tuned with the conll2003 dataset in English. The model is a language model. The model can be used for token classification, a natural language understanding task in which a label is assigned to some tokens in a text.
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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 ↗email-phishing-distilbert-finetuned
aamoshdahal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 ↗ModernBERT-base-doc_en-Cefr
AbdullahBarayan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 ↗ModernBERT-base-reference_AllLang2-Cefr2
AbdullahBarayan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 ↗English_Grammar_Checker
abdulmatinomotosoThis model is a fine-tuned version of bert-base-uncased on the glue dataset. It achieves the following results on the evaluation set: - Loss: 1.1117 - Matthews Correlation: 0.5324 The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5
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View details ↗jev-control-core
abhishek085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 ↗spark-s1-4b-v6-GGUF
abhishek085GGUF builds of `abhishek085/spark-s1-4b-v6` for **llama.cpp**, **Ollama**, LM Studio, Jan and other GGUF runtimes. Part of Open Spark Jev, an open-source project of the Nokast AI community. > **This is not a chat model.** `spark-s1` is a System-1 *decision* model: given a state and a question with a fixed menu of options, the > answer is the probability distribution over the option letters at the **first** generated token. Chatting with it (`ollama run ...`) only > ever produces a single letter. Use it through the logprob recipe below or through the Open Spark Jev gateway. Measured on 200 shuffled rows of the project's locked held-out split (`v5_test_locked`), llama.cpp `llama-server` (CUDA, build `38a5b42`), `/v1/completions` with `top_logprobs=20`, calibrated temperature applied client-side. Ollama 0.40.2 with the Q8_0 file gave the same 99.5% / 0.900. This is a parity check against the HF model, not a new benchmark; the model's own evaluation is on the base model card.
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View details ↗spark-s1-4b-v8-GGUF
abhishek085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 ↗sentiment-binary-dicoding
abilfad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 ↗Shieldstral-1.0-3B-GGUF
Abiray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 ↗persian-ezafe-albert
abreza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 ↗jobbert-ner-sonnet-v2
AchrafSoltani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 ↗tinybert-emotion-balanced
AdamCoddThis model is a fine-tuned version of bert-tiny on the emotion balanced dataset. It achieves the following results on the evaluation set: - Loss: 0.1809 - Accuracy: 0.9354 TinyBERT is 7.5 times smaller and 9.4 times faster on inference compared to its teacher BERT model (while DistilBERT is 40% smaller and 1.6 times faster than BERT). The model has been trained on 89_754 examples split into train, validation and test. Each label was perfectly balanced in each split. This model is not as accurate as the distilbert-emotion-balanced one because the focus was on speed, which can lead to misinterpretation of complex sentences. Despite this, its performance is quite good and should be more than sufficient for most use cases.
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View details ↗bert-base-uncased_sts_mrpc_houlsby
AdapterHub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-base-uncased_sts_mrpc_pfeiffer
AdapterHub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 ↗ai-detector
adaptive-classifier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 ↗business-sentiment
adaptive-classifier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 ↗compliance-classification
adaptive-classifier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 ↗content-moderation
adaptive-classifier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 ↗customer-intent
adaptive-classifier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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