KnowLine-4B-Gen5.5
PelaAI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
PII-Tracer
perplexity-aiPII masking model for conversational data. A ~600M-parameter bidirectional Qwen3 encoder (`perplexity-ai/pplx-embed-v1-0.6b` backbone, run encoder-only via `is_causal=false`) with two heads: `predict` and `mask` wrap `model(input_ids, attention_mask)`, which returns the 37 BIOES tag logits per token and one sensitivity logit per document. The implementation is `modeling_pii_masking.py` in this repo: it builds the encoder with the standard Transformers `Qwen3Model` run bidirectionally via `config.is_causal = False` (hence `transformers>=5.2`, which passes `is_causal` through to the attention backend), applies this repo's fine-tuned weights and the two heads, and decodes spans with the constrained BIOES Viterbi included in the file. Input is truncated to `max_seq_len` (4096) tokens; chunk longer documents before calling `predict`. `model.save_pretrained(dir)` also saves the tokenizer, so the saved directory loads and predicts on its own. `model.safetensors` holds the fine-tuned backbone (bf16, `backbone.*`), both heads (fp32, `token_cls_head.*` / `sensitivity_head.*`), and the Viterbi bias scalars (`viterbi.*`). `max_seq_len` is 4096 tokens.
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View details ↗PII-Tracer-GGUF
perplexity-aiThis repository provides PII-Tracer, the detector introduced in PII-TRACE. GGUF packaging of `perplexity-ai/PII-Tracer`, a PII masking model for conversational data. The model uses a ~600M-parameter bidirectional Qwen3 encoder with a `perplexity-ai/pplx-embed-v1-0.6b` backbone and two heads: - **Token classification head** (1024 → 37): BIOES tags over 9 PII categories (`private_person`, `account_number`, `private_url`, `private_date`, `private_address`, `private_email`, `private_phone`, `other_pii`, `secret`), decoded with a constrained Viterbi decoder. - **Sensitivity head** (1024 → 1): conversation-level sensitivity classifier on mean-pooled hidden states.
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View details ↗PII-Tracer-MLX
perplexity-aiMLX weights for PII-Tracer, a PII masking model for conversational data. The model uses a ~600M-parameter bidirectional Qwen3 encoder with a `perplexity-ai/pplx-embed-v1-0.6b` backbone and two heads: The backbone's linear projections use **8-bit affine quantization with group size 64**. Embeddings and normalization weights remain bf16; both heads remain fp32. The weights file is approximately 779 MB. Quantized projections retain the `backbone.*` key names and store packed `uint32` `weight` tensors with bf16 `scales` and `biases`. The unquantized heads use `token_cls_head.*` and `sensitivity_head.*`.
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View details ↗pplx-decider-flash-gguf-v1.2
perplexity-aipplx-decider-flash-v1.2 is the small version of pplx-decider-v1.2: the same joint request format, the same API and the same text and image support, at about one seventh of the size. It is built on Qwen3.5-4B, **distilled from the 27B decision models** on their training requests (the student learns the teachers' full probability distributions, not just their answers). It combines several training runs through model souping (weight averaging). Use it when latency or cost matters more than the last points of accuracy. On our evaluations it keeps most of the 27B model's Decision Index and vision scores (see below). Distillation also improves results on decision tasks held out from all training. Stock `llama-server` does not implement this checkpoint's protocol v3; its `/v1/systemone` uses protocol v1. A compatible runtime must:
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View details ↗pplx-decider-flash-v1.2
perplexity-ai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 ↗pplx-decider-v1.1-27b
perplexity-aipplx-decider-v1.1-27b is an update of our pplx-decider-v1-27b model, that boost the Decision Index score from 56.4 to 61.56, now outperforming Jev by more than 3.5 points, while using the same backbone. Most of the gains are driven by the lifting of the causal mask and training on more data, including data from tasksource. This is the **native decision-checkpoint layout**: a `Qwen3_5Model` backbone plus `readout.safetensors` containing a BF16 `[255, 5120]` decision head. The Transformers class name does not change the base model identity, Qwen3.8-27B. Use Python 3.12+, authenticated Hugging Face access to this private repository, and a CUDA GPU with room for roughly 49 GiB of weights plus working memory. Download the repository and install its pinned `requirements.txt` dependencies.
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View details ↗pplx-decider-v1.2
perplexity-ai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 ↗pplx-decider-v1-27b
perplexity-aiAccuracy across 11 benchmarks. The pplx-decider-v1-27b results were measured through the Perplexity API. uv installs the dependencies; the script downloads the model from Hugging Face. In an environment with these dependencies installed, use `Decider` directly: Use `{"type": "noul", "instructions": "Does this message express urgency?"}` for a yes/no probability. For images, pass `images=["screenshot.png"]` to `predict`, or run:
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View details ↗pplx-pii-masking-vllm
perplexity-ai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 ↗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-imdb-model
philipobiorah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 ↗programming-language-identification
philomath-1209This Model is a fine-tuned version of *huggingface/CodeBERTa-small-v1* on *cakiki/rosetta-code* Dataset for 26 Programming Languages as mentioned below. Model is trained for 25 epochs on Azure for nearly 26000 Datapoints for above Mentioned 26 Programming Languages extracted from Dataset having 1006 of total Programming Language. 'ARM Assembly': 'AppleScript' 'C' 'C#' 'C++' 'COBOL' 'Erlang' 'Fortran' 'Go' 'Java' 'JavaScript' 'Kotlin' 'Lua 'Mathematica/Wolfram Language' 'PHP' 'Pascal' 'Perl' 'PowerShell' 'Python' 'R 'Ruby' 'Rust' 'Scala' 'Swift' 'Visual Basic .NET' 'jq'
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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 ↗distilbert-onnx-banking77
philschmid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 ↗distilroberta-base-ner-conll2003
philschmid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 ↗MiniLM-L6-H384-uncased-sst2
philschmid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 ↗tiny-bert-sst2-distilled
philschmidThis 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: 1.7305 - Accuracy: 0.8326 The following hyperparameters were used during training: - learning_rate: 0.0007199555649276667 - train_batch_size: 1024 - eval_batch_size: 1024 - seed: 33 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 7 - mixed_precision_training: Native AMP
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View details ↗Phocinae-Largha-150M-v1
Phocinae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 ↗ner-bert-base-cased-pt-lenerbr
pierreguillou**ner-bert-base-portuguese-cased-lenerbr** is a NER model (token classification) in the legal domain in Portuguese that was finetuned on 20/12/2021 in Google Colab from the model pierreguillou/bert-base-cased-pt-lenerbr on the dataset LeNER_br by using a NER objective. Due to the small size of BERTimbau base and finetuning dataset, the model overfitted before to reach the end of training. Here are the overall final metrics on the validation dataset (*note: see the paragraph "Validation metrics by Named Entity" to get detailed metrics*): - **f1**: 0.8926146010186757 - **precision**: 0.8810222036028488 - **recall**: 0.9045161290322581 - **accuracy**: 0.9759397808828684 - **loss**: 0.18803243339061737 **Note**: the model pierreguillou/bert-base-cased-pt-lenerbr is a language model that was created through the finetuning of the model BERTimbau base on the dataset LeNER-Br language modeling by using a MASK objective. This first specialization of the language model before finetuning on the NER task improved a bit the model quality. To prove it, here are the results of the NER model finetuned from the model BERTimbau base (a non-specialized language model): - **f1**: 0.8716487228203504 - **precision**: 0.8559286898839138 - **recall**: 0.8879569892473118 - **accuracy**: 0.9755893153732458 - **loss**: 0.1133928969502449
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View details ↗ner-bert-large-cased-pt-lenerbr
pierreguillou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 ↗senda
pin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 ↗Winnow-12B-GGUF
Piotr1215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 ↗gliner2-polish-pii
piotrmaciejbednarski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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