Instructions to use pooria/foxmind with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers.js
How to use pooria/foxmind with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('token-classification', 'pooria/foxmind'); - GLiNER2
How to use pooria/foxmind with GLiNER2:
from gliner2 import AutoExtractor extractor = AutoExtractor.from_pretrained("pooria/foxmind") # Extract entities text = "Apple CEO Tim Cook announced iPhone 15 in Cupertino yesterday." result = extractor.extract_entities(text, ["company", "person", "product", "location"]) print(result) - Notebooks
- Google Colab
- Kaggle
File size: 1,299 Bytes
14b1e81 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 | {
"_attn_implementation_autoset": true,
"attention_probs_dropout_prob": 0.1,
"bos_token_id": null,
"dtype": "float32",
"eos_token_id": null,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"initializer_range": 0.02,
"intermediate_size": 3072,
"layer_norm_eps": 1e-07,
"legacy": true,
"max_position_embeddings": 512,
"max_relative_positions": -1,
"model_type": "deberta-v2",
"norm_rel_ebd": "layer_norm",
"num_attention_heads": 12,
"num_hidden_layers": 12,
"pad_token_id": 0,
"pooler_dropout": 0,
"pooler_hidden_act": "gelu",
"pooler_hidden_size": 768,
"pos_att_type": [
"p2c",
"c2p"
],
"position_biased_input": false,
"position_buckets": 256,
"relative_attention": true,
"share_att_key": true,
"tie_word_embeddings": true,
"transformers_version": "5.19.0",
"type_vocab_size": 0,
"vocab_size": 250112,
"gliner2": {
"source": "fastino/gliner2-multi-v1",
"max_width": 8,
"max_count": 20,
"inputs": "input_ids, attention_mask, word_positions, schema_positions",
"outputs": "cls_logits [B,L], count_logits [B,20], span_logits [B,L,T,W]"
},
"transformers.js_config": {
"use_external_data_format": {
"model.onnx": 1,
"model_fp16.onnx": 1
},
"dtype": "fp16"
}
} |