Instructions to use pooria/foxmind-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers.js
How to use pooria/foxmind-small with Transformers.js:
// ⚠️ Unknown pipeline tag
- GLiNER2
How to use pooria/foxmind-small with GLiNER2:
from gliner2 import AutoExtractor extractor = AutoExtractor.from_pretrained("pooria/foxmind-small") # 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: 1,278 Bytes
56373cc | 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 | {
"attention_probs_dropout_prob": 0.1,
"bos_token_id": null,
"dtype": "float16",
"eos_token_id": null,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 384,
"initializer_range": 0.02,
"intermediate_size": 1536,
"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": 6,
"num_hidden_layers": 12,
"pad_token_id": 0,
"pooler_dropout": 0.0,
"pooler_hidden_act": "gelu",
"pooler_hidden_size": 384,
"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": 128011,
"gliner2": {
"source": "fastino/gliner2.5-small-v1",
"revision": "7132dc4561c3f94563c6147e75ffa8ef34c4964a",
"architecture": "boundary",
"pool_size": 192,
"pair_temperature": 1.0,
"abstention_threshold": 0.5,
"overlap_policy": "flat",
"inputs": "input_ids, attention_mask, word_positions, schema_positions",
"outputs": "cls_logits [1,L], span_probs [1,L,C], span_bounds [1,C,2]"
}
} |