Token Classification
Transformers
PyTorch
English
roberta
keyphrase-extraction
Eval Results (legacy)
Instructions to use ml6team/keyphrase-extraction-kbir-inspec with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ml6team/keyphrase-extraction-kbir-inspec with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="ml6team/keyphrase-extraction-kbir-inspec")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("ml6team/keyphrase-extraction-kbir-inspec") model = AutoModelForTokenClassification.from_pretrained("ml6team/keyphrase-extraction-kbir-inspec", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
File size: 855 Bytes
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"_name_or_path": "bloomberg/KBIR",
"architectures": [
"RobertaForTokenClassification"
],
"attention_probs_dropout_prob": 0.1,
"bos_token_id": 0,
"classifier_dropout": null,
"eos_token_id": 2,
"gradient_checkpointing": false,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 1024,
"id2label": {
"0": "B-KEY",
"1": "I-KEY",
"2": "O"
},
"initializer_range": 0.02,
"intermediate_size": 4096,
"label2id": {
"B-KEY": 0,
"I-KEY": 1,
"O": 2
},
"layer_norm_eps": 1e-05,
"max_position_embeddings": 514,
"model_type": "roberta",
"num_attention_heads": 16,
"num_hidden_layers": 24,
"pad_token_id": 1,
"position_embedding_type": "absolute",
"torch_dtype": "float32",
"transformers_version": "4.17.0",
"type_vocab_size": 1,
"use_cache": true,
"vocab_size": 50265
}
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