Instructions to use wt3639/NER_skill_extractor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use wt3639/NER_skill_extractor with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="wt3639/NER_skill_extractor")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("wt3639/NER_skill_extractor") model = AutoModelForTokenClassification.from_pretrained("wt3639/NER_skill_extractor", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from wt3639/NER_skill_extractor: direct link, hf CLI and curl.
- Browser
- Download file 806 Bytes
-
https://huggingface.co/wt3639/NER_skill_extractor/resolve/main/config.json
- Command line
-
hf download hf://wt3639/NER_skill_extractor/config.json
-
curl -L -o config.json https://huggingface.co/wt3639/NER_skill_extractor/resolve/main/config.json
806 Bytes
| { | |
| "_name_or_path": "/hpcwork/vg380347/EduGBERT/encoder_model", | |
| "architectures": [ | |
| "BertForTokenClassification" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "classifier_dropout": null, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 1024, | |
| "id2label": { | |
| "0": "O", | |
| "1": "B-SKILL", | |
| "2": "I-SKILL" | |
| }, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 4096, | |
| "label2id": { | |
| "B-SKILL": 1, | |
| "I-SKILL": 2, | |
| "O": 0 | |
| }, | |
| "layer_norm_eps": 1e-12, | |
| "max_position_embeddings": 512, | |
| "model_type": "bert", | |
| "num_attention_heads": 16, | |
| "num_hidden_layers": 24, | |
| "pad_token_id": 0, | |
| "position_embedding_type": "absolute", | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.34.1", | |
| "type_vocab_size": 2, | |
| "use_cache": true, | |
| "vocab_size": 31102 | |
| } | |