Instructions to use ArBert/albert-base-v2-finetuned-ner-gmm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ArBert/albert-base-v2-finetuned-ner-gmm with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="ArBert/albert-base-v2-finetuned-ner-gmm")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("ArBert/albert-base-v2-finetuned-ner-gmm") model = AutoModelForTokenClassification.from_pretrained("ArBert/albert-base-v2-finetuned-ner-gmm", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from ArBert/albert-base-v2-finetuned-ner-gmm: direct link, hf CLI and curl.
- Browser
- Download file 467 Bytes
-
https://huggingface.co/ArBert/albert-base-v2-finetuned-ner-gmm/resolve/refs%2Fpr%2F1/tokenizer_config.json
- Command line
-
hf download hf://ArBert/albert-base-v2-finetuned-ner-gmm@refs/pr/1/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/ArBert/albert-base-v2-finetuned-ner-gmm/resolve/refs%2Fpr%2F1/tokenizer_config.json
467 Bytes
| {"do_lower_case": true, "remove_space": true, "keep_accents": false, "bos_token": "[CLS]", "eos_token": "[SEP]", "unk_token": "<unk>", "sep_token": "[SEP]", "pad_token": "<pad>", "cls_token": "[CLS]", "mask_token": {"content": "[MASK]", "single_word": false, "lstrip": true, "rstrip": false, "normalized": false, "__type": "AddedToken"}, "model_max_length": 512, "special_tokens_map_file": null, "name_or_path": "albert-base-v2", "tokenizer_class": "AlbertTokenizer"} |