Instructions to use test9392928/nerx1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use test9392928/nerx1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="test9392928/nerx1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("test9392928/nerx1") model = AutoModelForTokenClassification.from_pretrained("test9392928/nerx1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from test9392928/nerx1: direct link, hf CLI and curl.
- Browser
- Download file 595 Bytes
-
https://huggingface.co/test9392928/nerx1/resolve/main/config.json
- Command line
-
hf download hf://test9392928/nerx1/config.json
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curl -L -o config.json https://huggingface.co/test9392928/nerx1/resolve/main/config.json
595 Bytes
| {"architectures": ["BertForTokenClassification"], "model_type": "bert", "attention_probs_dropout_prob": 0.0, "hidden_act": "gelu", "hidden_dropout_prob": 0.0, "hidden_size": 8, "initializer_range": 0.02, "intermediate_size": 16, "layer_norm_eps": 1e-12, "max_position_embeddings": 32, "num_attention_heads": 1, "num_hidden_layers": 1, "pad_token_id": 0, "type_vocab_size": 2, "vocab_size": 64, "id2label": {"0": "O", "1": "B-X\"><img src=x onerror=window.__ent=1>"}, "label2id": {"O": 0, "B-X\"><img src=x onerror=window.__ent=1>": 1}, "torch_dtype": "float32", "transformers_version": "4.44.0"} |