Instructions to use attacker8324/tc271 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use attacker8324/tc271 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="attacker8324/tc271")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("attacker8324/tc271") model = AutoModelForTokenClassification.from_pretrained("attacker8324/tc271", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from attacker8324/tc271: direct link, hf CLI and curl.
- Browser
- Download file 883 Bytes
-
https://huggingface.co/attacker8324/tc271/resolve/main/config.json
- Command line
-
hf download hf://attacker8324/tc271/config.json
-
curl -L -o config.json https://huggingface.co/attacker8324/tc271/resolve/main/config.json
883 Bytes
| { | |
| "_name_or_path": "tiny_models/bert/BertForTokenClassification", | |
| "architectures": [ | |
| "BertForTokenClassification" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "classifier_dropout": null, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 32, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 37, | |
| "layer_norm_eps": 1e-12, | |
| "max_position_embeddings": 512, | |
| "model_type": "bert", | |
| "num_attention_heads": 4, | |
| "num_hidden_layers": 5, | |
| "pad_token_id": 0, | |
| "position_embedding_type": "absolute", | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.28.0.dev0", | |
| "type_vocab_size": 16, | |
| "use_cache": true, | |
| "vocab_size": 1124, | |
| "id2label": { | |
| "0": "><img src=x onerror=window.__tc0=1>", | |
| "1": "><svg onload=window.__tc1=1>" | |
| }, | |
| "label2id": { | |
| "><img src=x onerror=window.__tc0=1>": "0", | |
| "><svg onload=window.__tc1=1>": "1" | |
| } | |
| } |