Instructions to use benjamin/wtp-bert-tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use benjamin/wtp-bert-tiny with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="benjamin/wtp-bert-tiny")# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForTokenClassification model = AutoModelForTokenClassification.from_pretrained("benjamin/wtp-bert-tiny", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from benjamin/wtp-bert-tiny: direct link, hf CLI and curl.
- Browser
- Download file 656 Bytes
-
https://huggingface.co/benjamin/wtp-bert-tiny/resolve/4f4cdb5ddba3fb3f87cc3f1fe36e36a683afe545/config.json
- Command line
-
hf download hf://benjamin/wtp-bert-tiny@4f4cdb5ddba3fb3f87cc3f1fe36e36a683afe545/config.json
-
curl -L -o config.json https://huggingface.co/benjamin/wtp-bert-tiny/resolve/4f4cdb5ddba3fb3f87cc3f1fe36e36a683afe545/config.json
656 Bytes
| { | |
| "architectures": [ | |
| "BertCharForTokenClassification" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "classifier_dropout": null, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 128, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 512, | |
| "layer_norm_eps": 1e-12, | |
| "max_position_embeddings": 512, | |
| "model_type": "bert", | |
| "num_attention_heads": 2, | |
| "num_hash_buckets": 8192, | |
| "num_hash_functions": 8, | |
| "num_hidden_layers": 2, | |
| "pad_token_id": 0, | |
| "position_embedding_type": "absolute", | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.27.4", | |
| "type_vocab_size": 2, | |
| "use_cache": true, | |
| "vocab_size": 30522 | |
| } | |