Instructions to use tner/deberta-large-wnut2017 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tner/deberta-large-wnut2017 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="tner/deberta-large-wnut2017")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("tner/deberta-large-wnut2017") model = AutoModelForTokenClassification.from_pretrained("tner/deberta-large-wnut2017", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download trainer_config.json from tner/deberta-large-wnut2017: direct link, hf CLI and curl.
- Browser
- Download file 341 Bytes
-
https://huggingface.co/tner/deberta-large-wnut2017/resolve/main/trainer_config.json
- Command line
-
hf download hf://tner/deberta-large-wnut2017/trainer_config.json
-
curl -L -o trainer_config.json https://huggingface.co/tner/deberta-large-wnut2017/resolve/main/trainer_config.json
341 Bytes
| {"dataset": ["tner/wnut2017"], "dataset_split": "train", "dataset_name": null, "local_dataset": null, "model": "microsoft/deberta-large", "crf": true, "max_length": 128, "epoch": 15, "batch_size": 16, "lr": 1e-05, "random_seed": 42, "gradient_accumulation_steps": 4, "weight_decay": 1e-07, "lr_warmup_step_ratio": 0.1, "max_grad_norm": 10.0} |