Instructions to use universalner/uner_eng_ewt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use universalner/uner_eng_ewt with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="universalner/uner_eng_ewt")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("universalner/uner_eng_ewt") model = AutoModelForTokenClassification.from_pretrained("universalner/uner_eng_ewt", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download train_results.json from universalner/uner_eng_ewt: direct link, hf CLI and curl.
- Browser
- Download file 195 Bytes
-
https://huggingface.co/universalner/uner_eng_ewt/resolve/main/train_results.json
- Command line
-
hf download hf://universalner/uner_eng_ewt/train_results.json
-
curl -L -o train_results.json https://huggingface.co/universalner/uner_eng_ewt/resolve/main/train_results.json
195 Bytes
| { | |
| "epoch": 5.0, | |
| "train_loss": 0.026263209812495174, | |
| "train_runtime": 785.6494, | |
| "train_samples": 12544, | |
| "train_samples_per_second": 79.832, | |
| "train_steps_per_second": 4.99 | |
| } |