Token Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
Eval Results (legacy)
Instructions to use desonglll/test-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use desonglll/test-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="desonglll/test-ner")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("desonglll/test-ner") model = AutoModelForTokenClassification.from_pretrained("desonglll/test-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download eval_results.json from desonglll/test-ner: direct link, hf CLI and curl.
- Browser
- Download file 346 Bytes
-
https://huggingface.co/desonglll/test-ner/resolve/main/eval_results.json
- Command line
-
hf download hf://desonglll/test-ner/eval_results.json
-
curl -L -o eval_results.json https://huggingface.co/desonglll/test-ner/resolve/main/eval_results.json
346 Bytes
| { | |
| "epoch": 3.0, | |
| "eval_accuracy": 0.9896226782446167, | |
| "eval_f1": 0.9493203557643899, | |
| "eval_loss": 0.049828801304101944, | |
| "eval_precision": 0.946619812583668, | |
| "eval_recall": 0.9520363513968361, | |
| "eval_runtime": 9.7904, | |
| "eval_samples": 3250, | |
| "eval_samples_per_second": 331.959, | |
| "eval_steps_per_second": 41.572 | |
| } |