Instructions to use almanach/camembertav2-base-ftb-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use almanach/camembertav2-base-ftb-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="almanach/camembertav2-base-ftb-ner")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("almanach/camembertav2-base-ftb-ner") model = AutoModelForTokenClassification.from_pretrained("almanach/camembertav2-base-ftb-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download train_results.json from almanach/camembertav2-base-ftb-ner: direct link, hf CLI and curl.
- Browser
- Download file 230 Bytes
-
https://huggingface.co/almanach/camembertav2-base-ftb-ner/resolve/main/train_results.json
- Command line
-
hf download hf://almanach/camembertav2-base-ftb-ner/train_results.json
-
curl -L -o train_results.json https://huggingface.co/almanach/camembertav2-base-ftb-ner/resolve/main/train_results.json
230 Bytes
| { | |
| "epoch": 8.0, | |
| "total_flos": 2834249641269840.0, | |
| "train_loss": 0.05133420664178129, | |
| "train_runtime": 849.5258, | |
| "train_samples": 9881, | |
| "train_samples_per_second": 93.05, | |
| "train_steps_per_second": 5.82 | |
| } |