Instructions to use BSC-LT/roberta_model_for_anonimization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BSC-LT/roberta_model_for_anonimization with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="BSC-LT/roberta_model_for_anonimization")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("BSC-LT/roberta_model_for_anonimization") model = AutoModelForTokenClassification.from_pretrained("BSC-LT/roberta_model_for_anonimization", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download all_results.json from BSC-LT/roberta_model_for_anonimization: direct link, hf CLI and curl.
- Browser
- Download file 921 Bytes
-
https://huggingface.co/BSC-LT/roberta_model_for_anonimization/resolve/main/all_results.json
- Command line
-
hf download hf://BSC-LT/roberta_model_for_anonimization/all_results.json
-
curl -L -o all_results.json https://huggingface.co/BSC-LT/roberta_model_for_anonimization/resolve/main/all_results.json
921 Bytes
| { | |
| "epoch": 5.0, | |
| "eval_accuracy": 0.9905438520216592, | |
| "eval_f1": 0.9003506721215664, | |
| "eval_loss": 0.04321206733584404, | |
| "eval_mem_cpu_alloc_delta": 149028864, | |
| "eval_mem_cpu_peaked_delta": 9793536, | |
| "eval_mem_gpu_alloc_delta": 0, | |
| "eval_mem_gpu_peaked_delta": 314081280, | |
| "eval_precision": 0.8971173444627778, | |
| "eval_recall": 0.9036073907517841, | |
| "eval_runtime": 34.5441, | |
| "eval_samples": 7550, | |
| "eval_samples_per_second": 218.561, | |
| "init_mem_cpu_alloc_delta": -68857856, | |
| "init_mem_cpu_peaked_delta": 154255360, | |
| "init_mem_gpu_alloc_delta": 497570816, | |
| "init_mem_gpu_peaked_delta": 0, | |
| "train_mem_cpu_alloc_delta": 2823634944, | |
| "train_mem_cpu_peaked_delta": 164388864, | |
| "train_mem_gpu_alloc_delta": 1508407296, | |
| "train_mem_gpu_peaked_delta": 10039297024, | |
| "train_runtime": 1076.7664, | |
| "train_samples": 22648, | |
| "train_samples_per_second": 3.288 | |
| } |