Instructions to use g8a9/roberta-tiny-10M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use g8a9/roberta-tiny-10M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="g8a9/roberta-tiny-10M")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("g8a9/roberta-tiny-10M") model = AutoModelForMaskedLM.from_pretrained("g8a9/roberta-tiny-10M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download eval_results.json from g8a9/roberta-tiny-10M: direct link, hf CLI and curl.
- Browser
- Download file 267 Bytes
-
https://huggingface.co/g8a9/roberta-tiny-10M/resolve/main/eval_results.json
- Command line
-
hf download hf://g8a9/roberta-tiny-10M/eval_results.json
-
curl -L -o eval_results.json https://huggingface.co/g8a9/roberta-tiny-10M/resolve/main/eval_results.json
267 Bytes
| { | |
| "epoch": 89.58, | |
| "eval_accuracy": 0.5148013040185571, | |
| "eval_loss": 2.73911714553833, | |
| "eval_runtime": 145.896, | |
| "eval_samples": 24055, | |
| "eval_samples_per_second": 164.878, | |
| "eval_steps_per_second": 5.154, | |
| "perplexity": 15.47331837619993 | |
| } |