Text Classification
Transformers
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use miguelpr/roberta-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use miguelpr/roberta-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="miguelpr/roberta-base")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("miguelpr/roberta-base") model = AutoModelForSequenceClassification.from_pretrained("miguelpr/roberta-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download all_results.json from miguelpr/roberta-base: direct link, hf CLI and curl.
- Browser
- Download file 424 Bytes
-
https://huggingface.co/miguelpr/roberta-base/resolve/main/all_results.json
- Command line
-
hf download hf://miguelpr/roberta-base/all_results.json
-
curl -L -o all_results.json https://huggingface.co/miguelpr/roberta-base/resolve/main/all_results.json
424 Bytes
| { | |
| "epoch": 2.0, | |
| "eval_accuracy": 0.645, | |
| "eval_loss": 1.2481324672698975, | |
| "eval_runtime": 12.9479, | |
| "eval_samples": 400, | |
| "eval_samples_per_second": 30.893, | |
| "eval_steps_per_second": 1.004, | |
| "total_flos": 841955377152000.0, | |
| "train_loss": 0.7585713958740234, | |
| "train_runtime": 297.7415, | |
| "train_samples": 1600, | |
| "train_samples_per_second": 10.748, | |
| "train_steps_per_second": 0.672 | |
| } |