Instructions to use spjabech/sentiment_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use spjabech/sentiment_classification with PEFT:
from peft import PeftModel from transformers import AutoModelForSequenceClassification base_model = AutoModelForSequenceClassification.from_pretrained("gradientai/Llama-3-8B-Instruct-262k") model = PeftModel.from_pretrained(base_model, "spjabech/sentiment_classification") - Notebooks
- Google Colab
- Kaggle
Download train_results.json from spjabech/sentiment_classification: direct link, hf CLI and curl.
- Browser
- Download file 229 Bytes
-
https://huggingface.co/spjabech/sentiment_classification/resolve/main/train_results.json
- Command line
-
hf download hf://spjabech/sentiment_classification/train_results.json
-
curl -L -o train_results.json https://huggingface.co/spjabech/sentiment_classification/resolve/main/train_results.json
229 Bytes
| { | |
| "epoch": 1.0, | |
| "total_flos": 6359033384534016.0, | |
| "train_loss": 1.1705604413071193, | |
| "train_runtime": 1777.143, | |
| "train_samples": 296, | |
| "train_samples_per_second": 0.167, | |
| "train_steps_per_second": 0.021 | |
| } |