Text Classification
Transformers
Safetensors
xlm-roberta
Generated from Trainer
text-embeddings-inference
Instructions to use anpmts/bert-sentiment-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use anpmts/bert-sentiment-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="anpmts/bert-sentiment-classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("anpmts/bert-sentiment-classifier") model = AutoModelForSequenceClassification.from_pretrained("anpmts/bert-sentiment-classifier") - Notebooks
- Google Colab
- Kaggle
| { | |
| "epoch": 10.0, | |
| "eval_accuracy": 0.9736184171050263, | |
| "eval_f1": 0.9736184057853046, | |
| "eval_f1_negative": 0.973601124804235, | |
| "eval_f1_neutral": 0.973635686766374, | |
| "eval_f1_positive": 0.0, | |
| "eval_loss": 0.0820448026061058, | |
| "eval_precision": 0.9736191924477628, | |
| "eval_recall": 0.9736184043929482, | |
| "eval_runtime": 156.7418, | |
| "eval_samples_per_second": 2551.815, | |
| "eval_steps_per_second": 4.989, | |
| "total_flos": 4.5910293334888284e+18, | |
| "train_loss": 0.03279527726586759, | |
| "train_runtime": 72417.8071, | |
| "train_samples_per_second": 497.087, | |
| "train_steps_per_second": 1.942 | |
| } |