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")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("anpmts/bert-sentiment-classifier") model = AutoModelForSequenceClassification.from_pretrained("anpmts/bert-sentiment-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download eval_results.json from anpmts/bert-sentiment-classifier: direct link, hf CLI and curl.
- Browser
- Download file 436 Bytes
-
https://huggingface.co/anpmts/bert-sentiment-classifier/resolve/main/eval_results.json
- Command line
-
hf download hf://anpmts/bert-sentiment-classifier/eval_results.json
-
curl -L -o eval_results.json https://huggingface.co/anpmts/bert-sentiment-classifier/resolve/main/eval_results.json
436 Bytes
| { | |
| "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 | |
| } |