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 train_results.json from anpmts/bert-sentiment-classifier: direct link, hf CLI and curl.
- Browser
- Download file 213 Bytes
-
https://huggingface.co/anpmts/bert-sentiment-classifier/resolve/main/train_results.json
- Command line
-
hf download hf://anpmts/bert-sentiment-classifier/train_results.json
-
curl -L -o train_results.json https://huggingface.co/anpmts/bert-sentiment-classifier/resolve/main/train_results.json
213 Bytes
| { | |
| "epoch": 10.0, | |
| "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 | |
| } |