Text Classification
Transformers
TensorBoard
Safetensors
modernbert
sentiment
multilingual
sentiment-analysis
product-reviews
place-reviews
text-embeddings-inference
Instructions to use clapAI/modernBERT-large-multilingual-sentiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use clapAI/modernBERT-large-multilingual-sentiment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="clapAI/modernBERT-large-multilingual-sentiment")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("clapAI/modernBERT-large-multilingual-sentiment") model = AutoModelForSequenceClassification.from_pretrained("clapAI/modernBERT-large-multilingual-sentiment", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download train_results.json from clapAI/modernBERT-large-multilingual-sentiment: direct link, hf CLI and curl.
- Browser
- Download file 169 Bytes
-
https://huggingface.co/clapAI/modernBERT-large-multilingual-sentiment/resolve/main/train_results.json
- Command line
-
hf download hf://clapAI/modernBERT-large-multilingual-sentiment/train_results.json
-
curl -L -o train_results.json https://huggingface.co/clapAI/modernBERT-large-multilingual-sentiment/resolve/main/train_results.json
169 Bytes
| { | |
| "epoch": 5.0, | |
| "train_loss": 0.6255086388770993, | |
| "train_runtime": 65052.9958, | |
| "train_samples_per_second": 241.916, | |
| "train_steps_per_second": 0.118 | |
| } |