Text Classification
Transformers
Safetensors
modernbert
sentiment
multilingual
sentiment-analysis
product-reviews
place-reviews
mmbert
text-embeddings-inference
Instructions to use clapAI/mmBERT-small-multilingual-sentiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use clapAI/mmBERT-small-multilingual-sentiment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="clapAI/mmBERT-small-multilingual-sentiment")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("clapAI/mmBERT-small-multilingual-sentiment") model = AutoModelForSequenceClassification.from_pretrained("clapAI/mmBERT-small-multilingual-sentiment") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a853bbedd87114c432148c2da564a082d68a009d47735e52b6dd803a310ec2de
- Size of remote file:
- 34.4 MB
- SHA256:
- 609d8f4c067cd3950f88594c5a802616cea245823836ef5848ee4fc40aab5b6f
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