Text Classification
Transformers
Safetensors
distilbert
sentiment-analysis
sentiment
synthetic data
multi-class
social-media-analysis
customer-feedback
product-reviews
brand-monitoring
multilingual
🇪🇺
region:eu
Synthetic
text-embeddings-inference
Instructions to use tabularisai/multilingual-sentiment-analysis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tabularisai/multilingual-sentiment-analysis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tabularisai/multilingual-sentiment-analysis")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tabularisai/multilingual-sentiment-analysis") model = AutoModelForSequenceClassification.from_pretrained("tabularisai/multilingual-sentiment-analysis", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -51,7 +51,7 @@ tags:
|
|
| 51 |
|
| 52 |
|
| 53 |
# NEWS!
|
| 54 |
-
- 2025/8: API for our model! Please see below!
|
| 55 |
|
| 56 |
- 2025/7: We’ve just released ModernFinBERT, a model we’ve been working on for a while. It’s built on the ModernBERT architecture and trained on a mix of real and synthetic data, with LLM-based label correction applied to public datasets to fix human annotation errors.
|
| 57 |
It’s performing well across a range of benchmarks — in some cases improving accuracy by up to 48% over existing models like FinBERT.
|
|
|
|
| 51 |
|
| 52 |
|
| 53 |
# NEWS!
|
| 54 |
+
- 2025/8: Free API for our model! Please see below!
|
| 55 |
|
| 56 |
- 2025/7: We’ve just released ModernFinBERT, a model we’ve been working on for a while. It’s built on the ModernBERT architecture and trained on a mix of real and synthetic data, with LLM-based label correction applied to public datasets to fix human annotation errors.
|
| 57 |
It’s performing well across a range of benchmarks — in some cases improving accuracy by up to 48% over existing models like FinBERT.
|