Text Classification
Transformers
Safetensors
modernbert
Generated from Trainer
text-embeddings-inference
Instructions to use pradervonsky/modernbert-large_clinc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pradervonsky/modernbert-large_clinc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="pradervonsky/modernbert-large_clinc")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("pradervonsky/modernbert-large_clinc") model = AutoModelForSequenceClassification.from_pretrained("pradervonsky/modernbert-large_clinc") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -17,7 +17,7 @@ should probably proofread and complete it, then remove this comment. -->
|
|
| 17 |
|
| 18 |
# modernbert-large_clinc
|
| 19 |
|
| 20 |
-
This model is a fine-tuned version of [answerdotai/ModernBERT-large](https://huggingface.co/answerdotai/ModernBERT-large) on
|
| 21 |
It achieves the following results on the evaluation set:
|
| 22 |
- Loss: 0.1766
|
| 23 |
- Accuracy: 0.9619
|
|
|
|
| 17 |
|
| 18 |
# modernbert-large_clinc
|
| 19 |
|
| 20 |
+
This model is a fine-tuned version of [answerdotai/ModernBERT-large](https://huggingface.co/answerdotai/ModernBERT-large) on a CLINC150 dataset.
|
| 21 |
It achieves the following results on the evaluation set:
|
| 22 |
- Loss: 0.1766
|
| 23 |
- Accuracy: 0.9619
|