Text Classification
Transformers
Safetensors
English
modernbert
text-generation-inference
unsloth
trl
text-embeddings-inference
Instructions to use Nonovogo/emotionbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Nonovogo/emotionbert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Nonovogo/emotionbert")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Nonovogo/emotionbert") model = AutoModelForSequenceClassification.from_pretrained("Nonovogo/emotionbert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Desktop
Update README.md
Browse files
README.md
CHANGED
|
@@ -23,6 +23,7 @@ datasets:
|
|
| 23 |
- 0.868 F1
|
| 24 |
- 0.868 Accuracy
|
| 25 |
- 1 : Happy, 0 : Sad
|
|
|
|
| 26 |
|
| 27 |
This modernbert model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth)
|
| 28 |
|
|
|
|
| 23 |
- 0.868 F1
|
| 24 |
- 0.868 Accuracy
|
| 25 |
- 1 : Happy, 0 : Sad
|
| 26 |
+
- Need to merge with modernbert-base first
|
| 27 |
|
| 28 |
This modernbert model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth)
|
| 29 |
|