Text Classification
Transformers
PyTorch
TensorFlow
Safetensors
Turkish
bert
text-embeddings-inference
Instructions to use coltekin/berturk-tremo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use coltekin/berturk-tremo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="coltekin/berturk-tremo")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("coltekin/berturk-tremo") model = AutoModelForSequenceClassification.from_pretrained("coltekin/berturk-tremo") - Notebooks
- Google Colab
- Kaggle
Typo.
Browse files
README.md
CHANGED
|
@@ -8,7 +8,7 @@ datasets:
|
|
| 8 |
|
| 9 |
This model is [BERTurk cased](https://huggingface.co/dbmdz/bert-base-turkish-cased)
|
| 10 |
model finetuned on the [TREMO](http://demir.cs.deu.edu.tr/TREMO) data
|
| 11 |
-
set for
|
| 12 |
|
| 13 |
The macro-average F1 score of the model is approximately 94% on a
|
| 14 |
randomly set aside development set of 2k instances.
|
|
|
|
| 8 |
|
| 9 |
This model is [BERTurk cased](https://huggingface.co/dbmdz/bert-base-turkish-cased)
|
| 10 |
model finetuned on the [TREMO](http://demir.cs.deu.edu.tr/TREMO) data
|
| 11 |
+
set for Turkish emotion classification.
|
| 12 |
|
| 13 |
The macro-average F1 score of the model is approximately 94% on a
|
| 14 |
randomly set aside development set of 2k instances.
|