Instructions to use everyl12/stance_class_mod with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use everyl12/stance_class_mod with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="everyl12/stance_class_mod")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("everyl12/stance_class_mod") model = AutoModelForSequenceClassification.from_pretrained("everyl12/stance_class_mod") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -13,8 +13,7 @@ should probably proofread and complete it, then remove this comment. -->
|
|
| 13 |
|
| 14 |
# stance_class_mod
|
| 15 |
|
| 16 |
-
This model is a fine-tuned version of [vinai/bertweet-base](https://huggingface.co/vinai/bertweet-base) on the dataset of 644 labeled tweets
|
| 17 |
-
It classified the stance of an individual's tweet toward Bayer, Monsanto, or other relevant organizations in the crisis. Two stances were detected: (0) Aggressive, (1) Non-Aggressive (neutral and accomodative).
|
| 18 |
It achieves the following results on the evaluation set:
|
| 19 |
- Loss: 0.5008
|
| 20 |
- Accuracy: 0.8605
|
|
|
|
| 13 |
|
| 14 |
# stance_class_mod
|
| 15 |
|
| 16 |
+
This model is a fine-tuned version of [vinai/bertweet-base](https://huggingface.co/vinai/bertweet-base) on the dataset of 644 labeled tweets.
|
|
|
|
| 17 |
It achieves the following results on the evaluation set:
|
| 18 |
- Loss: 0.5008
|
| 19 |
- Accuracy: 0.8605
|