Instructions to use Osiris/neutral_non_neutral_classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Osiris/neutral_non_neutral_classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Osiris/neutral_non_neutral_classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Osiris/neutral_non_neutral_classifier") model = AutoModelForSequenceClassification.from_pretrained("Osiris/neutral_non_neutral_classifier") - Notebooks
- Google Colab
- Kaggle
Update config.json
Browse files- config.json +4 -0
config.json
CHANGED
|
@@ -3,6 +3,10 @@
|
|
| 3 |
"architectures": [
|
| 4 |
"RobertaForSequenceClassification"
|
| 5 |
],
|
|
|
|
|
|
|
|
|
|
|
|
|
| 6 |
"attention_probs_dropout_prob": 0.1,
|
| 7 |
"bos_token_id": 0,
|
| 8 |
"classifier_dropout": null,
|
|
|
|
| 3 |
"architectures": [
|
| 4 |
"RobertaForSequenceClassification"
|
| 5 |
],
|
| 6 |
+
"id2label": {
|
| 7 |
+
"0": "Neutral",
|
| 8 |
+
"1": "Non-Neutral"
|
| 9 |
+
},
|
| 10 |
"attention_probs_dropout_prob": 0.1,
|
| 11 |
"bos_token_id": 0,
|
| 12 |
"classifier_dropout": null,
|