Instructions to use ChunLok/CAM_sentiment_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ChunLok/CAM_sentiment_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ChunLok/CAM_sentiment_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ChunLok/CAM_sentiment_model") model = AutoModelForSequenceClassification.from_pretrained("ChunLok/CAM_sentiment_model") - Notebooks
- Google Colab
- Kaggle
Update tokenizer_config.json
Browse files- tokenizer_config.json +1 -1
tokenizer_config.json
CHANGED
|
@@ -50,6 +50,6 @@
|
|
| 50 |
"sep_token": "[SEP]",
|
| 51 |
"strip_accents": null,
|
| 52 |
"tokenize_chinese_chars": true,
|
| 53 |
-
"tokenizer_class": "
|
| 54 |
"unk_token": "[UNK]"
|
| 55 |
}
|
|
|
|
| 50 |
"sep_token": "[SEP]",
|
| 51 |
"strip_accents": null,
|
| 52 |
"tokenize_chinese_chars": true,
|
| 53 |
+
"tokenizer_class": "BertTokenizer",
|
| 54 |
"unk_token": "[UNK]"
|
| 55 |
}
|