Instructions to use sbcBI/sentiment_analysis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sbcBI/sentiment_analysis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="sbcBI/sentiment_analysis")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("sbcBI/sentiment_analysis") model = AutoModelForSequenceClassification.from_pretrained("sbcBI/sentiment_analysis") - Notebooks
- Google Colab
- Kaggle
Upload training_args.bin with git-lfs
Browse files- training_args.bin +3 -0
training_args.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:bbd5f5c1c8b780c3f9c86a408867237d2c57d1fe91044c7a8933071d69edac2b
|
| 3 |
+
size 3055
|