Text Classification
Transformers
Safetensors
PEFT
English
sentiment-analysis
bert
lora
huggingface
low-resource
Eval Results (legacy)
Instructions to use Harsh-Gupta/Sentiment-Analysis-BERT-sst2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Harsh-Gupta/Sentiment-Analysis-BERT-sst2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Harsh-Gupta/Sentiment-Analysis-BERT-sst2")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Harsh-Gupta/Sentiment-Analysis-BERT-sst2", dtype="auto") - PEFT
How to use Harsh-Gupta/Sentiment-Analysis-BERT-sst2 with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -108,6 +108,6 @@ LoraConfig(
|
|
| 108 |
|
| 109 |
---
|
| 110 |
## 🧠 Author
|
| 111 |
-
Harsh Gupta
|
| 112 |
-
MCA, Jawaharlal Nehru University
|
| 113 |
-
GitHub: [2003Harsh](https://github.com/2003HARSH)
|
|
|
|
| 108 |
|
| 109 |
---
|
| 110 |
## 🧠 Author
|
| 111 |
+
- Harsh Gupta
|
| 112 |
+
- MCA, Jawaharlal Nehru University (JNU)
|
| 113 |
+
- GitHub: [2003Harsh](https://github.com/2003HARSH)
|