Text Classification
Transformers
Safetensors
PyTorch
English
roberta
sentiment-analysis
fastapi
multilingual
docker
kafka
nlp
Eval Results (legacy)
Instructions to use SkyNet-DL/sentiment-roberta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SkyNet-DL/sentiment-roberta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="SkyNet-DL/sentiment-roberta")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("SkyNet-DL/sentiment-roberta") model = AutoModelForSequenceClassification.from_pretrained("SkyNet-DL/sentiment-roberta") - Notebooks
- Google Colab
- Kaggle
Update Readme and add metadata
Browse files
README.md
CHANGED
|
@@ -1,3 +1,8 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
## 📌 Project Philosophy
|
| 2 |
|
| 3 |
This project intentionally preserves a controlled amount of real-world noise inside the final training dataset instead of aggressively sanitizing every sample.
|
|
|
|
| 1 |
+
---
|
| 2 |
+
license: mit
|
| 3 |
+
---
|
| 4 |
+
|
| 5 |
+
|
| 6 |
## 📌 Project Philosophy
|
| 7 |
|
| 8 |
This project intentionally preserves a controlled amount of real-world noise inside the final training dataset instead of aggressively sanitizing every sample.
|