Text Classification
Transformers
Safetensors
English
distilbert
nlp
hotels
reviews
sentiment-analysis
text-embeddings-inference
Instructions to use nhull/distilbert-sentiment-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nhull/distilbert-sentiment-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="nhull/distilbert-sentiment-model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("nhull/distilbert-sentiment-model") model = AutoModelForSequenceClassification.from_pretrained("nhull/distilbert-sentiment-model") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -76,7 +76,7 @@ Model predicts too high on average by `0.3934`.
|
|
| 76 |
|
| 77 |
### Confusion Matrix (Test Set)
|
| 78 |
|
| 79 |
-
|
|
| 80 |
|-------------------|------|------|------|------|------|
|
| 81 |
| **1** | 1097 | 437 | 60 | 3 | 3 |
|
| 82 |
| **2** | 327 | 887 | 344 | 34 | 8 |
|
|
|
|
| 76 |
|
| 77 |
### Confusion Matrix (Test Set)
|
| 78 |
|
| 79 |
+
| True \\ Predicted | 1 | 2 | 3 | 4 | 5 |
|
| 80 |
|-------------------|------|------|------|------|------|
|
| 81 |
| **1** | 1097 | 437 | 60 | 3 | 3 |
|
| 82 |
| **2** | 327 | 887 | 344 | 34 | 8 |
|