Text Classification
Transformers
PyTorch
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use SetFit/distilbert-base-uncased__sst2__train-8-0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SetFit/distilbert-base-uncased__sst2__train-8-0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="SetFit/distilbert-base-uncased__sst2__train-8-0")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("SetFit/distilbert-base-uncased__sst2__train-8-0") model = AutoModelForSequenceClassification.from_pretrained("SetFit/distilbert-base-uncased__sst2__train-8-0", device_map="auto") - Notebooks
- Google Colab
- Kaggle
update model card README.md
Browse files
README.md
CHANGED
|
@@ -55,6 +55,11 @@ The following hyperparameters were used during training:
|
|
| 55 |
| 0.6211 | 4.0 | 12 | 0.7322 | 0.25 |
|
| 56 |
| 0.5522 | 5.0 | 15 | 0.7561 | 0.25 |
|
| 57 |
| 0.488 | 6.0 | 18 | 0.7883 | 0.25 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 58 |
|
| 59 |
|
| 60 |
### Framework versions
|
|
|
|
| 55 |
| 0.6211 | 4.0 | 12 | 0.7322 | 0.25 |
|
| 56 |
| 0.5522 | 5.0 | 15 | 0.7561 | 0.25 |
|
| 57 |
| 0.488 | 6.0 | 18 | 0.7883 | 0.25 |
|
| 58 |
+
| 0.48 | 7.0 | 21 | 0.8224 | 0.25 |
|
| 59 |
+
| 0.3948 | 8.0 | 24 | 0.8605 | 0.25 |
|
| 60 |
+
| 0.3478 | 9.0 | 27 | 0.8726 | 0.25 |
|
| 61 |
+
| 0.2723 | 10.0 | 30 | 0.8885 | 0.25 |
|
| 62 |
+
| 0.2174 | 11.0 | 33 | 0.8984 | 0.5 |
|
| 63 |
|
| 64 |
|
| 65 |
### Framework versions
|