Update README.md

README.md

@@ -9,11 +9,36 @@ pipeline_tag: text-classification
 
 # moshew/gte_small_setfit-sst2-english
 
-This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
+This is a [SetFit model](https://github.com/huggingface/setfit) ("thenlper/gte_small") that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
 
 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
 2. Training a classification head with features from the fine-tuned Sentence Transformer.
 
+## Training code
+
+```python
+from setfit import SetFitModel
+
+from datasets import load_dataset
+from setfit import SetFitModel, SetFitTrainer
+
+# Load a dataset from the Hugging Face Hub
+dataset = load_dataset("SetFit/sst2")
+
+# Upload Train and Test data
+num_classes = 2
+test_ds = dataset["test"]
+train_ds = dataset["train"]
+
+model = SetFitModel.from_pretrained("thenlper/gte_small") 
+trainer = SetFitTrainer(model=model, train_dataset=train_ds, eval_dataset=test_ds)
+
+# Train and evaluate
+trainer.train()
+trainer.evaluate()['accuracy']
+
+```
+
 ## Usage
 
 To use this model for inference, first install the SetFit library:
@@ -33,6 +58,13 @@ model = SetFitModel.from_pretrained("moshew/gte_small_setfit-sst2-english")
 preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
 ```
 
+## Accuracy
+On SST-2 dev set:
+
+91.2%  SetFit
+
+88.3% (no Fine-Tuning)
+
 ## BibTeX entry and citation info
 
 ```bibtex