Add training script etc.

README.md

@@ -59,6 +59,70 @@ The data fields are the same among all splits.
 |---------|----:|---------:|---:|
 |tomaarsen/setfit-absa-semeval-laptops|2358|49|654|
 
+### Training ABSA models using SetFit ABSA
+
+To train using this dataset, first install the SetFit library:
+
+```bash
+pip install setfit
+```
+
+And then you can use the following script as a guideline of how to train an ABSA model on this dataset:
+
+```python
+from setfit import AbsaModel, AbsaTrainer, TrainingArguments
+from datasets import load_dataset
+from transformers import EarlyStoppingCallback
+
+# You can initialize a AbsaModel using one or two SentenceTransformer models, or two ABSA models
+model = AbsaModel.from_pretrained("sentence-transformers/all-MiniLM-L6-v2")
+
+# The training/eval dataset must have `text`, `span`, `polarity`, and `ordinal` columns
+dataset = load_dataset("tomaarsen/setfit-absa-semeval-laptops")
+train_dataset = dataset["train"]
+eval_dataset = dataset["validation"]
+
+args = TrainingArguments(
+    output_dir="models",
+    use_amp=True,
+    batch_size=256,
+    eval_steps=50,
+    save_steps=50,
+    load_best_model_at_end=True,
+)
+
+trainer = AbsaTrainer(
+    model,
+    args=args,
+    train_dataset=train_dataset,
+    eval_dataset=eval_dataset,
+    callbacks=[EarlyStoppingCallback(early_stopping_patience=5)],
+)
+trainer.train()
+
+metrics = trainer.evaluate(eval_dataset)
+print(metrics)
+
+trainer.push_to_hub("tomaarsen/setfit-absa-laptops")
+```
+
+You can then run inference like so:
+```python
+from setfit import AbsaModel
+
+# Download from Hub and run inference
+model = AbsaModel.from_pretrained(
+    "tomaarsen/setfit-absa-laptops-aspect",
+    "tomaarsen/setfit-absa-laptops-polarity",
+)
+
+# Run inference
+preds = model([
+    "Boots up fast and runs great!",
+    "The screen shows great colors.",
+])
+```
+
 ### Citation Information
 
 ```bibtex