eval_results = trainer.evaluate()
print(” Evaluation Results:”)
for key, value in eval_results.items():
if isinstance(value, float):
print(f” {key:<25}: {value:.4f}”)
from sklearn.metrics import classification_report, confusion_matrix
preds_output = trainer.predict(eval_ds)
preds = np.argmax(preds_output.predictions, axis=-1)
labels = preds_output.label_ids
print(“\n Classification Report:”)
print(classification_report(labels, preds, target_names=[“NEGATIVE”, “POSITIVE”]))
cm = confusion_matrix(labels, preds)
fig, ax = plt.subplots(figsize=(5, 4))
im = ax.imshow(cm, cmap=”Blues”)
ax.set_xticks([0, 1]); ax.set_yticks([0, 1])
ax.set_xticklabels([“NEGATIVE”, “POSITIVE”])
ax.set_yticklabels([“NEGATIVE”, “POSITIVE”])
ax.set_xlabel(“Predicted”); ax.set_ylabel(“Actual”)
ax.set_title(“Confusion Matrix — Fine-Tuned DistilBERT”)
for i in range(2):
for j in range(2):
ax.text(j, i, str(cm[i, j]), ha=”center”, va=”center”,
color=”white” if cm[i, j] > cm.max()/2 else “black”, fontsize=18)
plt.colorbar(im)
plt.tight_layout()
plt.savefig(“confusion_matrix.png”, dpi=150)
plt.show()
print(” ✅ Saved confusion_matrix.png”)
print(“\n── Testing Fine-Tuned Model on New Inputs ──”)
ft_pipeline = hf_pipeline(
“sentiment-analysis”,
model=trainer.model,
tokenizer=tokenizer,
device=DEVICE,
)
new_reviews = [
“An absolutely breathtaking masterpiece with brilliant performances!”,
“Waste of two hours. Terrible script and wooden acting.”,
“Decent popcorn movie but nothing special. Had some fun moments.”,
]
for review in new_reviews:
res = ft_pipeline(review)[0]
emoji = “🟢” if res[“label”] == “POSITIVE” else “🔴”
print(f’ {emoji} {res[“label”]} ({res[“score”]:.4f}): “{review}”‘)
print(“\n💾 EXPORTING THE FINE-TUNED MODEL\n”)
save_path = “./ms_finetuned_model/final”
trainer.save_model(save_path)
tokenizer.save_pretrained(save_path)
print(f” ✅ Model saved to: {save_path}”)
print(f” Files: {os.listdir(save_path)}”)
print(“\n── ONNX Export ──”)
try:
from optimum.exporters.onnx import main_export
onnx_path = “./ms_finetuned_model/onnx”
main_export(save_path, output=onnx_path, task=”text-classification”)
print(f” ✅ ONNX model exported to: {onnx_path}”)
print(f” Files: {os.listdir(onnx_path)}”)
except Exception as e:
print(f” ⚠️ ONNX export skipped: {e}”)
print(“””
── Upload to ModelScope Hub (manual step) ──
1. Get a token from https://modelscope.cn/my/myaccesstoken
2. Run:
from modelscope.hub.api import HubApi
api = HubApi()
api.login(‘YOUR_TOKEN’)
api.push_model(
model_id=’your-username/my-finetuned-distilbert’,
model_dir=”./ms_finetuned_model/final”,
)
“””)
print(“””
╔══════════════════════════════════════════════════════════════════╗
║ 🎉 TUTORIAL COMPLETE! 🎉 ║
╠══════════════════════════════════════════════════════════════════╣
║ ✓ ModelScope Hub — search, browse & download models ║
║ ✓ MsDataset — load datasets from the ModelScope ecosystem ║
║ ✓ NLP pipelines — sentiment, NER, zero-shot, generation, mask ║
║ ✓ CV pipelines — image classification, object detection, viz ║
║ ✓ HuggingFace interop — snapshot_download + Transformers ║
║ ✓ Fine-tuning — DistilBERT on IMDB with Trainer API ║
║ ✓ Evaluation — accuracy, F1, confusion matrix ║
║ ✓ Export — local save, ONNX, Hub upload ║
╚══════════════════════════════════════════════════════════════════╝
“””)
