shorten categories

interfaces/cap_minor.py

@@ -71,7 +71,7 @@ def predict(text, model_id, tokenizer_id):
         logits = model(**inputs).logits
 
     probs = torch.nn.functional.softmax(logits, dim=1).cpu().numpy().flatten()
-    output_pred = {f"[{'999' if str(CAP_MIN_NUM_DICT[i]) == '999' else str(CAP_MIN_NUM_DICT[i])[:-2]}] {convert_minor_to_major(CAP_MIN_NUM_DICT[i])} - [{CAP_MIN_NUM_DICT[i]}] {CAP_MIN_LABEL_NAMES[CAP_MIN_NUM_DICT[i]]}": probs[i] for i in np.argsort(probs)[::-1]}
+    output_pred = {f"[{'999' if str(CAP_MIN_NUM_DICT[i]) == '999' else str(CAP_MIN_NUM_DICT[i])[:-2]}]{convert_minor_to_major(CAP_MIN_NUM_DICT[i])} [{CAP_MIN_NUM_DICT[i]}]{CAP_MIN_LABEL_NAMES[CAP_MIN_NUM_DICT[i]]}": probs[i] for i in np.argsort(probs)[::-1]}
     output_info = f'<p style="text-align: center; display: block">Prediction was made using the <a href="https://huggingface.co/{model_id}">{model_id}</a> model.</p>'
     return output_pred, output_info