| | |
|---|
| | | generated_ids = self.llm.generate( |
|---|
| | | inputs_embeds=inputs_embeds, max_new_tokens=kwargs.get("max_length", 512) |
|---|
| | | ) |
|---|
| | | generated_ids = [ |
|---|
| | | output_ids[len(input_id) :] |
|---|
| | | for input_id, output_ids in zip(input_ids, generated_ids) |
|---|
| | | ] |
|---|
| | | # generated_ids = [ |
|---|
| | | # output_ids[len(input_id) :] |
|---|
| | | # for input_id, output_ids in zip(input_ids, generated_ids) |
|---|
| | | # ] |
|---|
| | | response = tokenizer.batch_decode( |
|---|
| | | generated_ids, skip_special_tokens=kwargs.get("skip_special_tokens", True) |
|---|
| | | )[0] |
|---|
| | |
|---|
| | | ibest_writer = self.writer[f"{0 + 1}best_recog"] |
|---|
| | | |
|---|
| | | results = [] |
|---|
| | | result_i = {"key": key[0], "text": response, "label": label} |
|---|
| | | response_clean = re.sub("[^\w\s\u3000\u4e00-\u9fff]+", "", response) |
|---|
| | | result_i = {"key": key[0], "text": response, "text_tn": response_clean, "label": label} |
|---|
| | | if loss is not None: |
|---|
| | | result_i["loss"] = loss |
|---|
| | | results.append(result_i) |
|---|
| | |
|---|
| | | if ibest_writer is not None: |
|---|
| | | ibest_writer["text"][key[0]] = response |
|---|
| | | ibest_writer["label"][key[0]] = label |
|---|
| | | ibest_writer["text_tn"][key[0]] = response_clean |
|---|
| | | |
|---|
| | | return results, meta_data |
|---|
