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process eval issue

This commit is contained in:
zhipuch 2024-11-11 17:33:43 +08:00
parent a0c568753a
commit a7dbfa5ada
2 changed files with 7 additions and 3 deletions
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4
finetune_demo/finetune.py
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@ -348,7 +348,7 @@ def load_tokenizer_and_model(
model_dir: str, model_dir: str,
peft_config: Optional[PeftConfig] = None, peft_config: Optional[PeftConfig] = None,
): ):
tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True) tokenizer = AutoTokenizer.from_pretrained(model_dir, padding_side='left', trust_remote_code=True)
if peft_config is not None: if peft_config is not None:
model = AutoModelForCausalLM.from_pretrained( model = AutoModelForCausalLM.from_pretrained(
model_dir, model_dir,
@ -370,6 +370,8 @@ def load_tokenizer_and_model(
def compute_metrics(eval_preds: EvalPrediction, tokenizer): def compute_metrics(eval_preds: EvalPrediction, tokenizer):
batched_pred_ids, batched_label_ids = eval_preds batched_pred_ids, batched_label_ids = eval_preds
batched_pred_ids[batched_pred_ids==-100] = tokenizer.pad_token_id
batched_label_ids[batched_label_ids==-100] = tokenizer.pad_token_id
metrics_dct = {'rouge-1': [], 'rouge-2': [], 'rouge-l': [], 'bleu-4': []} metrics_dct = {'rouge-1': [], 'rouge-2': [], 'rouge-l': [], 'bleu-4': []}
for pred_ids, label_ids in zip(batched_pred_ids, batched_label_ids): for pred_ids, label_ids in zip(batched_pred_ids, batched_label_ids):
pred_txt = tokenizer.decode(pred_ids).strip() pred_txt = tokenizer.decode(pred_ids).strip()

6
finetune_demo/finetune_vision.py
View File

@ -364,7 +364,7 @@ def process_batch_eval(
if not dialogue_parts or dialogue_parts[-1] != len(input_ids): if not dialogue_parts or dialogue_parts[-1] != len(input_ids):
dialogue_parts.append(len(input_ids)) dialogue_parts.append(len(input_ids))
# Split the conversation into multiple dialogue segments # Split the conversation into multiple dialogue segments
for end_idx in range(1, len(dialogue_parts)): for end_idx in range(1, len(dialogue_parts)):
input_segment = input_ids[:dialogue_parts[end_idx]] input_segment = input_ids[:dialogue_parts[end_idx]]
attention_segment = attention_mask[:dialogue_parts[end_idx]] attention_segment = attention_mask[:dialogue_parts[end_idx]]
@ -394,7 +394,7 @@ def load_tokenizer_and_model(
model_dir: str, model_dir: str,
peft_config: Optional[PeftConfig] = None, peft_config: Optional[PeftConfig] = None,
): ):
tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True) tokenizer = AutoTokenizer.from_pretrained(model_dir, padding_side='left', trust_remote_code=True)
if peft_config is not None: if peft_config is not None:
model = AutoModelForCausalLM.from_pretrained( model = AutoModelForCausalLM.from_pretrained(
model_dir, model_dir,
@ -416,6 +416,8 @@ def load_tokenizer_and_model(
def compute_metrics(eval_preds: EvalPrediction, tokenizer): def compute_metrics(eval_preds: EvalPrediction, tokenizer):
batched_pred_ids, batched_label_ids = eval_preds batched_pred_ids, batched_label_ids = eval_preds
batched_pred_ids[batched_pred_ids==-100] = tokenizer.pad_token_id
batched_label_ids[batched_label_ids==-100] = tokenizer.pad_token_id
metrics_dct = {'rouge-1': [], 'rouge-2': [], 'rouge-l': [], 'bleu-4': []} metrics_dct = {'rouge-1': [], 'rouge-2': [], 'rouge-l': [], 'bleu-4': []}
for pred_ids, label_ids in zip(batched_pred_ids, batched_label_ids): for pred_ids, label_ids in zip(batched_pred_ids, batched_label_ids):
pred_txt = tokenizer.decode(pred_ids).strip() pred_txt = tokenizer.decode(pred_ids).strip()