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import warnings |
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warnings.filterwarnings("ignore") |
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from sklearn.metrics import accuracy_score,f1_score |
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from datasets import load_dataset |
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from tqdm import tqdm |
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import datasets |
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import torch |
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def format_example(example: dict) -> dict: |
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context = f"Instruction: {example['instruction']}\n" |
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if example.get("input"): |
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context += f"Input: {example['input']}\n" |
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context += "Answer: " |
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target = example["output"] |
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return {"context": context, "target": target} |
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def add_instructions(x): |
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if x.format == "post": |
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return "What is the sentiment of this tweet? Please choose an answer from {negative/neutral/positive}." |
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else: |
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return "What is the sentiment of this news? Please choose an answer from {negative/neutral/positive}." |
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def make_label(x): |
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if x < - 0.1: return "negative" |
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elif x >=-0.1 and x < 0.1: return "neutral" |
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elif x >= 0.1: return "positive" |
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def change_target(x): |
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if 'positive' in x or 'Positive' in x: |
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return 'positive' |
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elif 'negative' in x or 'Negative' in x: |
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return 'negative' |
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else: |
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return 'neutral' |
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def test_fiqa(model, tokenizer, batch_size = 8, prompt_fun = None ): |
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dataset = load_dataset('pauri32/fiqa-2018') |
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dataset = datasets.concatenate_datasets([dataset["train"], dataset["validation"] ,dataset["test"] ]) |
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dataset = dataset.train_test_split(0.226, seed = 42)['test'] |
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dataset = dataset.to_pandas() |
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dataset["output"] = dataset.sentiment_score.apply(make_label) |
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if prompt_fun is None: |
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dataset["instruction"] = "What is the sentiment of this news? Please choose an answer from {negative/neutral/positive}." |
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else: |
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dataset["instruction"] = dataset.apply(prompt_fun, axis = 1) |
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dataset = dataset[['sentence', 'output',"instruction"]] |
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dataset.columns = ["input", "output","instruction"] |
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dataset[["context","target"]] = dataset.apply(format_example, axis = 1, result_type="expand") |
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print(f"\n\nPrompt example:\n{dataset['context'][0]}\n\n") |
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context = dataset['context'].tolist() |
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total_steps = dataset.shape[0]//batch_size + 1 |
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print(f"Total len: {len(context)}. Batchsize: {batch_size}. Total steps: {total_steps}") |
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out_text_list = [] |
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for i in tqdm(range(total_steps)): |
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tmp_context = context[i* batch_size:(i+1)* batch_size] |
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tokens = tokenizer(tmp_context, return_tensors='pt', padding=True, max_length=512) |
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for k in tokens.keys(): |
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tokens[k] = tokens[k].cuda() |
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res = model.generate(**tokens, max_length=512) |
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res_sentences = [tokenizer.decode(i) for i in res] |
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out_text = [o.split("Answer: ")[1] for o in res_sentences] |
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out_text_list += out_text |
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torch.cuda.empty_cache() |
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dataset["out_text"] = out_text_list |
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dataset["new_target"] = dataset["target"].apply(change_target) |
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dataset["new_out"] = dataset["out_text"].apply(change_target) |
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acc = accuracy_score(dataset["new_target"], dataset["new_out"]) |
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f1_macro = f1_score(dataset["new_target"], dataset["new_out"], average = "macro") |
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f1_micro = f1_score(dataset["new_target"], dataset["new_out"], average = "micro") |
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f1_weighted = f1_score(dataset["new_target"], dataset["new_out"], average = "weighted") |
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print(f"Acc: {acc}. F1 macro: {f1_macro}. F1 micro: {f1_micro}. F1 weighted (BloombergGPT): {f1_weighted}. ") |
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return dataset |
