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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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Uses
1# タスクとなるデータの読み込み。
2# omnicampusの開発環境では、左にタスクのjsonlをドラッグアンドドロップしてから実行。
3import json
4datasets = []
5with open("./elyza-tasks-100-TV_0.jsonl", "r") as f:
6 item = ""
7 for line in f:
8 line = line.strip()
9 item += line
10 if item.endswith("}"):
11 datasets.append(json.loads(item))
12 item = ""
1# モデルによるタスクの推論。
2from tqdm import tqdm
3
4results = []
5for data in tqdm(datasets):
6
7 input = data["input"]
8
9 prompt = f"""### 指示
10 {input}
11 ### 回答
12 """
13
14 tokenized_input = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt").to(model.device)
15 attention_mask = torch.ones_like(tokenized_input)
16
17 with torch.no_grad():
18 outputs = model.generate(
19 tokenized_input,
20 attention_mask=attention_mask,
21 max_new_tokens=100,
22 do_sample=False,
23 repetition_penalty=1.2,
24 pad_token_id=tokenizer.eos_token_id
25 )[0]
26 output = tokenizer.decode(outputs[tokenized_input.size(1):], skip_special_tokens=True)
27
28 results.append({"task_id": data["task_id"], "input": input, "output": output})
1import re
2jsonl_id = re.sub(".*/", "", new_model_id)
3with open(f"./{jsonl_id}-outputs.jsonl", 'w', encoding='utf-8') as f:
4 for result in results:
5 json.dump(result, f, ensure_ascii=False) # ensure_ascii=False for handling non-ASCII characters
6 f.write('\n')
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Carbon emissions can be estimated using the
Machine Learning Impact calculator presented in
Lacoste et al. (2019).
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