yizhao-fin-zh-scorer

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license:apache-2.0
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Quick Summary

yizhao-fin-zh-scorer Introduction This is a BERT model fine-tuned on a high-quality Chinese financial dataset.

Code Examples

Quickstartpythontransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification

text = "你是一个聪明的机器人"
fin_model_name = "fin-model-zh-v0.1"

fin_tokenizer = AutoTokenizer.from_pretrained(fin_model_name)
fin_model = AutoModelForSequenceClassification.from_pretrained(fin_model_name)

fin_inputs = fin_tokenizer(text, return_tensors="pt", padding="longest", truncation=True)
fin_outputs = fin_model(**fin_inputs)
fin_logits = fin_outputs.logits.squeeze(-1).float().detach().numpy()

fin_score = fin_logits.item()
result = {
    "text": text,
    "fin_score": fin_score,
    "fin_int_score": int(round(max(0, min(fin_score, 5))))
}

print(result)
# {'text': '你是一个聪明的机器人', 'fin_score': 0.3258197605609894, 'fin_int_score': 0}
Quickstartpythontransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification

text = "你是一个聪明的机器人"
fin_model_name = "fin-model-zh-v0.1"

fin_tokenizer = AutoTokenizer.from_pretrained(fin_model_name)
fin_model = AutoModelForSequenceClassification.from_pretrained(fin_model_name)

fin_inputs = fin_tokenizer(text, return_tensors="pt", padding="longest", truncation=True)
fin_outputs = fin_model(**fin_inputs)
fin_logits = fin_outputs.logits.squeeze(-1).float().detach().numpy()

fin_score = fin_logits.item()
result = {
    "text": text,
    "fin_score": fin_score,
    "fin_int_score": int(round(max(0, min(fin_score, 5))))
}

print(result)
# {'text': '你是一个聪明的机器人', 'fin_score': 0.3258197605609894, 'fin_int_score': 0}
Quickstartpythontransformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification

text = "你是一个聪明的机器人"
fin_model_name = "fin-model-zh-v0.1"

fin_tokenizer = AutoTokenizer.from_pretrained(fin_model_name)
fin_model = AutoModelForSequenceClassification.from_pretrained(fin_model_name)

fin_inputs = fin_tokenizer(text, return_tensors="pt", padding="longest", truncation=True)
fin_outputs = fin_model(**fin_inputs)
fin_logits = fin_outputs.logits.squeeze(-1).float().detach().numpy()

fin_score = fin_logits.item()
result = {
    "text": text,
    "fin_score": fin_score,
    "fin_int_score": int(round(max(0, min(fin_score, 5))))
}

print(result)
# {'text': '你是一个聪明的机器人', 'fin_score': 0.3258197605609894, 'fin_int_score': 0}

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