yizhao-risk-en-scorer

Name: yizhao-risk-en-scorer
Author: HIT-TMG

license:apache-2.0

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Quick Summary

yizhao-risk-en-scorer Introduction This is a BERT model fine-tuned on a high-quality English financial dataset.

Code Examples

Quickstartpythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification

text = "You are a smart robot"
risk_model_name = "risk-model-en-v0.1"

risk_tokenizer = AutoTokenizer.from_pretrained(risk_model_name)
risk_model = AutoModelForSequenceClassification.from_pretrained(risk_model_name)

risk_inputs = risk_tokenizer(text, return_tensors="pt", padding="longest", truncation=True)
risk_outputs = risk_model(**risk_inputs)
risk_logits = risk_outputs.logits.squeeze(-1).float().detach().numpy()

risk_score = risk_logits.item()

result = {
    "text": text,
    "risk_score": risk_score
}

print(result)
# {'text': 'You are a smart robot', 'risk_score': 0.11226219683885574}

Quickstartpythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification

text = "You are a smart robot"
risk_model_name = "risk-model-en-v0.1"

risk_tokenizer = AutoTokenizer.from_pretrained(risk_model_name)
risk_model = AutoModelForSequenceClassification.from_pretrained(risk_model_name)

risk_inputs = risk_tokenizer(text, return_tensors="pt", padding="longest", truncation=True)
risk_outputs = risk_model(**risk_inputs)
risk_logits = risk_outputs.logits.squeeze(-1).float().detach().numpy()

risk_score = risk_logits.item()

result = {
    "text": text,
    "risk_score": risk_score
}

print(result)
# {'text': 'You are a smart robot', 'risk_score': 0.11226219683885574}

Quickstartpythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification

text = "You are a smart robot"
risk_model_name = "risk-model-en-v0.1"

risk_tokenizer = AutoTokenizer.from_pretrained(risk_model_name)
risk_model = AutoModelForSequenceClassification.from_pretrained(risk_model_name)

risk_inputs = risk_tokenizer(text, return_tensors="pt", padding="longest", truncation=True)
risk_outputs = risk_model(**risk_inputs)
risk_logits = risk_outputs.logits.squeeze(-1).float().detach().numpy()

risk_score = risk_logits.item()

result = {
    "text": text,
    "risk_score": risk_score
}

print(result)
# {'text': 'You are a smart robot', 'risk_score': 0.11226219683885574}

Quickstartpythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification

text = "You are a smart robot"
risk_model_name = "risk-model-en-v0.1"

risk_tokenizer = AutoTokenizer.from_pretrained(risk_model_name)
risk_model = AutoModelForSequenceClassification.from_pretrained(risk_model_name)

risk_inputs = risk_tokenizer(text, return_tensors="pt", padding="longest", truncation=True)
risk_outputs = risk_model(**risk_inputs)
risk_logits = risk_outputs.logits.squeeze(-1).float().detach().numpy()

risk_score = risk_logits.item()

result = {
    "text": text,
    "risk_score": risk_score
}

print(result)
# {'text': 'You are a smart robot', 'risk_score': 0.11226219683885574}

Quickstartpythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification

text = "You are a smart robot"
risk_model_name = "risk-model-en-v0.1"

risk_tokenizer = AutoTokenizer.from_pretrained(risk_model_name)
risk_model = AutoModelForSequenceClassification.from_pretrained(risk_model_name)

risk_inputs = risk_tokenizer(text, return_tensors="pt", padding="longest", truncation=True)
risk_outputs = risk_model(**risk_inputs)
risk_logits = risk_outputs.logits.squeeze(-1).float().detach().numpy()

risk_score = risk_logits.item()

result = {
    "text": text,
    "risk_score": risk_score
}

print(result)
# {'text': 'You are a smart robot', 'risk_score': 0.11226219683885574}

Quickstartpythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification

text = "You are a smart robot"
risk_model_name = "risk-model-en-v0.1"

risk_tokenizer = AutoTokenizer.from_pretrained(risk_model_name)
risk_model = AutoModelForSequenceClassification.from_pretrained(risk_model_name)

risk_inputs = risk_tokenizer(text, return_tensors="pt", padding="longest", truncation=True)
risk_outputs = risk_model(**risk_inputs)
risk_logits = risk_outputs.logits.squeeze(-1).float().detach().numpy()

risk_score = risk_logits.item()

result = {
    "text": text,
    "risk_score": risk_score
}

print(result)
# {'text': 'You are a smart robot', 'risk_score': 0.11226219683885574}

Quickstartpythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification

text = "You are a smart robot"
risk_model_name = "risk-model-en-v0.1"

risk_tokenizer = AutoTokenizer.from_pretrained(risk_model_name)
risk_model = AutoModelForSequenceClassification.from_pretrained(risk_model_name)

risk_inputs = risk_tokenizer(text, return_tensors="pt", padding="longest", truncation=True)
risk_outputs = risk_model(**risk_inputs)
risk_logits = risk_outputs.logits.squeeze(-1).float().detach().numpy()

risk_score = risk_logits.item()

result = {
    "text": text,
    "risk_score": risk_score
}

print(result)
# {'text': 'You are a smart robot', 'risk_score': 0.11226219683885574}

Quickstartpythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification

text = "You are a smart robot"
risk_model_name = "risk-model-en-v0.1"

risk_tokenizer = AutoTokenizer.from_pretrained(risk_model_name)
risk_model = AutoModelForSequenceClassification.from_pretrained(risk_model_name)

risk_inputs = risk_tokenizer(text, return_tensors="pt", padding="longest", truncation=True)
risk_outputs = risk_model(**risk_inputs)
risk_logits = risk_outputs.logits.squeeze(-1).float().detach().numpy()

risk_score = risk_logits.item()

result = {
    "text": text,
    "risk_score": risk_score
}

print(result)
# {'text': 'You are a smart robot', 'risk_score': 0.11226219683885574}

Quickstartpythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification

text = "You are a smart robot"
risk_model_name = "risk-model-en-v0.1"

risk_tokenizer = AutoTokenizer.from_pretrained(risk_model_name)
risk_model = AutoModelForSequenceClassification.from_pretrained(risk_model_name)

risk_inputs = risk_tokenizer(text, return_tensors="pt", padding="longest", truncation=True)
risk_outputs = risk_model(**risk_inputs)
risk_logits = risk_outputs.logits.squeeze(-1).float().detach().numpy()

risk_score = risk_logits.item()

result = {
    "text": text,
    "risk_score": risk_score
}

print(result)
# {'text': 'You are a smart robot', 'risk_score': 0.11226219683885574}

Quickstartpythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification

text = "You are a smart robot"
risk_model_name = "risk-model-en-v0.1"

risk_tokenizer = AutoTokenizer.from_pretrained(risk_model_name)
risk_model = AutoModelForSequenceClassification.from_pretrained(risk_model_name)

risk_inputs = risk_tokenizer(text, return_tensors="pt", padding="longest", truncation=True)
risk_outputs = risk_model(**risk_inputs)
risk_logits = risk_outputs.logits.squeeze(-1).float().detach().numpy()

risk_score = risk_logits.item()

result = {
    "text": text,
    "risk_score": risk_score
}

print(result)
# {'text': 'You are a smart robot', 'risk_score': 0.11226219683885574}

Quickstartpythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification

text = "You are a smart robot"
risk_model_name = "risk-model-en-v0.1"

risk_tokenizer = AutoTokenizer.from_pretrained(risk_model_name)
risk_model = AutoModelForSequenceClassification.from_pretrained(risk_model_name)

risk_inputs = risk_tokenizer(text, return_tensors="pt", padding="longest", truncation=True)
risk_outputs = risk_model(**risk_inputs)
risk_logits = risk_outputs.logits.squeeze(-1).float().detach().numpy()

risk_score = risk_logits.item()

result = {
    "text": text,
    "risk_score": risk_score
}

print(result)
# {'text': 'You are a smart robot', 'risk_score': 0.11226219683885574}

Quickstartpythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification

text = "You are a smart robot"
risk_model_name = "risk-model-en-v0.1"

risk_tokenizer = AutoTokenizer.from_pretrained(risk_model_name)
risk_model = AutoModelForSequenceClassification.from_pretrained(risk_model_name)

risk_inputs = risk_tokenizer(text, return_tensors="pt", padding="longest", truncation=True)
risk_outputs = risk_model(**risk_inputs)
risk_logits = risk_outputs.logits.squeeze(-1).float().detach().numpy()

risk_score = risk_logits.item()

result = {
    "text": text,
    "risk_score": risk_score
}

print(result)
# {'text': 'You are a smart robot', 'risk_score': 0.11226219683885574}

Quickstartpythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification

text = "You are a smart robot"
risk_model_name = "risk-model-en-v0.1"

risk_tokenizer = AutoTokenizer.from_pretrained(risk_model_name)
risk_model = AutoModelForSequenceClassification.from_pretrained(risk_model_name)

risk_inputs = risk_tokenizer(text, return_tensors="pt", padding="longest", truncation=True)
risk_outputs = risk_model(**risk_inputs)
risk_logits = risk_outputs.logits.squeeze(-1).float().detach().numpy()

risk_score = risk_logits.item()

result = {
    "text": text,
    "risk_score": risk_score
}

print(result)
# {'text': 'You are a smart robot', 'risk_score': 0.11226219683885574}

Quickstartpythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification

text = "You are a smart robot"
risk_model_name = "risk-model-en-v0.1"

risk_tokenizer = AutoTokenizer.from_pretrained(risk_model_name)
risk_model = AutoModelForSequenceClassification.from_pretrained(risk_model_name)

risk_inputs = risk_tokenizer(text, return_tensors="pt", padding="longest", truncation=True)
risk_outputs = risk_model(**risk_inputs)
risk_logits = risk_outputs.logits.squeeze(-1).float().detach().numpy()

risk_score = risk_logits.item()

result = {
    "text": text,
    "risk_score": risk_score
}

print(result)
# {'text': 'You are a smart robot', 'risk_score': 0.11226219683885574}

Quickstartpythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification

text = "You are a smart robot"
risk_model_name = "risk-model-en-v0.1"

risk_tokenizer = AutoTokenizer.from_pretrained(risk_model_name)
risk_model = AutoModelForSequenceClassification.from_pretrained(risk_model_name)

risk_inputs = risk_tokenizer(text, return_tensors="pt", padding="longest", truncation=True)
risk_outputs = risk_model(**risk_inputs)
risk_logits = risk_outputs.logits.squeeze(-1).float().detach().numpy()

risk_score = risk_logits.item()

result = {
    "text": text,
    "risk_score": risk_score
}

print(result)
# {'text': 'You are a smart robot', 'risk_score': 0.11226219683885574}

Quickstartpythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification

text = "You are a smart robot"
risk_model_name = "risk-model-en-v0.1"

risk_tokenizer = AutoTokenizer.from_pretrained(risk_model_name)
risk_model = AutoModelForSequenceClassification.from_pretrained(risk_model_name)

risk_inputs = risk_tokenizer(text, return_tensors="pt", padding="longest", truncation=True)
risk_outputs = risk_model(**risk_inputs)
risk_logits = risk_outputs.logits.squeeze(-1).float().detach().numpy()

risk_score = risk_logits.item()

result = {
    "text": text,
    "risk_score": risk_score
}

print(result)
# {'text': 'You are a smart robot', 'risk_score': 0.11226219683885574}

Quickstartpythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification

text = "You are a smart robot"
risk_model_name = "risk-model-en-v0.1"

risk_tokenizer = AutoTokenizer.from_pretrained(risk_model_name)
risk_model = AutoModelForSequenceClassification.from_pretrained(risk_model_name)

risk_inputs = risk_tokenizer(text, return_tensors="pt", padding="longest", truncation=True)
risk_outputs = risk_model(**risk_inputs)
risk_logits = risk_outputs.logits.squeeze(-1).float().detach().numpy()

risk_score = risk_logits.item()

result = {
    "text": text,
    "risk_score": risk_score
}

print(result)
# {'text': 'You are a smart robot', 'risk_score': 0.11226219683885574}

Quickstartpythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification

text = "You are a smart robot"
risk_model_name = "risk-model-en-v0.1"

risk_tokenizer = AutoTokenizer.from_pretrained(risk_model_name)
risk_model = AutoModelForSequenceClassification.from_pretrained(risk_model_name)

risk_inputs = risk_tokenizer(text, return_tensors="pt", padding="longest", truncation=True)
risk_outputs = risk_model(**risk_inputs)
risk_logits = risk_outputs.logits.squeeze(-1).float().detach().numpy()

risk_score = risk_logits.item()

result = {
    "text": text,
    "risk_score": risk_score
}

print(result)
# {'text': 'You are a smart robot', 'risk_score': 0.11226219683885574}

Quickstartpythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification

text = "You are a smart robot"
risk_model_name = "risk-model-en-v0.1"

risk_tokenizer = AutoTokenizer.from_pretrained(risk_model_name)
risk_model = AutoModelForSequenceClassification.from_pretrained(risk_model_name)

risk_inputs = risk_tokenizer(text, return_tensors="pt", padding="longest", truncation=True)
risk_outputs = risk_model(**risk_inputs)
risk_logits = risk_outputs.logits.squeeze(-1).float().detach().numpy()

risk_score = risk_logits.item()

result = {
    "text": text,
    "risk_score": risk_score
}

print(result)
# {'text': 'You are a smart robot', 'risk_score': 0.11226219683885574}

Quickstartpythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification

text = "You are a smart robot"
risk_model_name = "risk-model-en-v0.1"

risk_tokenizer = AutoTokenizer.from_pretrained(risk_model_name)
risk_model = AutoModelForSequenceClassification.from_pretrained(risk_model_name)

risk_inputs = risk_tokenizer(text, return_tensors="pt", padding="longest", truncation=True)
risk_outputs = risk_model(**risk_inputs)
risk_logits = risk_outputs.logits.squeeze(-1).float().detach().numpy()

risk_score = risk_logits.item()

result = {
    "text": text,
    "risk_score": risk_score
}

print(result)
# {'text': 'You are a smart robot', 'risk_score': 0.11226219683885574}

Quickstartpythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification

text = "You are a smart robot"
risk_model_name = "risk-model-en-v0.1"

risk_tokenizer = AutoTokenizer.from_pretrained(risk_model_name)
risk_model = AutoModelForSequenceClassification.from_pretrained(risk_model_name)

risk_inputs = risk_tokenizer(text, return_tensors="pt", padding="longest", truncation=True)
risk_outputs = risk_model(**risk_inputs)
risk_logits = risk_outputs.logits.squeeze(-1).float().detach().numpy()

risk_score = risk_logits.item()

result = {
    "text": text,
    "risk_score": risk_score
}

print(result)
# {'text': 'You are a smart robot', 'risk_score': 0.11226219683885574}

Quickstartpythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification

text = "You are a smart robot"
risk_model_name = "risk-model-en-v0.1"

risk_tokenizer = AutoTokenizer.from_pretrained(risk_model_name)
risk_model = AutoModelForSequenceClassification.from_pretrained(risk_model_name)

risk_inputs = risk_tokenizer(text, return_tensors="pt", padding="longest", truncation=True)
risk_outputs = risk_model(**risk_inputs)
risk_logits = risk_outputs.logits.squeeze(-1).float().detach().numpy()

risk_score = risk_logits.item()

result = {
    "text": text,
    "risk_score": risk_score
}

print(result)
# {'text': 'You are a smart robot', 'risk_score': 0.11226219683885574}

Quickstartpythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification

text = "You are a smart robot"
risk_model_name = "risk-model-en-v0.1"

risk_tokenizer = AutoTokenizer.from_pretrained(risk_model_name)
risk_model = AutoModelForSequenceClassification.from_pretrained(risk_model_name)

risk_inputs = risk_tokenizer(text, return_tensors="pt", padding="longest", truncation=True)
risk_outputs = risk_model(**risk_inputs)
risk_logits = risk_outputs.logits.squeeze(-1).float().detach().numpy()

risk_score = risk_logits.item()

result = {
    "text": text,
    "risk_score": risk_score
}

print(result)
# {'text': 'You are a smart robot', 'risk_score': 0.11226219683885574}

Quickstartpythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification

text = "You are a smart robot"
risk_model_name = "risk-model-en-v0.1"

risk_tokenizer = AutoTokenizer.from_pretrained(risk_model_name)
risk_model = AutoModelForSequenceClassification.from_pretrained(risk_model_name)

risk_inputs = risk_tokenizer(text, return_tensors="pt", padding="longest", truncation=True)
risk_outputs = risk_model(**risk_inputs)
risk_logits = risk_outputs.logits.squeeze(-1).float().detach().numpy()

risk_score = risk_logits.item()

result = {
    "text": text,
    "risk_score": risk_score
}

print(result)
# {'text': 'You are a smart robot', 'risk_score': 0.11226219683885574}

Quickstartpythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification

text = "You are a smart robot"
risk_model_name = "risk-model-en-v0.1"

risk_tokenizer = AutoTokenizer.from_pretrained(risk_model_name)
risk_model = AutoModelForSequenceClassification.from_pretrained(risk_model_name)

risk_inputs = risk_tokenizer(text, return_tensors="pt", padding="longest", truncation=True)
risk_outputs = risk_model(**risk_inputs)
risk_logits = risk_outputs.logits.squeeze(-1).float().detach().numpy()

risk_score = risk_logits.item()

result = {
    "text": text,
    "risk_score": risk_score
}

print(result)
# {'text': 'You are a smart robot', 'risk_score': 0.11226219683885574}

Quickstartpythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification

text = "You are a smart robot"
risk_model_name = "risk-model-en-v0.1"

risk_tokenizer = AutoTokenizer.from_pretrained(risk_model_name)
risk_model = AutoModelForSequenceClassification.from_pretrained(risk_model_name)

risk_inputs = risk_tokenizer(text, return_tensors="pt", padding="longest", truncation=True)
risk_outputs = risk_model(**risk_inputs)
risk_logits = risk_outputs.logits.squeeze(-1).float().detach().numpy()

risk_score = risk_logits.item()

result = {
    "text": text,
    "risk_score": risk_score
}

print(result)
# {'text': 'You are a smart robot', 'risk_score': 0.11226219683885574}

Quickstartpythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification

text = "You are a smart robot"
risk_model_name = "risk-model-en-v0.1"

risk_tokenizer = AutoTokenizer.from_pretrained(risk_model_name)
risk_model = AutoModelForSequenceClassification.from_pretrained(risk_model_name)

risk_inputs = risk_tokenizer(text, return_tensors="pt", padding="longest", truncation=True)
risk_outputs = risk_model(**risk_inputs)
risk_logits = risk_outputs.logits.squeeze(-1).float().detach().numpy()

risk_score = risk_logits.item()

result = {
    "text": text,
    "risk_score": risk_score
}

print(result)
# {'text': 'You are a smart robot', 'risk_score': 0.11226219683885574}

Quickstartpythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification

text = "You are a smart robot"
risk_model_name = "risk-model-en-v0.1"

risk_tokenizer = AutoTokenizer.from_pretrained(risk_model_name)
risk_model = AutoModelForSequenceClassification.from_pretrained(risk_model_name)

risk_inputs = risk_tokenizer(text, return_tensors="pt", padding="longest", truncation=True)
risk_outputs = risk_model(**risk_inputs)
risk_logits = risk_outputs.logits.squeeze(-1).float().detach().numpy()

risk_score = risk_logits.item()

result = {
    "text": text,
    "risk_score": risk_score
}

print(result)
# {'text': 'You are a smart robot', 'risk_score': 0.11226219683885574}

Quickstartpythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification

text = "You are a smart robot"
risk_model_name = "risk-model-en-v0.1"

risk_tokenizer = AutoTokenizer.from_pretrained(risk_model_name)
risk_model = AutoModelForSequenceClassification.from_pretrained(risk_model_name)

risk_inputs = risk_tokenizer(text, return_tensors="pt", padding="longest", truncation=True)
risk_outputs = risk_model(**risk_inputs)
risk_logits = risk_outputs.logits.squeeze(-1).float().detach().numpy()

risk_score = risk_logits.item()

result = {
    "text": text,
    "risk_score": risk_score
}

print(result)
# {'text': 'You are a smart robot', 'risk_score': 0.11226219683885574}

Quickstartpythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification

text = "You are a smart robot"
risk_model_name = "risk-model-en-v0.1"

risk_tokenizer = AutoTokenizer.from_pretrained(risk_model_name)
risk_model = AutoModelForSequenceClassification.from_pretrained(risk_model_name)

risk_inputs = risk_tokenizer(text, return_tensors="pt", padding="longest", truncation=True)
risk_outputs = risk_model(**risk_inputs)
risk_logits = risk_outputs.logits.squeeze(-1).float().detach().numpy()

risk_score = risk_logits.item()

result = {
    "text": text,
    "risk_score": risk_score
}

print(result)
# {'text': 'You are a smart robot', 'risk_score': 0.11226219683885574}

Quickstartpythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification

text = "You are a smart robot"
risk_model_name = "risk-model-en-v0.1"

risk_tokenizer = AutoTokenizer.from_pretrained(risk_model_name)
risk_model = AutoModelForSequenceClassification.from_pretrained(risk_model_name)

risk_inputs = risk_tokenizer(text, return_tensors="pt", padding="longest", truncation=True)
risk_outputs = risk_model(**risk_inputs)
risk_logits = risk_outputs.logits.squeeze(-1).float().detach().numpy()

risk_score = risk_logits.item()

result = {
    "text": text,
    "risk_score": risk_score
}

print(result)
# {'text': 'You are a smart robot', 'risk_score': 0.11226219683885574}

Quickstartpythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification

text = "You are a smart robot"
risk_model_name = "risk-model-en-v0.1"

risk_tokenizer = AutoTokenizer.from_pretrained(risk_model_name)
risk_model = AutoModelForSequenceClassification.from_pretrained(risk_model_name)

risk_inputs = risk_tokenizer(text, return_tensors="pt", padding="longest", truncation=True)
risk_outputs = risk_model(**risk_inputs)
risk_logits = risk_outputs.logits.squeeze(-1).float().detach().numpy()

risk_score = risk_logits.item()

result = {
    "text": text,
    "risk_score": risk_score
}

print(result)
# {'text': 'You are a smart robot', 'risk_score': 0.11226219683885574}

Quickstartpythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification

text = "You are a smart robot"
risk_model_name = "risk-model-en-v0.1"

risk_tokenizer = AutoTokenizer.from_pretrained(risk_model_name)
risk_model = AutoModelForSequenceClassification.from_pretrained(risk_model_name)

risk_inputs = risk_tokenizer(text, return_tensors="pt", padding="longest", truncation=True)
risk_outputs = risk_model(**risk_inputs)
risk_logits = risk_outputs.logits.squeeze(-1).float().detach().numpy()

risk_score = risk_logits.item()

result = {
    "text": text,
    "risk_score": risk_score
}

print(result)
# {'text': 'You are a smart robot', 'risk_score': 0.11226219683885574}

Quickstartpythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification

text = "You are a smart robot"
risk_model_name = "risk-model-en-v0.1"

risk_tokenizer = AutoTokenizer.from_pretrained(risk_model_name)
risk_model = AutoModelForSequenceClassification.from_pretrained(risk_model_name)

risk_inputs = risk_tokenizer(text, return_tensors="pt", padding="longest", truncation=True)
risk_outputs = risk_model(**risk_inputs)
risk_logits = risk_outputs.logits.squeeze(-1).float().detach().numpy()

risk_score = risk_logits.item()

result = {
    "text": text,
    "risk_score": risk_score
}

print(result)
# {'text': 'You are a smart robot', 'risk_score': 0.11226219683885574}

Quickstartpythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification

text = "You are a smart robot"
risk_model_name = "risk-model-en-v0.1"

risk_tokenizer = AutoTokenizer.from_pretrained(risk_model_name)
risk_model = AutoModelForSequenceClassification.from_pretrained(risk_model_name)

risk_inputs = risk_tokenizer(text, return_tensors="pt", padding="longest", truncation=True)
risk_outputs = risk_model(**risk_inputs)
risk_logits = risk_outputs.logits.squeeze(-1).float().detach().numpy()

risk_score = risk_logits.item()

result = {
    "text": text,
    "risk_score": risk_score
}

print(result)
# {'text': 'You are a smart robot', 'risk_score': 0.11226219683885574}

Quickstartpythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification

text = "You are a smart robot"
risk_model_name = "risk-model-en-v0.1"

risk_tokenizer = AutoTokenizer.from_pretrained(risk_model_name)
risk_model = AutoModelForSequenceClassification.from_pretrained(risk_model_name)

risk_inputs = risk_tokenizer(text, return_tensors="pt", padding="longest", truncation=True)
risk_outputs = risk_model(**risk_inputs)
risk_logits = risk_outputs.logits.squeeze(-1).float().detach().numpy()

risk_score = risk_logits.item()

result = {
    "text": text,
    "risk_score": risk_score
}

print(result)
# {'text': 'You are a smart robot', 'risk_score': 0.11226219683885574}

Quickstartpythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification

text = "You are a smart robot"
risk_model_name = "risk-model-en-v0.1"

risk_tokenizer = AutoTokenizer.from_pretrained(risk_model_name)
risk_model = AutoModelForSequenceClassification.from_pretrained(risk_model_name)

risk_inputs = risk_tokenizer(text, return_tensors="pt", padding="longest", truncation=True)
risk_outputs = risk_model(**risk_inputs)
risk_logits = risk_outputs.logits.squeeze(-1).float().detach().numpy()

risk_score = risk_logits.item()

result = {
    "text": text,
    "risk_score": risk_score
}

print(result)
# {'text': 'You are a smart robot', 'risk_score': 0.11226219683885574}

Quickstartpythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification

text = "You are a smart robot"
risk_model_name = "risk-model-en-v0.1"

risk_tokenizer = AutoTokenizer.from_pretrained(risk_model_name)
risk_model = AutoModelForSequenceClassification.from_pretrained(risk_model_name)

risk_inputs = risk_tokenizer(text, return_tensors="pt", padding="longest", truncation=True)
risk_outputs = risk_model(**risk_inputs)
risk_logits = risk_outputs.logits.squeeze(-1).float().detach().numpy()

risk_score = risk_logits.item()

result = {
    "text": text,
    "risk_score": risk_score
}

print(result)
# {'text': 'You are a smart robot', 'risk_score': 0.11226219683885574}

Quickstartpythontransformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification

text = "You are a smart robot"
risk_model_name = "risk-model-en-v0.1"

risk_tokenizer = AutoTokenizer.from_pretrained(risk_model_name)
risk_model = AutoModelForSequenceClassification.from_pretrained(risk_model_name)

risk_inputs = risk_tokenizer(text, return_tensors="pt", padding="longest", truncation=True)
risk_outputs = risk_model(**risk_inputs)
risk_logits = risk_outputs.logits.squeeze(-1).float().detach().numpy()

risk_score = risk_logits.item()

result = {
    "text": text,
    "risk_score": risk_score
}

print(result)
# {'text': 'You are a smart robot', 'risk_score': 0.11226219683885574}

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