Instructions to use SoumyaRanjan/bert-pretrained-text-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SoumyaRanjan/bert-pretrained-text-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="SoumyaRanjan/bert-pretrained-text-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("SoumyaRanjan/bert-pretrained-text-classification") model = AutoModelForSequenceClassification.from_pretrained("SoumyaRanjan/bert-pretrained-text-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,057 Bytes
9e37e43 48505cf 9e37e43 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 | # Environment variables
import os
MODEL_PATH = os.environ.get('MODEL_PATH', '/opt/ml/model')
# Load the model
def model_fn(model_dir):
model_file = os.path.join(model_dir, 'xgboost-model')
model = model
model.load_model(model_file)
return model
# Deserialize the input data
def input_fn(request_body, request_content_type):
if request_content_type == 'application/json':
input_data = json.loads(request_body)
return np.array(input_data['instances'])
else:
raise ValueError("Unsupported content type: {}".format(request_content_type))
# Serialize the output data
def output_fn(prediction, response_content_type):
if response_content_type == 'application/json':
response = json.dumps({'predictions': prediction.tolist()})
return response
else:
raise ValueError("Unsupported content type: {}".format(response_content_type))
# Make predictions
def predict_fn(input_data, model):
dmatrix = model.DMatrix(input_data)
prediction = model.predict(dmatrix)
return prediction |