Text Classification
Transformers
Safetensors
PyTorch
English
ticket_gpt
feature-extraction
gpt2
custom-architecture
tiktoken
custom_code
Instructions to use FarhanAK128/TicketClassificationGPT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FarhanAK128/TicketClassificationGPT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="FarhanAK128/TicketClassificationGPT", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("FarhanAK128/TicketClassificationGPT", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Update model_class.py
Browse files- model_class.py +1 -1
model_class.py
CHANGED
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@@ -174,7 +174,7 @@ class TicketGPT(
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logits = self.out_head(x) #[2,4,50257]
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return logits
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-
def predict(self, text, tokenizer, max_length=
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lookup = {
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0:"Hardware",
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1:"HR Support",
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logits = self.out_head(x) #[2,4,50257]
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return logits
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+
def predict(self, text, tokenizer, max_length=1024, pad_token_id=50256):
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lookup = {
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0:"Hardware",
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1:"HR Support",
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