Text Classification
PEFT
Joblib
ONNX
Safetensors
Transformers
English
distilbert
lora
text-embeddings-inference
Instructions to use iam-tsr/feedback-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use iam-tsr/feedback-classifier with PEFT:
Task type is invalid.
- Transformers
How to use iam-tsr/feedback-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="iam-tsr/feedback-classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("iam-tsr/feedback-classifier") model = AutoModelForSequenceClassification.from_pretrained("iam-tsr/feedback-classifier") - Notebooks
- Google Colab
- Kaggle
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# results
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This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on
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It achieves the following results on the evaluation set:
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- Loss: 0.1520
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- Accuracy: 0.9423
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# results
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This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on [employ feedback dataset](iam-tsr/employ_fdbk).
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It achieves the following results on the evaluation set:
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- Loss: 0.1520
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- Accuracy: 0.9423
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