nyu-mll/glue
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How to use deathperminutV2/NLP_sequence_clasiffication with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="deathperminutV2/NLP_sequence_clasiffication") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("deathperminutV2/NLP_sequence_clasiffication")
model = AutoModelForSequenceClassification.from_pretrained("deathperminutV2/NLP_sequence_clasiffication")This model is a fine-tuned version of distilroberta-base on the glue and the mrpc datasets. It achieves the following results on the evaluation set:
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The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 0.5129 | 1.09 | 500 | 0.7246 | 0.8113 | 0.8679 |
| 0.3526 | 2.18 | 1000 | 0.5325 | 0.8505 | 0.8872 |