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intermezzo672
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NHS-roberta-binary-random

Text Classification
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roberta
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text-embeddings-inference
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Transformers library
# Gated model: Login with a HF token with gated access permission
hf auth login
# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-classification", model="intermezzo672/NHS-roberta-binary-random")
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("intermezzo672/NHS-roberta-binary-random")
model = AutoModelForSequenceClassification.from_pretrained("intermezzo672/NHS-roberta-binary-random")
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NHS-roberta-binary-random

This model is a fine-tuned version of roberta-base on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.5076
  • Accuracy: 0.7937
  • Precision: 0.7920
  • Recall: 0.8022
  • F1: 0.7915

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 3e-05
  • train_batch_size: 16
  • eval_batch_size: 16
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 6

Training results

Training Loss Epoch Step Validation Loss Accuracy Precision Recall F1
0.0996 1.0 397 0.4221 0.8088 0.8018 0.8041 0.8029
0.0996 2.0 794 0.4597 0.7861 0.7913 0.8009 0.7851
1.9859 3.0 1191 0.5076 0.7937 0.7920 0.8022 0.7915

Framework versions

  • Transformers 4.38.2
  • Pytorch 2.2.1+cu121
  • Datasets 2.18.0
  • Tokenizers 0.15.2
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