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anvorja
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panoncology-RE-sp

Text Classification
Transformers
Safetensors
xlm-roberta
Generated from Trainer
text-embeddings-inference
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xet
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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="anvorja/panoncology-RE-sp")
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("anvorja/panoncology-RE-sp")
model = AutoModelForSequenceClassification.from_pretrained("anvorja/panoncology-RE-sp")
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panoncology-RE-sp

This model is a fine-tuned version of FacebookAI/xlm-roberta-large. It achieves the following results on the evaluation set:

  • Loss: 0.1095
  • F1 Macro: 0.9791
  • F1 Weighted: 0.9878
  • Precision Macro: 0.9834
  • Recall Macro: 0.9749
  • Accuracy: 0.9878

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 2e-05
  • train_batch_size: 16
  • eval_batch_size: 16
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 64
  • optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_steps: 0.1
  • num_epochs: 15
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss F1 Macro F1 Weighted Precision Macro Recall Macro Accuracy
6.4623 1.0 65 1.5947 0.1778 0.1949 0.1840 0.2552 0.3520
5.4292 2.0 130 1.0449 0.6247 0.7446 0.6485 0.7039 0.7408
1.8966 3.0 195 0.3466 0.8689 0.9233 0.8803 0.8765 0.9304
0.9579 4.0 260 0.2411 0.9352 0.9573 0.9319 0.9387 0.9574
0.5551 5.0 325 0.1844 0.9308 0.9528 0.9279 0.9346 0.9536
0.5579 6.0 390 0.2231 0.9218 0.9431 0.9051 0.9512 0.9381
0.2832 7.0 455 0.1940 0.9505 0.9666 0.9511 0.9504 0.9671
0.4714 8.0 520 0.1696 0.9655 0.9750 0.9608 0.9705 0.9749
0.1448 9.0 585 0.2455 0.9573 0.9705 0.9602 0.9553 0.9710
0.1025 10.0 650 0.2173 0.9637 0.9749 0.9605 0.9672 0.9749
0.1946 11.0 715 0.1978 0.9653 0.9770 0.9593 0.9717 0.9768

Framework versions

  • Transformers 5.9.0
  • Pytorch 2.11.0+cu128
  • Datasets 4.8.5
  • Tokenizers 0.22.2
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