# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("parhamabedazad/results")
model = AutoModelForSequenceClassification.from_pretrained("parhamabedazad/results")Quick Links
results
This model is a fine-tuned version of cardiffnlp/twitter-xlm-roberta-base on the None dataset. It achieves the following results on the evaluation set:
- eval_loss: 0.3651
- eval_label_0_f1: 0.7305
- eval_label_0_accuracy: 0.9498
- eval_label_1_f1: 0.8458
- eval_label_1_accuracy: 0.8573
- eval_label_2_f1: 0.7816
- eval_label_2_accuracy: 0.9576
- eval_label_3_f1: 0.7638
- eval_label_3_accuracy: 0.9097
- eval_label_4_f1: 0.6438
- eval_label_4_accuracy: 0.9420
- eval_label_5_f1: 0.6857
- eval_label_5_accuracy: 0.9755
- eval_runtime: 5.7337
- eval_samples_per_second: 156.444
- eval_steps_per_second: 5.058
- epoch: 5.0
- step: 795
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: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
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Model tree for parhamabedazad/results
Base model
cardiffnlp/twitter-xlm-roberta-base
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="parhamabedazad/results")