# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("AliAvd/results")
model = AutoModelForSequenceClassification.from_pretrained("AliAvd/results", device_map="auto")Quick Links
results
This model is a fine-tuned version of bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 2.0300
- Accuracy: 0.5894
- F1: 0.5891
- Precision: 0.5918
- Recall: 0.5894
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: 5e-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
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| 1.1618 | 1.0 | 367 | 1.0932 | 0.5433 | 0.5350 | 0.5951 | 0.5433 |
| 0.8085 | 2.0 | 734 | 1.0683 | 0.5806 | 0.5769 | 0.5769 | 0.5806 |
| 0.5055 | 3.0 | 1101 | 1.2485 | 0.5711 | 0.5728 | 0.5867 | 0.5711 |
| 0.1641 | 4.0 | 1468 | 1.7630 | 0.5925 | 0.5917 | 0.5916 | 0.5925 |
| 0.0525 | 5.0 | 1835 | 2.0300 | 0.5894 | 0.5891 | 0.5918 | 0.5894 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Tokenizers 0.19.1
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Model tree for AliAvd/results
Base model
google-bert/bert-base-uncased
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="AliAvd/results")