# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("ilaria-oneofftech/ikitracks_netzero")
model = AutoModelForSequenceClassification.from_pretrained("ilaria-oneofftech/ikitracks_netzero", device_map="auto")Quick Links
ikitracks_netzero
This model is a fine-tuned version of bert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.5963
- F1: 0.8424
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: 3
- eval_batch_size: 3
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|---|---|---|---|---|
| 0.5967 | 1.0 | 109 | 0.6004 | 0.7168 |
| 0.3709 | 2.0 | 218 | 0.6017 | 0.8215 |
| 0.1412 | 3.0 | 327 | 0.5071 | 0.8851 |
| 0.0604 | 4.0 | 436 | 0.5599 | 0.8851 |
| 0.0365 | 5.0 | 545 | 0.5963 | 0.8424 |
Framework versions
- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ilaria-oneofftech/ikitracks_netzero")