Text Classification
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distilbert
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Eval Results (legacy)
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Hartunka/bert_base_rand_100_v2_rte")
model = AutoModelForSequenceClassification.from_pretrained("Hartunka/bert_base_rand_100_v2_rte")Quick Links
bert_base_rand_100_v2_rte
This model is a fine-tuned version of Hartunka/bert_base_rand_100_v2 on the GLUE RTE dataset. It achieves the following results on the evaluation set:
- Loss: 0.7022
- Accuracy: 0.4729
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: 256
- eval_batch_size: 256
- seed: 10
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 50
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.7068 | 1.0 | 10 | 0.7022 | 0.4729 |
| 0.6917 | 2.0 | 20 | 0.7030 | 0.5018 |
| 0.6548 | 3.0 | 30 | 0.7343 | 0.5415 |
| 0.5524 | 4.0 | 40 | 0.9991 | 0.5235 |
| 0.3992 | 5.0 | 50 | 1.0804 | 0.5126 |
| 0.3246 | 6.0 | 60 | 1.4221 | 0.5090 |
Framework versions
- Transformers 4.50.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.21.1
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Model tree for Hartunka/bert_base_rand_100_v2_rte
Base model
Hartunka/bert_base_rand_100_v2Dataset used to train Hartunka/bert_base_rand_100_v2_rte
Evaluation results
- Accuracy on GLUE RTEself-reported0.473
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Hartunka/bert_base_rand_100_v2_rte")