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_mnli")
model = AutoModelForSequenceClassification.from_pretrained("Hartunka/bert_base_rand_100_v2_mnli")Quick Links
bert_base_rand_100_v2_mnli
This model is a fine-tuned version of Hartunka/bert_base_rand_100_v2 on the GLUE MNLI dataset. It achieves the following results on the evaluation set:
- Loss: 0.7597
- Accuracy: 0.6743
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 |
|---|---|---|---|---|
| 0.9712 | 1.0 | 1534 | 0.8988 | 0.5824 |
| 0.861 | 2.0 | 3068 | 0.8350 | 0.6276 |
| 0.769 | 3.0 | 4602 | 0.7970 | 0.6504 |
| 0.6896 | 4.0 | 6136 | 0.7633 | 0.6661 |
| 0.6191 | 5.0 | 7670 | 0.7852 | 0.6735 |
| 0.5467 | 6.0 | 9204 | 0.8340 | 0.6729 |
| 0.4721 | 7.0 | 10738 | 0.8675 | 0.6770 |
| 0.4013 | 8.0 | 12272 | 0.9629 | 0.6663 |
| 0.3355 | 9.0 | 13806 | 1.0930 | 0.6595 |
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_mnli
Base model
Hartunka/bert_base_rand_100_v2Dataset used to train Hartunka/bert_base_rand_100_v2_mnli
Evaluation results
- Accuracy on GLUE MNLIself-reported0.674
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Hartunka/bert_base_rand_100_v2_mnli")