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/tiny_bert_rand_20_v2_mnli")
model = AutoModelForSequenceClassification.from_pretrained("Hartunka/tiny_bert_rand_20_v2_mnli")Quick Links
tiny_bert_rand_20_v2_mnli
This model is a fine-tuned version of Hartunka/tiny_bert_rand_20_v2 on the GLUE MNLI dataset. It achieves the following results on the evaluation set:
- Loss: 0.8598
- Accuracy: 0.6247
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 |
|---|---|---|---|---|
| 0.9926 | 1.0 | 1534 | 0.9314 | 0.5507 |
| 0.9032 | 2.0 | 3068 | 0.8911 | 0.5824 |
| 0.8523 | 3.0 | 4602 | 0.8651 | 0.6032 |
| 0.8089 | 4.0 | 6136 | 0.8571 | 0.6096 |
| 0.7665 | 5.0 | 7670 | 0.8496 | 0.6182 |
| 0.7234 | 6.0 | 9204 | 0.8672 | 0.6244 |
| 0.6827 | 7.0 | 10738 | 0.8687 | 0.6287 |
| 0.6402 | 8.0 | 12272 | 0.9204 | 0.6143 |
| 0.6004 | 9.0 | 13806 | 0.9507 | 0.6229 |
| 0.5621 | 10.0 | 15340 | 0.9863 | 0.6298 |
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/tiny_bert_rand_20_v2_mnli
Base model
Hartunka/tiny_bert_rand_20_v2Dataset used to train Hartunka/tiny_bert_rand_20_v2_mnli
Evaluation results
- Accuracy on GLUE MNLIself-reported0.625
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Hartunka/tiny_bert_rand_20_v2_mnli")