Text Classification
Transformers
TensorBoard
Safetensors
English
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Hartunka/tiny_bert_rand_5_v2_mnli")
model = AutoModelForSequenceClassification.from_pretrained("Hartunka/tiny_bert_rand_5_v2_mnli")Quick Links
tiny_bert_rand_5_v2_mnli
This model is a fine-tuned version of Hartunka/tiny_bert_rand_5_v2 on the GLUE MNLI dataset. It achieves the following results on the evaluation set:
- Loss: 0.8521
- Accuracy: 0.6216
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 |
|---|---|---|---|---|
| 0.9894 | 1.0 | 1534 | 0.9260 | 0.5553 |
| 0.9044 | 2.0 | 3068 | 0.8912 | 0.5842 |
| 0.8553 | 3.0 | 4602 | 0.8659 | 0.5969 |
| 0.8096 | 4.0 | 6136 | 0.8656 | 0.6083 |
| 0.765 | 5.0 | 7670 | 0.8499 | 0.6164 |
| 0.7195 | 6.0 | 9204 | 0.8635 | 0.6300 |
| 0.6754 | 7.0 | 10738 | 0.8770 | 0.6363 |
| 0.6336 | 8.0 | 12272 | 0.9145 | 0.6287 |
| 0.5919 | 9.0 | 13806 | 0.9488 | 0.6296 |
| 0.5523 | 10.0 | 15340 | 0.9851 | 0.6293 |
Framework versions
- Transformers 4.50.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.21.1
- Downloads last month
- 4
Model tree for Hartunka/tiny_bert_rand_5_v2_mnli
Base model
Hartunka/tiny_bert_rand_5_v2Dataset used to train Hartunka/tiny_bert_rand_5_v2_mnli
Evaluation results
- Accuracy on GLUE MNLIself-reported0.622
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Hartunka/tiny_bert_rand_5_v2_mnli")