Text Classification
Transformers
TensorBoard
Safetensors
English
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use Hartunka/tiny_bert_rand_5_v2_cola with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hartunka/tiny_bert_rand_5_v2_cola with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Hartunka/tiny_bert_rand_5_v2_cola")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Hartunka/tiny_bert_rand_5_v2_cola") model = AutoModelForSequenceClassification.from_pretrained("Hartunka/tiny_bert_rand_5_v2_cola", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Hartunka/tiny_bert_rand_5_v2_cola: direct link, hf CLI and curl.
- Browser
- Download file 2.56 kB
-
https://huggingface.co/Hartunka/tiny_bert_rand_5_v2_cola/resolve/main/README.md
- Command line
-
hf download hf://Hartunka/tiny_bert_rand_5_v2_cola/README.md
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curl -L -o README.md https://huggingface.co/Hartunka/tiny_bert_rand_5_v2_cola/resolve/main/README.md
2.56 kB
metadata
library_name: transformers
language:
- en
base_model: Hartunka/tiny_bert_rand_5_v2
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
- accuracy
model-index:
- name: tiny_bert_rand_5_v2_cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE COLA
type: glue
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.00705908192371065
- name: Accuracy
type: accuracy
value: 0.6855225563049316
tiny_bert_rand_5_v2_cola
This model is a fine-tuned version of Hartunka/tiny_bert_rand_5_v2 on the GLUE COLA dataset. It achieves the following results on the evaluation set:
- Loss: 0.6145
- Matthews Correlation: 0.0071
- Accuracy: 0.6855
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 | Matthews Correlation | Accuracy |
|---|---|---|---|---|---|
| 0.6181 | 1.0 | 34 | 0.6160 | 0.0 | 0.6913 |
| 0.603 | 2.0 | 68 | 0.6146 | 0.0 | 0.6913 |
| 0.5863 | 3.0 | 102 | 0.6145 | 0.0071 | 0.6855 |
| 0.5473 | 4.0 | 136 | 0.6423 | 0.1090 | 0.6874 |
| 0.5062 | 5.0 | 170 | 0.6451 | 0.1031 | 0.6663 |
| 0.4682 | 6.0 | 204 | 0.7057 | 0.0943 | 0.6596 |
| 0.4321 | 7.0 | 238 | 0.7519 | 0.0915 | 0.6357 |
| 0.4043 | 8.0 | 272 | 0.7743 | 0.0624 | 0.6232 |
Framework versions
- Transformers 4.50.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.21.1