Text Classification
Transformers
TensorBoard
Safetensors
English
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use Hartunka/tiny_bert_rand_50_v2_sst2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hartunka/tiny_bert_rand_50_v2_sst2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Hartunka/tiny_bert_rand_50_v2_sst2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Hartunka/tiny_bert_rand_50_v2_sst2") model = AutoModelForSequenceClassification.from_pretrained("Hartunka/tiny_bert_rand_50_v2_sst2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Hartunka/tiny_bert_rand_50_v2_sst2: direct link, hf CLI and curl.
- Browser
- Download file 2.06 kB
-
https://huggingface.co/Hartunka/tiny_bert_rand_50_v2_sst2/resolve/main/README.md
- Command line
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hf download hf://Hartunka/tiny_bert_rand_50_v2_sst2/README.md
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curl -L -o README.md https://huggingface.co/Hartunka/tiny_bert_rand_50_v2_sst2/resolve/main/README.md
2.06 kB
| library_name: transformers | |
| language: | |
| - en | |
| base_model: Hartunka/tiny_bert_rand_50_v2 | |
| tags: | |
| - generated_from_trainer | |
| datasets: | |
| - glue | |
| metrics: | |
| - accuracy | |
| model-index: | |
| - name: tiny_bert_rand_50_v2_sst2 | |
| results: | |
| - task: | |
| name: Text Classification | |
| type: text-classification | |
| dataset: | |
| name: GLUE SST2 | |
| type: glue | |
| args: sst2 | |
| metrics: | |
| - name: Accuracy | |
| type: accuracy | |
| value: 0.8096330275229358 | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # tiny_bert_rand_50_v2_sst2 | |
| This model is a fine-tuned version of [Hartunka/tiny_bert_rand_50_v2](https://huggingface.co/Hartunka/tiny_bert_rand_50_v2) on the GLUE SST2 dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.4656 | |
| - Accuracy: 0.8096 | |
| ## 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 | | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:| | |
| | 0.4222 | 1.0 | 264 | 0.4656 | 0.8096 | | |
| | 0.2394 | 2.0 | 528 | 0.5868 | 0.7913 | | |
| | 0.1886 | 3.0 | 792 | 0.5450 | 0.7913 | | |
| | 0.1585 | 4.0 | 1056 | 0.6403 | 0.7947 | | |
| | 0.1306 | 5.0 | 1320 | 0.6642 | 0.7856 | | |
| | 0.1116 | 6.0 | 1584 | 0.7983 | 0.7810 | | |
| ### Framework versions | |
| - Transformers 4.50.2 | |
| - Pytorch 2.2.1+cu121 | |
| - Datasets 2.18.0 | |
| - Tokenizers 0.21.1 | |