Text Classification
Transformers
TensorBoard
Safetensors
English
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use Hartunka/bert_base_rand_100_v2_stsb with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hartunka/bert_base_rand_100_v2_stsb with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Hartunka/bert_base_rand_100_v2_stsb")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Hartunka/bert_base_rand_100_v2_stsb") model = AutoModelForSequenceClassification.from_pretrained("Hartunka/bert_base_rand_100_v2_stsb", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Hartunka/bert_base_rand_100_v2_stsb: direct link, hf CLI and curl.
- Browser
- Download file 2.51 kB
-
https://huggingface.co/Hartunka/bert_base_rand_100_v2_stsb/resolve/main/README.md
- Command line
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hf download hf://Hartunka/bert_base_rand_100_v2_stsb/README.md
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curl -L -o README.md https://huggingface.co/Hartunka/bert_base_rand_100_v2_stsb/resolve/main/README.md
2.51 kB
| library_name: transformers | |
| language: | |
| - en | |
| base_model: Hartunka/bert_base_rand_100_v2 | |
| tags: | |
| - generated_from_trainer | |
| datasets: | |
| - glue | |
| metrics: | |
| - spearmanr | |
| model-index: | |
| - name: bert_base_rand_100_v2_stsb | |
| results: | |
| - task: | |
| name: Text Classification | |
| type: text-classification | |
| dataset: | |
| name: GLUE STSB | |
| type: glue | |
| args: stsb | |
| metrics: | |
| - name: Spearmanr | |
| type: spearmanr | |
| value: 0.21780785979027786 | |
| <!-- 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. --> | |
| # bert_base_rand_100_v2_stsb | |
| This model is a fine-tuned version of [Hartunka/bert_base_rand_100_v2](https://huggingface.co/Hartunka/bert_base_rand_100_v2) on the GLUE STSB dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 2.4307 | |
| - Pearson: 0.2244 | |
| - Spearmanr: 0.2178 | |
| - Combined Score: 0.2211 | |
| ## 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 | Pearson | Spearmanr | Combined Score | | |
| |:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:|:--------------:| | |
| | 2.8477 | 1.0 | 23 | 2.5026 | 0.0881 | 0.0800 | 0.0840 | | |
| | 1.9428 | 2.0 | 46 | 2.4721 | 0.1475 | 0.1358 | 0.1417 | | |
| | 1.6984 | 3.0 | 69 | 2.4307 | 0.2244 | 0.2178 | 0.2211 | | |
| | 1.3566 | 4.0 | 92 | 2.5091 | 0.2426 | 0.2407 | 0.2416 | | |
| | 1.0597 | 5.0 | 115 | 2.5674 | 0.2486 | 0.2424 | 0.2455 | | |
| | 0.8102 | 6.0 | 138 | 2.6909 | 0.2713 | 0.2666 | 0.2690 | | |
| | 0.6127 | 7.0 | 161 | 2.7525 | 0.2639 | 0.2592 | 0.2616 | | |
| | 0.4965 | 8.0 | 184 | 2.8618 | 0.2606 | 0.2565 | 0.2586 | | |
| ### Framework versions | |
| - Transformers 4.50.2 | |
| - Pytorch 2.2.1+cu121 | |
| - Datasets 2.18.0 | |
| - Tokenizers 0.21.1 | |