Text Classification
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distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Hartunka/distilbert_rand_10_v2_stsb")
model = AutoModelForSequenceClassification.from_pretrained("Hartunka/distilbert_rand_10_v2_stsb")Quick Links
distilbert_rand_10_v2_stsb
This model is a fine-tuned version of Hartunka/distilbert_rand_10_v2 on the GLUE STSB dataset. It achieves the following results on the evaluation set:
- Loss: 2.2101
- Pearson: 0.3094
- Spearmanr: 0.3015
- Combined Score: 0.3055
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 |
|---|---|---|---|---|---|---|
| 2.7267 | 1.0 | 23 | 2.4802 | 0.1260 | 0.1086 | 0.1173 |
| 1.9118 | 2.0 | 46 | 2.4211 | 0.2286 | 0.2110 | 0.2198 |
| 1.6277 | 3.0 | 69 | 2.2101 | 0.3094 | 0.3015 | 0.3055 |
| 1.3088 | 4.0 | 92 | 2.2704 | 0.3073 | 0.3050 | 0.3062 |
| 1.0113 | 5.0 | 115 | 2.4404 | 0.3233 | 0.3195 | 0.3214 |
| 0.7442 | 6.0 | 138 | 2.2811 | 0.3766 | 0.3775 | 0.3770 |
| 0.5763 | 7.0 | 161 | 2.3778 | 0.3448 | 0.3449 | 0.3449 |
| 0.4382 | 8.0 | 184 | 2.5305 | 0.3544 | 0.3537 | 0.3541 |
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/distilbert_rand_10_v2_stsb
Base model
Hartunka/distilbert_rand_10_v2Dataset used to train Hartunka/distilbert_rand_10_v2_stsb
Evaluation results
- Spearmanr on GLUE STSBself-reported0.302
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Hartunka/distilbert_rand_10_v2_stsb")