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/bert_base_rand_10_v1_stsb")
model = AutoModelForSequenceClassification.from_pretrained("Hartunka/bert_base_rand_10_v1_stsb")Quick Links
bert_base_rand_10_v1_stsb
This model is a fine-tuned version of Hartunka/bert_base_rand_10_v1 on the GLUE STSB dataset. It achieves the following results on the evaluation set:
- Loss: 2.3422
- Pearson: 0.2300
- Spearmanr: 0.2238
- Combined Score: 0.2269
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 |
|---|---|---|---|---|---|---|
| 2.5501 | 1.0 | 23 | 2.5206 | 0.1247 | 0.1086 | 0.1166 |
| 1.9123 | 2.0 | 46 | 2.3921 | 0.1624 | 0.1434 | 0.1529 |
| 1.6606 | 3.0 | 69 | 2.3422 | 0.2300 | 0.2238 | 0.2269 |
| 1.2907 | 4.0 | 92 | 2.5930 | 0.2617 | 0.2667 | 0.2642 |
| 0.9783 | 5.0 | 115 | 2.4709 | 0.2854 | 0.2803 | 0.2828 |
| 0.7673 | 6.0 | 138 | 2.4687 | 0.3073 | 0.3021 | 0.3047 |
| 0.5922 | 7.0 | 161 | 2.4917 | 0.3069 | 0.3033 | 0.3051 |
| 0.4832 | 8.0 | 184 | 2.7527 | 0.2931 | 0.2892 | 0.2911 |
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/bert_base_rand_10_v1_stsb
Base model
Hartunka/bert_base_rand_10_v1Dataset used to train Hartunka/bert_base_rand_10_v1_stsb
Evaluation results
- Spearmanr on GLUE STSBself-reported0.224
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Hartunka/bert_base_rand_10_v1_stsb")