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_5_v1_stsb")
model = AutoModelForSequenceClassification.from_pretrained("Hartunka/bert_base_rand_5_v1_stsb")Quick Links
bert_base_rand_5_v1_stsb
This model is a fine-tuned version of Hartunka/bert_base_rand_5_v1 on the GLUE STSB dataset. It achieves the following results on the evaluation set:
- Loss: 2.2682
- Pearson: 0.2151
- Spearmanr: 0.1983
- Combined Score: 0.2067
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 |
|---|---|---|---|---|---|---|
| 2.7364 | 1.0 | 23 | 2.7827 | 0.0963 | 0.0802 | 0.0883 |
| 1.8997 | 2.0 | 46 | 2.2682 | 0.2151 | 0.1983 | 0.2067 |
| 1.5738 | 3.0 | 69 | 2.3547 | 0.2811 | 0.2677 | 0.2744 |
| 1.242 | 4.0 | 92 | 2.3781 | 0.3120 | 0.3112 | 0.3116 |
| 0.8939 | 5.0 | 115 | 2.4723 | 0.3232 | 0.3164 | 0.3198 |
| 0.725 | 6.0 | 138 | 2.5860 | 0.3181 | 0.3054 | 0.3117 |
| 0.5201 | 7.0 | 161 | 2.3986 | 0.3379 | 0.3299 | 0.3339 |
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_5_v1_stsb
Base model
Hartunka/bert_base_rand_5_v1Dataset used to train Hartunka/bert_base_rand_5_v1_stsb
Evaluation results
- Spearmanr on GLUE STSBself-reported0.198
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Hartunka/bert_base_rand_5_v1_stsb")