Text Classification
Transformers
TensorBoard
Safetensors
bert
lsb_ais5
categorical
multi_label
10_class
Generated from Trainer
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("SBzz/model_output")
model = AutoModelForSequenceClassification.from_pretrained("SBzz/model_output")Quick Links
model_output
This model is a fine-tuned version of beomi/kcbert-base on the unsmiled_data dataset. It achieves the following results on the evaluation set:
- Loss: 0.1404
- Lrap: 0.8761
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: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Lrap |
|---|---|---|---|---|
| No log | 1.0 | 235 | 0.1275 | 0.8714 |
| No log | 2.0 | 470 | 0.1223 | 0.8802 |
| 0.0988 | 3.0 | 705 | 0.1285 | 0.8792 |
| 0.0988 | 4.0 | 940 | 0.1399 | 0.8752 |
| 0.0515 | 5.0 | 1175 | 0.1404 | 0.8761 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1
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Model tree for SBzz/model_output
Base model
beomi/kcbert-base
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="SBzz/model_output")