Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Mediocre-Judge/my_mind_classifier_BertMini")
model = AutoModelForSequenceClassification.from_pretrained("Mediocre-Judge/my_mind_classifier_BertMini", device_map="auto")Quick Links
my_mind_classifier_BertMini
This model is a fine-tuned version of prajjwal1/bert-mini on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.4530
- Accuracy: 0.8620
- Precision: 0.8578
- Recall: 0.8620
- F1: 0.8599
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: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| 0.6179 | 1.0 | 9024 | 0.5588 | 0.8303 | 0.8272 | 0.8303 | 0.8288 |
| 0.4973 | 2.0 | 18048 | 0.4530 | 0.8620 | 0.8578 | 0.8620 | 0.8599 |
Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0
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Model tree for Mediocre-Judge/my_mind_classifier_BertMini
Base model
prajjwal1/bert-mini
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Mediocre-Judge/my_mind_classifier_BertMini")