Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("SubhasishSaha/Resume-Classifier")
model = AutoModelForSequenceClassification.from_pretrained("SubhasishSaha/Resume-Classifier", device_map="auto")Quick Links
Saved_Model
This model is a fine-tuned version of bert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.0582
- Accuracy: 0.7659
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: 1e-05
- train_batch_size: 2
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 2.6601 | 1.0 | 885 | 1.3884 | 0.7287 |
| 1.2011 | 2.0 | 1770 | 0.9965 | 0.7637 |
| 0.782 | 3.0 | 2655 | 1.0051 | 0.7702 |
| 0.5712 | 4.0 | 3540 | 1.0452 | 0.7681 |
| 0.5244 | 5.0 | 4425 | 1.0582 | 0.7659 |
Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
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Model tree for SubhasishSaha/Resume-Classifier
Base model
google-bert/bert-base-uncased
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="SubhasishSaha/Resume-Classifier")