Instructions to use KalaiselvanD/kalai_bert_model_test_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KalaiselvanD/kalai_bert_model_test_2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="KalaiselvanD/kalai_bert_model_test_2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("KalaiselvanD/kalai_bert_model_test_2") model = AutoModelForSequenceClassification.from_pretrained("KalaiselvanD/kalai_bert_model_test_2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from KalaiselvanD/kalai_bert_model_test_2: direct link, hf CLI and curl.
- Browser
- Download file 1.44 kB
-
https://huggingface.co/KalaiselvanD/kalai_bert_model_test_2/resolve/main/README.md
- Command line
-
hf download hf://KalaiselvanD/kalai_bert_model_test_2/README.md
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curl -L -o README.md https://huggingface.co/KalaiselvanD/kalai_bert_model_test_2/resolve/main/README.md
1.44 kB
metadata
license: apache-2.0
base_model: albert/albert-base-v2
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: kalai_bert_model_test_2
results: []
kalai_bert_model_test_2
This model is a fine-tuned version of albert/albert-base-v2 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.3276
- Accuracy: 0.93
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: 16
- eval_batch_size: 16
- 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 |
|---|---|---|---|---|
| No log | 1.0 | 25 | 0.5033 | 0.93 |
| No log | 2.0 | 50 | 0.3276 | 0.93 |
Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1