Instructions to use dd3434/test_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dd3434/test_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dd3434/test_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dd3434/test_model") model = AutoModelForSequenceClassification.from_pretrained("dd3434/test_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,658 Bytes
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tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: test_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# test_model
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9938
- Accuracy: 0.76
## 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: 128
- eval_batch_size: 128
- 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 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6263 | 0.62 | 100 | 0.5941 | 0.7394 |
| 0.5263 | 1.24 | 200 | 0.5814 | 0.7578 |
| 0.4177 | 1.86 | 300 | 0.6013 | 0.7530 |
| 0.2901 | 2.48 | 400 | 0.7275 | 0.7402 |
| 0.2377 | 3.11 | 500 | 0.7853 | 0.7433 |
| 0.1313 | 3.73 | 600 | 0.8747 | 0.7394 |
| 0.1043 | 4.35 | 700 | 0.9510 | 0.7464 |
| 0.0714 | 4.97 | 800 | 0.9938 | 0.7473 |
### Framework versions
- Transformers 4.32.0
- Pytorch 2.1.2
- Datasets 2.16.0
- Tokenizers 0.13.3
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