Instructions to use gamino/gte-base-finetuned-imdb with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gamino/gte-base-finetuned-imdb with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="gamino/gte-base-finetuned-imdb")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("gamino/gte-base-finetuned-imdb") model = AutoModelForMaskedLM.from_pretrained("gamino/gte-base-finetuned-imdb") - Notebooks
- Google Colab
- Kaggle
gte-base-finetuned-imdb
This model is a fine-tuned version of thenlper/gte-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 7.3837
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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 8.9078 | 1.0 | 32 | 7.6809 |
| 7.555 | 2.0 | 64 | 7.4141 |
| 7.4182 | 3.0 | 96 | 7.3361 |
Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0
- Datasets 2.14.6
- Tokenizers 0.14.1
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Model tree for gamino/gte-base-finetuned-imdb
Base model
thenlper/gte-base