Text Classification
Transformers
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use Cippppy/mobileBert_imdb with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Cippppy/mobileBert_imdb with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Cippppy/mobileBert_imdb")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Cippppy/mobileBert_imdb") model = AutoModelForSequenceClassification.from_pretrained("Cippppy/mobileBert_imdb", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Cippppy/mobileBert_imdb: direct link, hf CLI and curl.
- Browser
- Download file 1.37 kB
-
https://huggingface.co/Cippppy/mobileBert_imdb/resolve/main/README.md
- Command line
-
hf download hf://Cippppy/mobileBert_imdb/README.md
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curl -L -o README.md https://huggingface.co/Cippppy/mobileBert_imdb/resolve/main/README.md
1.37 kB
metadata
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: mobileBert_imdb
results: []
mobileBert_imdb
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.6938
- Accuracy: 0.5
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: 0.2
- 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: 1
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.6965 | 1.0 | 1563 | 0.6938 | 0.5 |
Framework versions
- Transformers 4.37.2
- Pytorch 2.2.0+cu118
- Datasets 2.17.1
- Tokenizers 0.15.2