Instructions to use rushikeshwalode/MLM_rotten_tomatoes with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rushikeshwalode/MLM_rotten_tomatoes with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="rushikeshwalode/MLM_rotten_tomatoes")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("rushikeshwalode/MLM_rotten_tomatoes") model = AutoModelForMaskedLM.from_pretrained("rushikeshwalode/MLM_rotten_tomatoes", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from rushikeshwalode/MLM_rotten_tomatoes: direct link, hf CLI and curl.
- Browser
- Download file 1.24 kB
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https://huggingface.co/rushikeshwalode/MLM_rotten_tomatoes/resolve/main/README.md
- Command line
-
hf download hf://rushikeshwalode/MLM_rotten_tomatoes/README.md
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curl -L -o README.md https://huggingface.co/rushikeshwalode/MLM_rotten_tomatoes/resolve/main/README.md
1.24 kB
metadata
library_name: transformers
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
model-index:
- name: MLM_rotten_tomatoes
results: []
MLM_rotten_tomatoes
This model is a fine-tuned version of bert-base-cased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 2.2214
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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
Training results
Framework versions
- Transformers 4.53.3
- Pytorch 2.11.0+cu128
- Datasets 4.0.0
- Tokenizers 0.21.4