Instructions to use bdotloh/twitter-roberta-base-finetuned-twitter-user-desc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bdotloh/twitter-roberta-base-finetuned-twitter-user-desc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="bdotloh/twitter-roberta-base-finetuned-twitter-user-desc")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("bdotloh/twitter-roberta-base-finetuned-twitter-user-desc") model = AutoModelForMaskedLM.from_pretrained("bdotloh/twitter-roberta-base-finetuned-twitter-user-desc") - Notebooks
- Google Colab
- Kaggle
twitter-roberta-base-finetuned-twitter-user-desc
This model is a fine-tuned version of cardiffnlp/twitter-roberta-base on a dataset of twitter user descriptions. It achieves the following results on the evaluation set:
- eval_perplexity: 2.33
- epoch: 15
- step: 10635
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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
- mixed_precision_training: Native AMP
Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
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