# Load model directly
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("touhidulislam/BERTweet_retrain_2021_38")
model = AutoModelForMaskedLM.from_pretrained("touhidulislam/BERTweet_retrain_2021_38", device_map="auto")Quick Links
BERTweet_retrain_2021_38
This model is a fine-tuned version of vinai/bertweet-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 2.5849
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: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 2.774 | 1.0 | 6077 | 2.6608 |
| 2.7144 | 2.0 | 12154 | 2.6153 |
| 2.7145 | 3.0 | 18231 | 2.5844 |
Framework versions
- Transformers 4.45.1
- Pytorch 2.1.0+cu121
- Datasets 3.0.1
- Tokenizers 0.20.0
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Model tree for touhidulislam/BERTweet_retrain_2021_38
Base model
vinai/bertweet-base
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="touhidulislam/BERTweet_retrain_2021_38")