Instructions to use agentlans/deberta-finewebedu with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use agentlans/deberta-finewebedu with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="agentlans/deberta-finewebedu")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("agentlans/deberta-finewebedu") model = AutoModelForMaskedLM.from_pretrained("agentlans/deberta-finewebedu") - Notebooks
- Google Colab
- Kaggle
Deberta-FineWebEdu
This model is a fine-tuned version of microsoft/deberta-v3-xsmall on the FineWebSentences dataset. It achieves the following results on the evaluation set:
- Loss: 3.4314
- Accuracy: 0.4905
Model description
Finetuned on sentences from randomly chosen HuggingFaceFW/fineweb-edu entries.
Intended uses & limitations
To be finetuned on more tasks involving English sentences.
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-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
The evaluation and training losses were similar indicating no overfitting.
Framework versions
- Transformers 4.39.3
- Pytorch 2.3.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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Model tree for agentlans/deberta-finewebedu
Base model
microsoft/deberta-v3-xsmallEvaluation results
- Accuracy on FineWebSentencesself-reported0.491