Instructions to use eternis/eternis_router_encoder_sft_4Sep with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use eternis/eternis_router_encoder_sft_4Sep with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("eternis/eternis_router_encoder_sft_4Sep", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Model save
Browse files- README.md +70 -0
- model.safetensors +1 -1
README.md
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---
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library_name: transformers
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license: apache-2.0
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base_model: answerdotai/ModernBERT-base
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tags:
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- generated_from_trainer
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model-index:
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- name: eternis_router_encoder_sft_4Sep
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# eternis_router_encoder_sft_4Sep
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This model is a fine-tuned version of [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.1710
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- Mse: 0.1710
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- Model Accuracy: 0.3285
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 2e-05
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- train_batch_size: 16
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- eval_batch_size: 16
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- seed: 42
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- gradient_accumulation_steps: 2
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- total_train_batch_size: 32
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- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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- lr_scheduler_type: cosine
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- lr_scheduler_warmup_ratio: 0.06
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- num_epochs: 3
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Mse | Model Accuracy |
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|:-------------:|:------:|:----:|:---------------:|:------:|:--------------:|
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| 0.4021 | 0.3429 | 300 | 0.1875 | 0.1875 | 0.153 |
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| 0.3702 | 0.6857 | 600 | 0.1809 | 0.1809 | 0.1757 |
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| 0.36 | 1.0286 | 900 | 0.1762 | 0.1762 | 0.3068 |
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| 0.3686 | 1.3714 | 1200 | 0.1788 | 0.1788 | 0.2333 |
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| 0.3337 | 1.7143 | 1500 | 0.1733 | 0.1733 | 0.3192 |
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| 0.3293 | 2.0571 | 1800 | 0.1709 | 0.1709 | 0.3297 |
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| 0.3245 | 2.4 | 2100 | 0.1706 | 0.1706 | 0.3035 |
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| 0.3158 | 2.7429 | 2400 | 0.1710 | 0.1710 | 0.3285 |
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### Framework versions
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- Transformers 4.56.0
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- Pytorch 2.7.0
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- Datasets 4.0.0
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- Tokenizers 0.22.0
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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size 596090240
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version https://git-lfs.github.com/spec/v1
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size 596090240
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