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- library_name: transformers
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- tags: []
 
 
 
 
 
 
 
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- # Model Card for Model ID
 
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- <!-- Provide a quick summary of what the model is/does. -->
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- ## Model Details
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- ### Model Description
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- <!-- Provide a longer summary of what this model is. -->
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- This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- - **Developed by:** [More Information Needed]
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- ## Uses
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- <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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- ### Direct Use
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- [More Information Needed]
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- ### Downstream Use [optional]
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- ### Out-of-Scope Use
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- <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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- ## Bias, Risks, and Limitations
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- ### Recommendations
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- Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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- ## How to Get Started with the Model
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- Use the code below to get started with the model.
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- [More Information Needed]
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- ## Training Details
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- ### Training Data
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- ### Training Procedure
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- #### Preprocessing [optional]
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- #### Training Hyperparameters
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- - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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- ## Evaluation
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- ### Testing Data, Factors & Metrics
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- #### Testing Data
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- #### Factors
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- #### Metrics
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- ### Results
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- #### Summary
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- ## Model Examination [optional]
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- Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- ## Technical Specifications [optional]
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- ### Model Architecture and Objective
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- ## Glossary [optional]
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- ## More Information [optional]
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+ license: apache-2.0
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+ base_model: bert-base-cased
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+ tags:
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+ - generated_from_trainer
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+ metrics:
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+ - accuracy
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+ model-index:
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+ - name: mitre-bert-base-cased
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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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+ # mitre-bert-base-cased
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+ This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 1.0145
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+ - Accuracy: 0.6994
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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: 5e-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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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - num_epochs: 10
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+ ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | 1.2761 | 0.68 | 500 | 0.8453 | 0.6864 |
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+ | 0.7448 | 1.36 | 1000 | 0.7566 | 0.7164 |
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+ | 0.6056 | 2.04 | 1500 | 0.7187 | 0.7318 |
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+ | 0.4763 | 2.72 | 2000 | 0.7134 | 0.7307 |
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+ | 0.4276 | 3.41 | 2500 | 0.7604 | 0.7420 |
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+ | 0.3855 | 4.09 | 3000 | 0.7493 | 0.7362 |
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+ | 0.3303 | 4.77 | 3500 | 0.7727 | 0.7423 |
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+ | 0.313 | 5.45 | 4000 | 0.8053 | 0.7263 |
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+ | 0.2948 | 6.13 | 4500 | 0.8555 | 0.7280 |
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+ | 0.2779 | 6.81 | 5000 | 0.8839 | 0.7127 |
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+ | 0.2526 | 7.49 | 5500 | 0.9097 | 0.7144 |
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+ | 0.2576 | 8.17 | 6000 | 0.9421 | 0.7171 |
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+ | 0.2461 | 8.86 | 6500 | 0.9821 | 0.7018 |
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+ | 0.2357 | 9.54 | 7000 | 1.0145 | 0.6994 |
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+ ### Framework versions
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+ - Transformers 4.38.2
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+ - Pytorch 2.2.1+cu121
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+ - Datasets 2.18.0
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+ - Tokenizers 0.15.2
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