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  # tool-bert
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- This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on an unknown dataset.
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- It achieves the following results on the evaluation set:
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- - Loss: 0.0158
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- - Accuracy: 0.9886
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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: 8
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- - eval_batch_size: 8
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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: 3.0
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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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- | No log | 1.0 | 64 | 0.1104 | 0.9830 |
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- | No log | 2.0 | 128 | 0.0222 | 0.9886 |
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- | No log | 3.0 | 192 | 0.0158 | 0.9886 |
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  ### Framework versions
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  # tool-bert
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+ This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased).
 
 
 
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+ It uses a custom made dataset of sample user instructions, which are classified to a number of possible local assistant function calling endpoints.
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+ For example, given an input query, tool-bert returns a prediction as to what tool to use to augment a downstream LLM generated output with.
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+ More information on these tools to follow, but example tools are "play music", "check the weather", "get the news", "take a photo", or use no tool.
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+ Basically, this model is meant to be a means of allowing very small LLMs (i.e. 8B and below) to use function calling.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ All limitations and biases are inherited from the parent model.
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  ### Framework versions
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