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--- |
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license: apache-2.0 |
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base_model: mistralai/Mistral-7B-v0.1 |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: storage/context |
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results: [] |
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datasets: |
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- mhenrichsen/context-aware-splits-english |
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language: |
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- en |
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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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[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) |
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# storage/context |
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This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the mhenrichsen/context-aware-splits-english dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0253 |
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## Model description |
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- This model is used to split texts in a context aware way. Used for RAG applications. |
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- This model is an adapter for Mistral 7b. |
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It uses the Alpaca format: |
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``` |
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Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request. |
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### Instruction: |
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Your task is to segment text into smaller blocks. Split the text where it makes sense and be vary of the context. The ideal split should be close to {WORD_COUNT} words. |
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### Input: |
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Q: Information/File Manager I'm looking for a file manager application which helps to organize a large amount of movies, pictures, music, text documents, databases, audio-books and ebooks. Right now I only use the Finder which doesn't work well, because I really need a function to put single files into multiple categories. Simply using the file system for this creates a confusing nesting of files. A: Depending on the number of categories you require to handle, you could always use a combination of the finder with the built in label functionality, thus a movie can be held in one area (movies directory, for example), but "tagged" as something else. Using smart directories and saved searches you can view your files by a combination of the attributes (location, label, media type) to create custom views. All without purchasing software. Cheap and cheerful, but may be suitable to your needs. A: Maybe use a file manager that supports Open Meta. Or use symbolic links for organizing all your media files. Or even use hardlinked files if you dare. |
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### Response: |
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``` |
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Response: |
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``` |
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{'splits': ["Q: Information/File Manager I'm looking for a file manager application which helps to organize a large amount of movies, pictures, music, text documents, databases, audio-books and ebooks. Right now I only use the Finder which doesn't work well, because I really need a function to put single files into multiple categories. Simply using the file system for this creates a confusing nesting of files.", 'A: Depending on the number of categories you require to handle, you could always use a combination of the finder with the built in label functionality, thus a movie can be held in one area (movies directory, for example), but "tagged" as something else. Using smart directories and saved searches you can view your files by a combination of the attributes (location, label, media type) to create custom views. All without purchasing software. Cheap and cheerful, but may be suitable to your needs.', 'A: Maybe use a file manager that supports Open Meta. Or use symbolic links for organizing all your media files. Or even use hardlinked files if you dare.'], 'topic': 'Discussion on file manager applications for organizing large amount of media files.'} |
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``` |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0002 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- gradient_accumulation_steps: 8 |
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- total_train_batch_size: 32 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_steps: 10 |
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- num_epochs: 1 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 0.1557 | 0.01 | 1 | 0.1552 | |
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| 0.0936 | 0.05 | 6 | 0.0695 | |
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| 0.0447 | 0.1 | 12 | 0.0413 | |
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| 0.0347 | 0.16 | 18 | 0.0357 | |
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| 0.0314 | 0.21 | 24 | 0.0324 | |
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| 0.0306 | 0.26 | 30 | 0.0309 | |
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| 0.0276 | 0.31 | 36 | 0.0294 | |
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| 0.028 | 0.36 | 42 | 0.0284 | |
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| 0.0307 | 0.41 | 48 | 0.0281 | |
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| 0.0276 | 0.47 | 54 | 0.0274 | |
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| 0.0251 | 0.52 | 60 | 0.0267 | |
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| 0.0244 | 0.57 | 66 | 0.0269 | |
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| 0.0268 | 0.62 | 72 | 0.0263 | |
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| 0.0249 | 0.67 | 78 | 0.0262 | |
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| 0.0252 | 0.73 | 84 | 0.0258 | |
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| 0.0259 | 0.78 | 90 | 0.0257 | |
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| 0.0241 | 0.83 | 96 | 0.0255 | |
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| 0.0241 | 0.88 | 102 | 0.0254 | |
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| 0.0253 | 0.93 | 108 | 0.0254 | |
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| 0.0234 | 0.98 | 114 | 0.0253 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
