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README.md
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library_name: peft
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base_model: mistralai/Mistral-7B-v0.1
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- **Language(s) (NLP):** [More Information Needed]
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- **License:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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- **Paper [optional]:** [More Information Needed]
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- **Demo [optional]:** [More Information Needed]
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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[More Information Needed]
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## Bias, Risks, and Limitations
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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[More Information Needed]
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### Recommendations
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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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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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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[More Information Needed]
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### Training Procedure
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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#### Preprocessing [optional]
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[More Information Needed]
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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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#### Speeds, Sizes, Times [optional]
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[More Information Needed]
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## Evaluation
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<!-- This section describes the evaluation protocols and provides the results. -->
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### Testing Data, Factors & Metrics
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#### Testing Data
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[More Information Needed]
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#### Factors
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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[More Information Needed]
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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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<!-- Relevant interpretability work for the model goes here -->
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[More Information Needed]
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## Environmental Impact
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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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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- **Hardware Type:** [More Information Needed]
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- **Hours used:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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## Technical Specifications [optional]
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### Model Architecture and Objective
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### Compute Infrastructure
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#### Hardware
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#### Software
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## Citation [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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[More Information Needed]
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**APA:**
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## Glossary [optional]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
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## More Information [optional]
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## Model Card Authors [optional]
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## Model Card Contact
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### Framework versions
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- PEFT 0.8.2
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---
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library_name: peft
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base_model: mistralai/Mistral-7B-v0.1
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license: apache-2.0
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datasets:
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- bitext/Bitext-customer-support-llm-chatbot-training-dataset
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language:
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- en
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tags:
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- text-generation-inference
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---
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# Description
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This LoRA adapter was fine-tuned on the `bitext/Bitext-customer-support-llm-chatbot-training-dataset`, specifically by:
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1. Grouping the data on the following `category` column values: `PAYMENT`, `INVOICE`, `REFUND`
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2. Merging `intent` and `response` columns into a new single column called `response_json` that is a JSON object consistint of two keys: `intent` and `response`.
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This is what the dataset looks like once it is preprared:
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<img src="./payments_dataset.png" alt="drawing" width="1500"/>
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It also has the following token distribution (without the prompt template being merged into the input)
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<img src="./token_distribution.png" alt="drawing" width="400"/>
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# How To Use This Model
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## Prompt Template
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This adapter was instruction tuned using the following prompt template:
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```text
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You are a support agent for a company and you receive requests from customers.
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Your job is to reply to the customer by providing both the intent, which you
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should determine from the customer's request, as well as an appropriate response.
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Please note that the intent can only be one of the following: check_payment_methods, get_invoice, check_refund_policy, track_refund, payment_issue, check_invoice, get_refund.
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Please package your reply in the JSON format.
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Request: {instruction}
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Reply:
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```
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At inference time, just replace/insert `{instruction}` with an actual instruction.
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## Example Input and Output
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Input:
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```
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You are a support agent for a company and you receive requests from customers.
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Your job is to reply to the customer by providing both the intent, which you
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should determine from the customer's request, as well as an appropriate response.
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Please note that the intent can only be one of the following: check_payment_methods, get_invoice, check_refund_policy, track_refund, payment_issue, check_invoice, get_refund.
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Please package your reply in the JSON format.
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Request: can you help me check in which cases can I ask for refunds?
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Reply:
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```
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Output (rendered using pprint, but is a proper JSON with full key-value pairs):
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```python
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{'intent': 'check_refund_policy',
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'response': "I'll do my best! I completely understand your need to have a "
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'clear understanding of the situations in which you can request a '
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'refund. Let me provide you with a comprehensive breakdown:\n'
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'\n'
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'1. **Product/Service Defect:** If the product or service you '
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"purchased has a defect or doesn't meet the description, you are "
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'typically eligible for a refund.\n'
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'2. **Cancellation within Grace Period:** Should you change your '
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'mind and decide to cancel your purchase within the specified '
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'grace period, you should be able to receive a refund.\n'
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'3. **Unauthorized Charges:** If you notice any charges on your '
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"account that you didn't authorize or recognize, we will "
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'investigate the matter and assist you in obtaining a refund.\n'
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'4. **Event Cancellation:** If you purchased tickets for an event '
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'that gets canceled without a rescheduled date, you would usually '
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'be entitled to a refund.\n'
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'5. **Duplicate Charges:** If you have been charged multiple '
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'times for the same item, please inform us, and we will rectify '
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'the situation promptly.\n'
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"6. **Non-receipt of Goods:** If you haven't received your "
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'ordered product within the expected timeframe, you may qualify '
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'for a refund.\n'
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'\n'
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'Please keep in mind that refund policies may vary depending on '
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"the vendor or service provider. It's always a good idea to "
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'review their terms and conditions or contact them directly for '
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'more specific information. If you have any specific concerns or '
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'need assistance with a particular order, please provide me with '
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"the necessary details, such as the order number, and I'll be "
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'glad to assist you further.\n'
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'\n'
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'Is there anything else I can help you with today?'}
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```
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### Framework versions
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- PEFT 0.8.2
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- Ludwig 0.10.2
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