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library_name: transformers
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---
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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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- **Funded by [optional]:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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- **Model type:** [More Information Needed]
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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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### Model Sources [optional]
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<!-- Provide the basic links for the model. -->
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- **Repository:** [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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## 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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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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[More Information Needed]
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### Downstream Use [optional]
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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[More Information Needed]
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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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[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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### Training Data
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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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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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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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<!-- This should link to a Dataset Card if possible. -->
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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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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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[More Information Needed]
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### Results
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[More Information Needed]
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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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[More Information Needed]
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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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[More Information Needed]
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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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[More Information Needed]
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## Model Card Authors [optional]
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[More Information Needed]
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## Model Card Contact
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[More Information Needed]
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---
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library_name: transformers
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tags:
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- text-classification
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- bert
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- nlp
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- classification
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language: en
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license: mit
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datasets:
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- Kerassy/trustpilot-reviews-123k
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metrics:
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- accuracy
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base_model:
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- google-bert/bert-base-uncased
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# Model Card for Kerassy/bert_base_tp_123k
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Classifies sentiment of user review text as either positive, neautral or negative.
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- **Developed by:** Jay Broughton (Kerassy)
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- **Language(s) (NLP):** En
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- **License:** MIT
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- **Finetuned from model:** [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased)
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## How to Get Started with the Model
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```python
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from transformers import pipeline
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reviews = ["Really long wait time for drinks and food and then for food order wrong. Bowling however that was great no issues at all.",
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"Took my son and friends for a birthday party 6 children under 12. A week later get a £200 fine for parking in a free car park!!Apparently you have to type your reg number into something to validate your stay. They don’t care as a third party operate the car park so it’s not there problem if you get a fine!! Everywhere I’ve ever been with one of these systems the person at reception will tell you when booking to enter your details into the machine. But not Hollywood bowl Oxford!!I see they have replied to me saying to get in touch I already did they said it’s not their problem due to them not owning the car park!",
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"Beyond brilliant! Rachel's energy is something else and she entertains them children thoroughly throughout the entire time of booking, plenty of dancing and music and she matches her routine to the ages of the children.I've used for a couple of years now and have no hesitation in booking her for every kids event we have, She makes it a proper party and the adults love it too!",
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"Terrible Got charged 5 times Over half the times smashed and still waiting for someone to contact me..."]
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pipe = pipeline(task="sentiment-analysis", model="Kerassy/distilbert_base_tp_123k", device="cuda") # or "cpu"
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preds = pipe(reviews)
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print(preds)
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```
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### Training Data
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The model was fine-tuned using the following dataset:
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[Kerassy/trustpilot-reviews-123k](https://huggingface.co/datasets/Kerassy/trustpilot-reviews-123k)
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### Accuracy
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The performance of the model was evaluated using the Trustpilot reviews dataset in conjunction with Scikit Learn metrics libraries. The results are as follows:
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- Accuracy: 0.9543
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- Precision: 0.9670
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- Recall 0.9543
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- F1-Score: 0.9597
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