Update README.md

README.md

@@ -44,7 +44,6 @@ This model aims to parse simple english arguments, arguments formed of two premi
 <!-- Provide the basic links for the model. -->
 
 - **Repository:** TBD
-- **Paper:** TBD
 - **Demo:** TBD
 
 ## Usage
@@ -118,121 +117,27 @@ The model was trained on syntethic data, based on the following types of argumen
 
 Each argument was constructed by selecting two random propositions (from a list of 400 propositions that was generated beforehand), choosing a type of argument and combining it all with randomly selected connectors (therefore, since, hence, thus, etc).
 
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+50k arguments were created to train the model, and 100 to test.
 
 ### Training Procedure
 
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-#### Preprocessing [optional]
+#### Preprocessing
 
 [More Information Needed]
+We converted the data to the Alpaca chat format before feeding it to the model.
 
+#### Training
 
-#### 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 -->
+We used unsloth for memory reduced sped up training.
 
-#### Speeds, Sizes, Times [optional]
+We trained for one epoch.
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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-[More Information Needed]
+Less than 2.5 GB of VRAM were used for training, and it took 2.5 hours.
 
 ## Evaluation
 
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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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-[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]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **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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-[More Information Needed]
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-#### Hardware
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-[More Information Needed]
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-#### Software
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-[More Information Needed]
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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]
+The model obtains 100% train and test accuracy on our synthetic dataset.