Rename README (1).md to README.md

README (1).md → README.md

@@ -3,35 +3,21 @@ base_model: unsloth/meta-llama-3.1-8b-instruct-bnb-4bit
 library_name: peft
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-# Model Card for Model ID
+# Model Card for Fine-Tuned LLaMA 3.1 on Dependency Parsing
 
 <!-- Provide a quick summary of what the model is/does. -->
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+This model is a fine-tuned version of **LLaMA 3.1** specifically designed to automate dependency parsing of simple sentences, categorizing words into their syntactic roles according to Universal Dependency Parsing tags.
 
 ## Model Details
 
 ### Model Description
 
 <!-- Provide a longer summary of what this model is. -->
+The model has been fine-tuned to accurately parse simple sentences by classifying each word into its respective dependency category, such as `nsubj`, `obj`, and `root`, following the Universal Dependency framework. This fine-tuning enhances the LLaMA 3.1 model's ability to understand and analyze sentence structures, making it a valuable tool for linguistic analysis and natural language processing tasks.
 
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-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **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]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Developed by:** Emanuel Pinasco
+- **Model type:** NLP, Dependency Parsing
+- **Language(s) (NLP):** English
 
 ## Uses
 
@@ -40,65 +26,40 @@ library_name: peft
 ### Direct Use
 
 <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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-[More Information Needed]
+The model can be used directly for syntactic analysis and linguistic research, where dependency parsing is required to understand sentence structures. It’s particularly suited for tasks involving simple sentence parsing.
 
 ### Downstream Use [optional]
 
 <!-- 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]
+Ideal for integration into larger NLP systems that require detailed sentence parsing, such as grammar checking tools, machine translation systems, and educational software.
 
 ### Out-of-Scope Use
 
 <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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-[More Information Needed]
+The model is not designed for complex sentence structures, idiomatic expressions, or languages other than English. Misuse may involve attempts to apply it to tasks beyond simple dependency parsing, leading to inaccurate results.
 
 ## Bias, Risks, and Limitations
 
 <!-- This section is meant to convey both technical and sociotechnical limitations. -->
 
-[More Information Needed]
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 ### Recommendations
 
-<!-- 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]
+Users (both direct and downstream) should be aware that the model's accuracy may decline with more complex or less conventional sentence structures. It's recommended to use this model in conjunction with other tools for more comprehensive linguistic analysis.
 
 ## Training Details
 
 ### Training Data
 
 <!-- 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]
+The model was trained on a curated dataset of simple English sentences annotated with Universal Dependency Parsing tags. The training data focused on ensuring high accuracy in syntactic role assignment.
 
 ### Training Procedure
 
 <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
 
-#### Preprocessing [optional]
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-[More Information Needed]
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 #### Training Hyperparameters
 
-- **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]
+- **Training regime:** Mixed precision (fp16)
 
 ## Evaluation
 
@@ -109,94 +70,35 @@ Use the code below to get started with the model.
 #### Testing Data
 
 <!-- This should link to a Dataset Card if possible. -->
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-[More Information Needed]
+The model was evaluated using a separate dataset of simple sentences annotated with Universal Dependency tags.
 
 #### Factors
 
 <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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-[More Information Needed]
+Evaluation focused on sentence simplicity, vocabulary diversity, and syntactic structure variations.
 
 #### Metrics
 
 <!-- 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]
+Accuracy in word classification into dependency categories was the primary metric.
 
 #### 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]
+The fine-tuned model demonstrates high accuracy in dependency parsing of simple English sentences, making it a robust tool for basic syntactic analysis.
 
 ## Environmental Impact
 
 <!-- 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).
 
-- **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]
+## Model Card Authors
 
-## 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]
+Emanuel Pinasco
 
 ## Model Card Contact
 
-[More Information Needed]
+Emanuel Pinasco
+
 ### Framework versions
 
-- PEFT 0.12.0
+- PEFT 0.12.0