filling model card

README.md

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+# Model Card for Simple Latin BERT 
+
+<!-- Provide a quick summary of what the model is/does. [Optional] -->
+A simple BERT Masked Language Model for Latin for my portfolio, trained on Latin Corpora from the [Classical Language Toolkit](http://cltk.org/)  corpora. 
+
+**NOT** apt for production nor commercial use.  
+This model&#39;s performance is really poor, and it has not been evaluated.
+
+This model comes with its own tokenizer! It will automatically use **lowercase**.
+
+Check the `notebooks` folder for the preprocessing and training scripts.
+
+
+
+
+#  Table of Contents
+
+- [Model Card for Simple Latin BERT ](#model-card-for--model_id-)
+- [Table of Contents](#table-of-contents)
+- [Table of Contents](#table-of-contents-1)
+- [Model Details](#model-details)
+  - [Model Description](#model-description)
+- [Uses](#uses)
+  - [Direct Use](#direct-use)
+  - [Downstream Use [Optional]](#downstream-use-optional)
+- [Training Details](#training-details)
+  - [Training Data](#training-data)
+  - [Training Procedure](#training-procedure)
+    - [Preprocessing](#preprocessing)
+    - [Speeds, Sizes, Times](#speeds-sizes-times)
+- [Evaluation](#evaluation)
+
+
+# Model Details
+
+## Model Description
+
+<!-- Provide a longer summary of what this model is/does. -->
+A simple BERT Masked Language Model for Latin for my portfolio, trained on Latin Corpora from the [Classical Language Toolkit](http://cltk.org/)  corpora. 
+
+**NOT** apt for production nor commercial use.  
+This model&#39;s performance is really poor, and it has not been evaluated.
+
+This model comes with its own tokenizer!
+
+Check the `notebooks` folder for the preprocessing and training scripts.
+
+- **Developed by:** Luis Antonio VASQUEZ
+- **Model type:** Language model
+- **Language(s) (NLP):** la
+- **License:** mit
+
+
+
+# Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+## Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+<!-- If the user enters content, print that. If not, but they enter a task in the list, use that. If neither, say "more info needed." -->
+
+This model can be used directly for Masked Language Modelling.
+
+
+## Downstream Use
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+<!-- If the user enters content, print that. If not, but they enter a task in the list, use that. If neither, say "more info needed." -->
+ 
+This model could be used as a base model for other NLP tasks, for example, Text Classification (that is, using transformers&#39; `BertForSequenceClassification`)
+
+
+
+
+
+
+# Training Details
+
+## Training Data
+
+<!-- This should link to a Data 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. -->
+
+The training data comes from the corpora freely available from the [Classical Language Toolkit](http://cltk.org/)
+
+- [The Latin Library](https://www.thelatinlibrary.com/)
+- Latin section of the [Perseus Digital Library](http://www.perseus.tufts.edu/hopper/)
+-  Latin section of the [Tesserae Project](https://tesserae.caset.buffalo.edu/)
+- [Corpus Grammaticorum Latinorum](https://cgl.hypotheses.org/)
+
+
+
+
+## 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
+
+For preprocessing, the raw text from each of the corpora was extracted by parsing.  Then, it was **lowercased** and written onto `txt` files. Ideally, in these files one line would correspond to one sentence.
+
+Other data from the corpora, like Entity Tags, POS Tags, etc., were discarded.
+
+Training hyperparameters:
+- epochs: 1
+- Batch size: 64
+- Attention heads: 12
+- Hidden Layers: 12
+- Max input size: 512 tokens
+
+### Speeds, Sizes, Times
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+After having the dataset ready, training this model on a 16 GB Nvidia Graphics card took around 10 hours.
+ 
+# Evaluation
+
+No evaluation was performed on this dataset.