updated project and readme to specify files

README.md

@@ -1,6 +1,65 @@
 ---
 license: mit
 ---
+# 📚 Placing the Holocaust Weasel (spacy) Project
+
+This is the official spaCy project for the Placing the Holocaust Project. This project houses our data and our Python scripts for converting data, serializing it, training 4 different spaCy models with it, and evaluating those models. It also contains all the metrics from v. 0.0.1.
+
+For this project, we are using spaCy v. 3.7.4.
+
+## 📋 project.yml
+
+The [`project.yml`](project.yml) defines the data assets required by the project, as well as the available commands and workflows. For details, see the [Weasel documentation](https://github.com/explosion/weasel).
+
+### ⏯ Commands
+
+The following commands are defined by the project. They can be executed using [`weasel run [name]`](https://github.com/explosion/weasel/tree/main/docs/cli.md#rocket-run). Commands are only re-run if their inputs have changed.
+
+| Command | Description |
+| --- | --- |
+| `download-lg` | Download a large spaCy model with pretrained vectors |
+| `download-md` | Download a medium spaCy model with pretrained vectors |
+| `convert` | Convert the data to spaCy's binary format |
+| `convert-sents` | Convert the data to sentences before converting to spaCy's binary format |
+| `split` | Split data into train/dev/test sets |
+| `create-config-sm` | Create a new config with a spancat pipeline component for small models |
+| `train-sm` | Train the spancat model with a small configuration |
+| `train-md` | Train the spancat model with a medium configuration |
+| `train-lg` | Train the spancat model with a large configuration |
+| `train-trf` | Train the spancat model with a transformer configuration |
+| `evaluate-sm` | Evaluate the small model and export metrics |
+| `evaluate-md` | Evaluate the medium model and export metrics |
+| `evaluate-lg` | Evaluate the large model and export metrics |
+| `build-table` | Build a table from the metrics for README.md |
+| `readme` | Build a table from the metrics for README.md |
+| `package` | Package the trained model as a pip package |
+| `clean` | Remove intermediary directories |
+
+### ⏭ Workflows
+
+The following workflows are defined by the project. They can be executed using [`weasel run [name]`](https://github.com/explosion/weasel/tree/main/docs/cli.md#rocket-run) and will run the specified commands in order. Commands are only re-run if their inputs have changed.
+
+| Workflow | Steps |
+| --- | --- |
+| `all-sm-sents` | `convert-sents` → `split` → `create-config-sm` → `train-sm` → `evaluate-sm` |
+
+### 🗂 Assets
+
+The following assets are defined by the project. They can be fetched by running [`weasel assets`](https://github.com/explosion/weasel/tree/main/docs/cli.md#open_file_folder-assets) in the project directory.
+
+| File | Source | Description |
+| --- | --- | --- |
+| [`assets/train.jsonl`](assets/train.jsonl) | Local | Training data. Chunked into sentences. |
+| [`assets/dev.jsonl`](assets/dev.jsonl) | Local | Validation data. Chunked into sentences. |
+| [`assets/test.jsonl`](assets/test.jsonl) | Local | Testing data. Chunked into sentences. |
+| [`assets/annotated_data.json/`](assets/annotated_data.json/) | Local | All data, including negative examples. |
+| [`assets/annotated_data_spans.jsonl`](assets/annotated_data_spans.jsonl) | Local | Data with examples of span annotations. |
+| [`corpus/train.spacy`](corpus/train.spacy) | Local | Training data in serialized format. |
+| [`corpus/dev.spacy`](corpus/dev.spacy) | Local | Validation data in serialized format. |
+| [`corpus/test.spacy`](corpus/test.spacy) | Local | Testing data in serialized format. |
+| [`gold-training-data/*`](gold-training-data/*) | Local | Original outputs from Prodigy. |
+| [`notebooks/*`](notebooks/*) | Local | Notebooks for testing project features. |
+| [`configs/*`](configs/*) | Local | Config files for training spaCy models. |
 
 # Overall Model Performance
 | Model       |   Precision |   Recall |   F-Score |
@@ -48,4 +107,4 @@ license: mit
 | Small       | SPATIAL_OBJ     |        96   |     90   |      92.9 |
 | Medium      | SPATIAL_OBJ     |        95.2 |     93.8 |      94.5 |
 | Large       | SPATIAL_OBJ     |        95.3 |     95.5 |      95.4 |
-| Transformer | SPATIAL_OBJ     |        96.3 |     92.8 |      94.5 |
+| Transformer | SPATIAL_OBJ     |        96.3 |     92.8 |      94.5 |  

project.yml

@@ -24,11 +24,34 @@ directories: ["assets", "corpus", "configs", "training", "scripts", "packages"]
 # Assets that should be downloaded or available in the directory. We're shipping
 # them with the project, so they won't have to be downloaded.
 assets:
-  - dest: "assets/train.json"
-    description: "Demo training data adapted from the `ner_demo` project"
-  - dest: "assets/dev.json"
-    description: "Demo development data"
-
+  - dest: "assets/train.jsonl"
+    description: "Training data. For this project, they were chunked into sentences."
+  - dest: "assets/dev.jsonl"
+    description: "Validation data. For this project, they were chunked into sentences."
+  - dest: "assets/test.jsonl"
+    description: "Testing data. For this project, they were chunked into sentences."
+
+  - dest: "assets/annotated_data.json/"
+    description: "All data, including those without annotations because they are negative examples."
+
+  - dest: "assets/annotated_data_spans.jsonl"
+    description: "This is just the data that contained examples of span annotations."
+
+  - dest: "corpus/train.spacy"
+    description: "Training data in serialized format."
+  - dest: "corpus/dev.spacy"
+    description: "Validation data in serialized format."
+  - dest: "corpus/test.spacy"
+    description: "Testing data in serialized format."
+
+  - dest: "gold-training-data/*"
+    description: "The original outputs from Prodigy, the annotation software used."
+
+  - dest: "notebooks/*"
+    description: "A collection of notebooks for testing different features of the project."
+
+  - dest: "configs/*"
+    description: "A collection of config files used for training the spaCy models." 
 # Workflows are sequences of commands (see below) executed in order. You can
 # run them via "spacy project run [workflow]". If a commands's inputs/outputs
 # haven't changed, it won't be re-run.