Upload refined README

README.md

@@ -8,12 +8,61 @@ tags:
 - ner
 - named-entity-recognition
 pipeline_tag: token-classification
+widget:
+- text: "X-Linked adrenoleukodystrophy (ALD) is a genetic disease associated with demyelination of the central nervous system, adrenal insufficiency, and accumulation of very long chain fatty acids in tissue and body fluids."
+  example_title: "Example 1"
+- text: "Canavan disease is inherited as an autosomal recessive trait that is caused by the deficiency of aspartoacylase (ASPA)."
+  example_title: "Example 2"
+- text: "However, both models lack other frequent DM symptoms including the fibre-type dependent atrophy, myotonia, cataract and male-infertility."
+  example_title: "Example 3"
+model-index:
+  - name: SpanMarker w. bert-base-cased on NCBI Disease by Tom Aarsen
+    results:
+      - task:
+          type: token-classification
+          name: Named Entity Recognition
+        dataset:
+          type: ncbi_disease
+          name: NCBI Disease
+          split: test
+          revision: acd0e6451198d5b615c12356ab6a05fff4610920
+        metrics:
+          - type: f1
+            value: 0.8813
+            name: F1
+          - type: precision
+            value: 0.8661
+            name: Precision
+          - type: recall
+            value: 0.8971
+            name: Recall
+datasets:
+  - ncbi_disease
+language:
+  - en
+metrics:
+  - f1
+  - recall
+  - precision
 ---
 
-# SpanMarker for Named Entity Recognition
+# SpanMarker for Disease Named Entity Recognition
 
-This is a [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) model that can be used for Named Entity Recognition. In particular, this SpanMarker model uses [bert-base-cased](https://huggingface.co/bert-base-cased) as the underlying encoder. 
+This is a [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) model trained on the [ncbi_disease](https://huggingface.co/datasets/ncbi_disease) dataset. In particular, this SpanMarker model uses [bert-base-cased](https://huggingface.co/bert-base-cased) as the underlying encoder. See [train.py](train.py) for the training script.
 
+## Metrics
+
+This model achieves the following results on the testing set:
+- Overall Precision: 0.8661
+- Overall Recall: 0.8971
+- Overall F1: 0.8813
+- Overall Accuracy: 0.9837
+
+## Labels
+
+| **Label** | **Examples** |
+|-----------|--------------|
+| DISEASE   | "ataxia-telangiectasia", "T-cell leukaemia", "C5D", "neutrophilic leukocytosis", "pyogenic infection" | 
 
 ## Usage
 
@@ -29,9 +78,39 @@ You can then run inference with this model like so:
 from span_marker import SpanMarkerModel
 
 # Download from the 🤗 Hub
-model = SpanMarkerModel.from_pretrained("span_marker_model_name")
+model = SpanMarkerModel.from_pretrained("tomaarsen/span-marker-bert-base-ncbi-disease")
 # Run inference
-entities = model.predict("Amelia Earhart flew her single engine Lockheed Vega 5B across the Atlantic to Paris.")
+entities = model.predict("Canavan disease is inherited as an autosomal recessive trait that is caused by the deficiency of aspartoacylase (ASPA).")
 ```
 
 See the [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) repository for documentation and additional information on this library.
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 5e-05
+- train_batch_size: 32
+- eval_batch_size: 32
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 3
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 0.0038        | 1.41  | 300  | 0.0059          | 0.8141            | 0.8579         | 0.8354     | 0.9818           |
+| 0.0018        | 2.82  | 600  | 0.0054          | 0.8315            | 0.8720         | 0.8513     | 0.9840           |
+
+
+### Framework versions
+
+- SpanMarker 1.2.4
+- Transformers 4.31.0
+- Pytorch 1.13.1+cu117
+- Datasets 2.14.3
+- Tokenizers 0.13.2