Upload improved README

README.md

@@ -8,12 +8,67 @@ tags:
 - ner
 - named-entity-recognition
 pipeline_tag: token-classification
+widget:
+- text: "here, da = direct assessment, rr = relative ranking, ds = discrete scale and cs = continuous scale."
+  example_title: "Uncased Example 1"
+- text: "modifying or replacing the erasable programmable read only memory (eprom) in a phone would allow the configuration of any esn and min via software for cellular devices."
+  example_title: "Uncased Example 2"
+- text: "we propose a technique called aggressive stochastic weight averaging (aswa) and an extension called norm-filtered aggressive stochastic weight averaging (naswa) which improves te stability of models over random seeds."
+  example_title: "Uncased Example 3"
+- text: "the choice of the encoder and decoder modules of dnpg can be quite flexible, for instance long-short term memory networks (lstm) or convolutional neural network (cnn)."
+  example_title: "Uncased Example 4"
+model-index:
+  - name: SpanMarker w. bert-base-uncased on Acronym Identification by Tom Aarsen
+    results:
+      - task:
+          type: token-classification
+          name: Named Entity Recognition
+        dataset:
+          type: acronym_identification
+          name: Acronym Identification
+          split: validation
+          revision: c3c245a18bbd57b1682b099e14460eebf154cbdf
+        metrics:
+          - type: f1
+            value: 0.9198
+            name: F1
+          - type: precision
+            value: 0.9252
+            name: Precision
+          - type: recall
+            value: 0.9145
+            name: Recall
+datasets:
+  - acronym_identification
+language:
+  - en
+metrics:
+  - f1
+  - recall
+  - precision
 ---
 
-# SpanMarker for Named Entity Recognition
+# SpanMarker for uncased Acronyms 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-uncased](https://huggingface.co/bert-base-uncased) as the underlying encoder. 
+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-uncased](https://huggingface.co/bert-base-uncased) as the underlying encoder. See [train.py](train.py) for the training script.
 
+Is your data only capitalized correctly? Then consider using the cased variant of this model instead for better performance: 
+[tomaarsen/span-marker-bert-base-acronyms](https://huggingface.co/tomaarsen/span-marker-bert-base-acronyms).
+
+## Metrics
+
+It achieves the following results on the validation set:
+- Overall Precision: 0.9252
+- Overall Recall: 0.9145
+- Overall F1: 0.9198
+- Overall Accuracy: 0.9797
+
+## Labels
+
+| **Label** | **Examples** |
+|-----------|--------------|
+| SHORT     | "nlp", "coqa", "soda", "sca" | 
+| LONG      | "natural language processing", "conversational question answering", "symposium on discrete algorithms", "successive convex approximation" |
 
 ## Usage
 
@@ -29,9 +84,43 @@ 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-uncased-acronyms")
 # Run inference
-entities = model.predict("Amelia Earhart flew her single engine Lockheed Vega 5B across the Atlantic to Paris.")
+entities = model.predict("compression algorithms like principal component analysis (pca) can reduce noise and complexity.")
 ```
 
 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: 2
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 0.013         | 0.31  | 200  | 0.0101          | 0.8998            | 0.8514         | 0.8749     | 0.9696           |
+| 0.0088        | 0.62  | 400  | 0.0082          | 0.8997            | 0.9142         | 0.9069     | 0.9764           |
+| 0.0082        | 0.94  | 600  | 0.0071          | 0.9173            | 0.8955         | 0.9063     | 0.9765           |
+| 0.0063        | 1.25  | 800  | 0.0066          | 0.9210            | 0.9187         | 0.9198     | 0.9802           |
+| 0.0066        | 1.56  | 1000 | 0.0066          | 0.9302            | 0.8941         | 0.9118     | 0.9783           |
+| 0.0064        | 1.87  | 1200 | 0.0063          | 0.9304            | 0.9042         | 0.9171     | 0.9792           |
+| 0.0063        | 2.00  | 1290 | 0.0063          | 0.9252            | 0.9145         | 0.9198     | 0.9797           |
+
+### Framework versions
+
+- SpanMarker 1.2.4
+- Transformers 4.31.0
+- Pytorch 1.13.1+cu117
+- Datasets 2.14.3
+- Tokenizers 0.13.2