Add improved README

README.md

@@ -8,11 +8,64 @@ 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: "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: "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 the stability of models over random seeds."
+  example_title: "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: "Example 4"
+model-index:
+  - name: SpanMarker w. bert-base-cased 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: 0.9310
+            name: F1
+          - type: precision
+            value: 0.9423
+            name: Precision
+          - type: recall
+            value: 0.9199
+            name: Recall
+datasets:
+  - acronym_identification
+language:
+  - en
+metrics:
+  - f1
+  - recall
+  - precision
 ---
 
-# SpanMarker for Named Entity Recognition
+# SpanMarker for 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-cased](https://huggingface.co/bert-base-cased) as the underlying encoder. 
+This is a [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) model trained on the [acronym_identification](https://huggingface.co/datasets/acronym_identification) 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
+
+It achieves the following results on the validation set:
+- Overall Precision: 0.9423
+- Overall Recall: 0.9199
+- Overall F1: 0.9310
+- Overall Accuracy: 0.9830
+
+## 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 +82,43 @@ 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_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 |
+|:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 0.0109        | 0.31  | 200  | 0.0079          | 0.9202            | 0.8962         | 0.9080     | 0.9765           |
+| 0.0075        | 0.62  | 400  | 0.0070          | 0.9358            | 0.8724         | 0.9030     | 0.9765           |
+| 0.0068        | 0.93  | 600  | 0.0059          | 0.9363            | 0.9203         | 0.9282     | 0.9821           |
+| 0.0057        | 1.24  | 800  | 0.0056          | 0.9372            | 0.9187         | 0.9278     | 0.9824           |
+| 0.0051        | 1.55  | 1000 | 0.0054          | 0.9381            | 0.9170         | 0.9274     | 0.9824           |
+| 0.0054        | 1.86  | 1200 | 0.0053          | 0.9424            | 0.9218         | 0.9320     | 0.9834           |
+| 0.0054        | 2.00  | 1290 | 0.0054          | 0.9423            | 0.9199         | 0.9310     | 0.9830           |
+
+### Framework versions
+
+- SpanMarker 1.2.4
+- Transformers 4.31.0
+- Pytorch 1.13.1+cu117
+- Datasets 2.14.3
+- Tokenizers 0.13.2