Use updated, generated README

README.md

@@ -1,4 +1,7 @@
 ---
+language:
+- en
+license: apache-2.0
 library_name: span-marker
 tags:
 - span-marker
@@ -6,35 +9,92 @@ tags:
 - ner
 - named-entity-recognition
 - generated_from_span_marker_trainer
+datasets:
+- acronym_identification
 metrics:
 - precision
 - recall
 - f1
-widget: []
+widget:
+- text: "here, da = direct assessment, rr = relative ranking, ds = discrete scale and cs = continuous scale."
+  example_title: "Uncased 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 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 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 4"
 pipeline_tag: token-classification
+co2_eq_emissions:
+  emissions: 31.203903222402037
+  source: codecarbon
+  training_type: fine-tuning
+  on_cloud: false
+  cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K
+  ram_total_size: 31.777088165283203
+  hours_used: 0.272
+  hardware_used: 1 x NVIDIA GeForce RTX 3090
+base_model: bert-base-uncased
+model-index:
+- name: SpanMarker with bert-base-uncased on Acronym Identification
+  results:
+  - task:
+      type: token-classification
+      name: Named Entity Recognition
+    dataset:
+      name: Acronym Identification
+      type: acronym_identification
+      split: validation
+    metrics:
+    - type: f1
+      value: 0.9198933333333332
+      name: F1
+    - type: precision
+      value: 0.9339397877409573
+      name: Precision
+    - type: recall
+      value: 0.9062631357713324
+      name: Recall
 ---
 
-# SpanMarker
+# SpanMarker with bert-base-uncased on Acronym Identification
 
-This is a [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) model that can be used for Named Entity Recognition.
+This is a [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) model trained on the [Acronym Identification](https://huggingface.co/datasets/acronym_identification) dataset that can be used for Named Entity Recognition. 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 always capitalized correctly? Then consider using the cased variant of this model instead for better performance: tomaarsen/span-marker-bert-base-acronyms.
 
 ## Model Details
 
 ### Model Description
 
 - **Model Type:** SpanMarker
-<!-- - **Encoder:** [Unknown](https://huggingface.co/unknown) -->
+- **Encoder:** [bert-base-uncased](https://huggingface.co/bert-base-uncased)
 - **Maximum Sequence Length:** 256 tokens
 - **Maximum Entity Length:** 8 words
-<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
-<!-- - **Language:** Unknown -->
-<!-- - **License:** Unknown -->
+- **Training Dataset:** [Acronym Identification](https://huggingface.co/datasets/acronym_identification)
+- **Language:** en
+- **License:** apache-2.0
 
 ### Model Sources
 
 - **Repository:** [SpanMarker on GitHub](https://github.com/tomaarsen/SpanMarkerNER)
 - **Thesis:** [SpanMarker For Named Entity Recognition](https://raw.githubusercontent.com/tomaarsen/SpanMarkerNER/main/thesis.pdf)
 
+### Model Labels
+| Label | Examples                                                                                              |
+|:------|:------------------------------------------------------------------------------------------------------|
+| long  | "successive convex approximation", "controlled natural language", "Conversational Question Answering" |
+| short | "SODA", "CNL", "CoQA"                                                                                 |
+
+## Evaluation
+
+### Metrics
+| Label   | Precision | Recall | F1     |
+|:--------|:----------|:-------|:-------|
+| **all** | 0.9339    | 0.9063 | 0.9199 |
+| long    | 0.9314    | 0.8845 | 0.9074 |
+| short   | 0.9352    | 0.9174 | 0.9262 |
+
 ## Uses
 
 ### Direct Use for Inference
@@ -43,9 +103,9 @@ This is a [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) model that ca
 from span_marker import SpanMarkerModel
 
 # Download from the 🤗 Hub
-model = SpanMarkerModel.from_pretrained("span_marker_model_id")
+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.")
 ```
 
 ### Downstream Use
@@ -57,7 +117,7 @@ You can finetune this model on your own dataset.
 from span_marker import SpanMarkerModel, Trainer
 
 # Download from the 🤗 Hub
-model = SpanMarkerModel.from_pretrained("span_marker_model_id")
+model = SpanMarkerModel.from_pretrained("tomaarsen/span-marker-bert-base-uncased-acronyms")
 
 # Specify a Dataset with "tokens" and "ner_tag" columns
 dataset = load_dataset("conll2003") # For example CoNLL2003
@@ -69,7 +129,7 @@ trainer = Trainer(
     eval_dataset=dataset["validation"],
 )
 trainer.train()
-trainer.save_model("span_marker_model_id-finetuned")
+trainer.save_model("tomaarsen/span-marker-bert-base-uncased-acronyms-finetuned")
 ```
 </details>
 
@@ -93,6 +153,43 @@ trainer.save_model("span_marker_model_id-finetuned")
 
 ## Training Details
 
+### Training Set Metrics
+| Training set          | Min | Median  | Max |
+|:----------------------|:----|:--------|:----|
+| Sentence length       | 4   | 32.3372 | 170 |
+| Entities per sentence | 0   | 2.6775  | 24  |
+
+### Training Hyperparameters
+- 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
+| Epoch  | Step | Validation Loss | Validation Precision | Validation Recall | Validation F1 | Validation Accuracy |
+|:------:|:----:|:---------------:|:--------------------:|:-----------------:|:-------------:|:-------------------:|
+| 0.3120 | 200  | 0.0097          | 0.8999               | 0.8731            | 0.8863        | 0.9718              |
+| 0.6240 | 400  | 0.0075          | 0.9163               | 0.8995            | 0.9078        | 0.9769              |
+| 0.9360 | 600  | 0.0076          | 0.9079               | 0.9153            | 0.9116        | 0.9773              |
+| 1.2480 | 800  | 0.0069          | 0.9267               | 0.9006            | 0.9135        | 0.9778              |
+| 1.5601 | 1000 | 0.0065          | 0.9268               | 0.9044            | 0.9154        | 0.9782              |
+| 1.8721 | 1200 | 0.0065          | 0.9279               | 0.9061            | 0.9168        | 0.9787              |
+
+### Environmental Impact
+Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
+- **Carbon Emitted**: 0.031 kg of CO2
+- **Hours Used**: 0.272 hours
+
+### Training Hardware
+- **On Cloud**: No
+- **GPU Model**: 1 x NVIDIA GeForce RTX 3090
+- **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K
+- **RAM Size**: 31.78 GB
+
 ### Framework Versions
 
 - Python: 3.9.16