tollefj/nordic-ner

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,209 @@
+---
+language: en
+license: cc-by-sa-4.0
+library_name: span-marker
+tags:
+- span-marker
+- token-classification
+- ner
+- named-entity-recognition
+- generated_from_span_marker_trainer
+base_model: FacebookAI/xlm-roberta-base
+datasets:
+- norne
+metrics:
+- precision
+- recall
+- f1
+widget:
+- text: Av Boethius hand förelåg De institutione arithmetica (" Om aritmetikens grunder
+    ") i två böcker.
+- text: Hans hovedmotstander var lederen for opposisjonspartiet Movement for Democratic
+    Change, Morgan Tsvangirai.
+- text: Roddarn blir proffs efter OS.
+- text: Han blev dog diskvalificeret for at have trådt på banelinjen, og bronzemedaljen
+    gik i stedet til landsmanden Walter Dix.
+- text: Stillingen var på dette tidspunkt 1-1, men Almunias redning banede vejen for
+    et sejrsmål af danske Nicklas Bendtner.
+pipeline_tag: token-classification
+model-index:
+- name: SpanMarker with FacebookAI/xlm-roberta-base on norne
+  results:
+  - task:
+      type: token-classification
+      name: Named Entity Recognition
+    dataset:
+      name: norne
+      type: norne
+      split: test
+    metrics:
+    - type: f1
+      value: 0.9181825779313034
+      name: F1
+    - type: precision
+      value: 0.9217689611454993
+      name: Precision
+    - type: recall
+      value: 0.9146239940801036
+      name: Recall
+---
+
+# SpanMarker with FacebookAI/xlm-roberta-base on norne
+
+This is a [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) model trained on the [norne](https://huggingface.co/datasets/norne) dataset that can be used for Named Entity Recognition. This SpanMarker model uses [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) as the underlying encoder.
+
+## Model Details
+
+### Model Description
+- **Model Type:** SpanMarker
+- **Encoder:** [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base)
+- **Maximum Sequence Length:** 256 tokens
+- **Maximum Entity Length:** 8 words
+- **Training Dataset:** [norne](https://huggingface.co/datasets/norne)
+- **Language:** en
+- **License:** cc-by-sa-4.0
+
+### 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                                                     |
+|:------|:-------------------------------------------------------------|
+| LOC   | "Gran", "Leicestershire", "Den tyske antarktisekspedisjonen" |
+| MISC  | "socialdemokratiske", "nationalist", "Living Legend"         |
+| ORG   | "Stabæk", "Samlaget", "Marillion"                            |
+| PER   | "Fish", "Dmitrij Medvedev", "Guru Ardjan Dev"                |
+
+## Evaluation
+
+### Metrics
+| Label   | Precision | Recall | F1     |
+|:--------|:----------|:-------|:-------|
+| **all** | 0.9218    | 0.9146 | 0.9182 |
+| LOC     | 0.9284    | 0.9433 | 0.9358 |
+| MISC    | 0.6515    | 0.6047 | 0.6272 |
+| ORG     | 0.8951    | 0.8547 | 0.8745 |
+| PER     | 0.9513    | 0.9526 | 0.9520 |
+
+## Uses
+
+### Direct Use for Inference
+
+```python
+from span_marker import SpanMarkerModel
+
+# Download from the 🤗 Hub
+model = SpanMarkerModel.from_pretrained("span_marker_model_id")
+# Run inference
+entities = model.predict("Roddarn blir proffs efter OS.")
+```
+
+### Downstream Use
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+```python
+from span_marker import SpanMarkerModel, Trainer
+
+# Download from the 🤗 Hub
+model = SpanMarkerModel.from_pretrained("span_marker_model_id")
+
+# Specify a Dataset with "tokens" and "ner_tag" columns
+dataset = load_dataset("conll2003") # For example CoNLL2003
+
+# Initialize a Trainer using the pretrained model & dataset
+trainer = Trainer(
+    model=model,
+    train_dataset=dataset["train"],
+    eval_dataset=dataset["validation"],
+)
+trainer.train()
+trainer.save_model("span_marker_model_id-finetuned")
+```
+</details>
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Set Metrics
+| Training set          | Min | Median  | Max |
+|:----------------------|:----|:--------|:----|
+| Sentence length       | 1   | 12.8175 | 331 |
+| Entities per sentence | 0   | 1.0055  | 54  |
+
+### 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: 3
+
+### Training Results
+| Epoch  | Step  | Validation Loss | Validation Precision | Validation Recall | Validation F1 | Validation Accuracy |
+|:------:|:-----:|:---------------:|:--------------------:|:-----------------:|:-------------:|:-------------------:|
+| 0.5711 | 3000  | 0.0146          | 0.8650               | 0.8725            | 0.8687        | 0.9722              |
+| 1.1422 | 6000  | 0.0123          | 0.8994               | 0.8920            | 0.8957        | 0.9778              |
+| 1.7133 | 9000  | 0.0101          | 0.9184               | 0.8984            | 0.9083        | 0.9805              |
+| 2.2844 | 12000 | 0.0101          | 0.9198               | 0.9110            | 0.9154        | 0.9818              |
+| 2.8555 | 15000 | 0.0089          | 0.9245               | 0.9150            | 0.9197        | 0.9830              |
+
+### Framework Versions
+- Python: 3.12.2
+- SpanMarker: 1.5.0
+- Transformers: 4.38.2
+- PyTorch: 2.2.1+cu121
+- Datasets: 2.18.0
+- Tokenizers: 0.15.2
+
+## Citation
+
+### BibTeX
+```
+@software{Aarsen_SpanMarker,
+    author = {Aarsen, Tom},
+    license = {Apache-2.0},
+    title = {{SpanMarker for Named Entity Recognition}},
+    url = {https://github.com/tomaarsen/SpanMarkerNER}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

added_tokens.json

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all_results.json

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+    "test_runtime": 170.3172,
+    "test_samples_per_second": 305.401,
+    "test_steps_per_second": 9.547
+}

config.json

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