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--- |
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base_model: roberta-base |
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datasets: |
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- conll2003 |
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language: |
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- en |
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library_name: span-marker |
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license: apache-2.0 |
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metrics: |
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- precision |
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- recall |
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- f1 |
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pipeline_tag: token-classification |
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tags: |
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- span-marker |
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- token-classification |
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- ner |
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- named-entity-recognition |
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- generated_from_span_marker_trainer |
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widget: |
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- text: '" The worst thing that could happen for financial markets is that if Clinton |
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and Dole start to trade shots in the middle of the ring with one-upmanship, " |
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said Hugh Johnson, chief investment officer at First Albany Corp. " That''s when |
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Wall Street will need to worry . "' |
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- text: Poland revived diplomatic ties at ambassadorial level with Yugoslavia in April |
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but economic links are almost moribund, despite the end of a three-year U.N. trade |
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embargo imposed to punish Belgrade for its support of Bosnian Serbs. |
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- text: '" We believe that the Israeli settlement policy in the occupied areas is |
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an obstacle to the establishment of peace, " German Foreign Ministry spokesman |
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Martin Erdmann said.' |
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- text: U.S. Agriculture Department officials said Friday that Mexican avocados--which |
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are restricted from entering the continental United States--will not likely be |
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entering U.S. markets any time soon, even if the controversial ban were lifted |
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today. |
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- text: 3. Tristan Hoffman (Netherlands) TVM same time |
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model-index: |
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- name: SpanMarker with roberta-base on conll2003 |
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results: |
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- task: |
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type: token-classification |
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name: Named Entity Recognition |
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dataset: |
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name: Unknown |
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type: conll2003 |
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split: test |
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metrics: |
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- type: f1 |
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value: 0.9022464022464022 |
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name: F1 |
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- type: precision |
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value: 0.8943980514961726 |
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name: Precision |
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- type: recall |
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value: 0.9102337110481586 |
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name: Recall |
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--- |
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|
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# SpanMarker with roberta-base on conll2003 |
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This is a [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) model trained on the [conll2003](https://huggingface.co/datasets/conll2003) dataset that can be used for Named Entity Recognition. This SpanMarker model uses [roberta-base](https://huggingface.co/roberta-base) as the underlying encoder. |
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## Model Details |
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### Model Description |
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- **Model Type:** SpanMarker |
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- **Encoder:** [roberta-base](https://huggingface.co/roberta-base) |
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- **Maximum Sequence Length:** 256 tokens |
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- **Maximum Entity Length:** 6 words |
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- **Training Dataset:** [conll2003](https://huggingface.co/datasets/conll2003) |
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- **Language:** en |
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- **License:** apache-2.0 |
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### Model Sources |
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- **Repository:** [SpanMarker on GitHub](https://github.com/tomaarsen/SpanMarkerNER) |
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- **Thesis:** [SpanMarker For Named Entity Recognition](https://raw.githubusercontent.com/tomaarsen/SpanMarkerNER/main/thesis.pdf) |
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|
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### Model Labels |
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| Label | Examples | |
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|:------|:--------------------------------------------------------------| |
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| LOC | "BRUSSELS", "Britain", "Germany" | |
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| MISC | "British", "EU-wide", "German" | |
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| ORG | "EU", "European Commission", "European Union" | |
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| PER | "Werner Zwingmann", "Nikolaus van der Pas", "Peter Blackburn" | |
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## Evaluation |
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### Metrics |
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| Label | Precision | Recall | F1 | |
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|:--------|:----------|:-------|:-------| |
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| **all** | 0.8944 | 0.9102 | 0.9022 | |
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| LOC | 0.9220 | 0.9215 | 0.9217 | |
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| MISC | 0.7332 | 0.7949 | 0.7628 | |
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| ORG | 0.8764 | 0.8964 | 0.8863 | |
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| PER | 0.9605 | 0.9629 | 0.9617 | |
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## Uses |
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### Direct Use for Inference |
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```python |
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from span_marker import SpanMarkerModel |
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# Download from the 🤗 Hub |
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model = SpanMarkerModel.from_pretrained("span_marker_model_id") |
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# Run inference |
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entities = model.predict("3. Tristan Hoffman (Netherlands) TVM same time") |
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``` |
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### Downstream Use |
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You can finetune this model on your own dataset. |
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<details><summary>Click to expand</summary> |
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```python |
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from span_marker import SpanMarkerModel, Trainer |
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# Download from the 🤗 Hub |
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model = SpanMarkerModel.from_pretrained("span_marker_model_id") |
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# Specify a Dataset with "tokens" and "ner_tag" columns |
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dataset = load_dataset("conll2003") # For example CoNLL2003 |
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# Initialize a Trainer using the pretrained model & dataset |
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trainer = Trainer( |
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model=model, |
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train_dataset=dataset["train"], |
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eval_dataset=dataset["validation"], |
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) |
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trainer.train() |
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trainer.save_model("span_marker_model_id-finetuned") |
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``` |
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</details> |
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## Training Details |
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### Training Set Metrics |
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| Training set | Min | Median | Max | |
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|:----------------------|:----|:--------|:----| |
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| Sentence length | 1 | 14.5019 | 113 | |
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| Entities per sentence | 0 | 1.6736 | 20 | |
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### Training Hyperparameters |
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- learning_rate: 1e-05 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 8 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 1 |
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- mixed_precision_training: Native AMP |
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### Training Results |
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| Epoch | Step | Validation Loss | Validation Precision | Validation Recall | Validation F1 | Validation Accuracy | |
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|:------:|:----:|:---------------:|:--------------------:|:-----------------:|:-------------:|:-------------------:| |
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| 0.2775 | 500 | 0.0282 | 0.9105 | 0.8355 | 0.8714 | 0.9670 | |
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| 0.5549 | 1000 | 0.0166 | 0.9215 | 0.9205 | 0.9210 | 0.9824 | |
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| 0.8324 | 1500 | 0.0151 | 0.9247 | 0.9346 | 0.9296 | 0.9853 | |
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### Framework Versions |
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- Python: 3.10.12 |
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- SpanMarker: 1.5.0 |
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- Transformers: 4.41.2 |
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- PyTorch: 2.3.0+cu121 |
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- Datasets: 2.20.0 |
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- Tokenizers: 0.19.1 |
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## Citation |
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### BibTeX |
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``` |
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@software{Aarsen_SpanMarker, |
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author = {Aarsen, Tom}, |
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license = {Apache-2.0}, |
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title = {{SpanMarker for Named Entity Recognition}}, |
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url = {https://github.com/tomaarsen/SpanMarkerNER} |
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} |
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``` |
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