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--- |
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language: |
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- en |
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license: cc-by-sa-4.0 |
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library_name: span-marker |
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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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datasets: |
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- tomaarsen/ner-orgs |
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metrics: |
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- precision |
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- recall |
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- f1 |
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widget: |
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- text: Today in Zhongnanhai, General Secretary of the Communist Party of China, President |
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of the country and honorary President of China's Red Cross, Zemin Jiang met with |
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representatives of the 6th National Member Congress of China's Red Cross, and |
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expressed warm greetings to the 20 million hardworking members on behalf of the |
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Central Committee of the Chinese Communist Party and State Council. |
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- text: On April 20, 2017, MGM Television Studios, headed by Mark Burnett formed a |
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partnership with McLane and Buss to produce and distribute new content across |
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a number of media platforms. |
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- text: 'Postponed: East Fife v Clydebank, St Johnstone v' |
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- text: Prime contractor was Hughes Aircraft Company Electronics Division which developed |
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the Tiamat with the assistance of the NACA. |
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- text: After graduating from Auburn University with a degree in Engineering in 1985, |
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he went on to play inside linebacker for the Pittsburgh Steelers for four seasons. |
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pipeline_tag: token-classification |
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co2_eq_emissions: |
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emissions: 248.1008753496152 |
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source: codecarbon |
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training_type: fine-tuning |
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on_cloud: false |
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cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K |
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ram_total_size: 31.777088165283203 |
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hours_used: 1.766 |
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hardware_used: 1 x NVIDIA GeForce RTX 3090 |
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base_model: bert-base-cased |
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model-index: |
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- name: SpanMarker with bert-base-cased on FewNERD, CoNLL2003, and OntoNotes v5 |
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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: FewNERD, CoNLL2003, and OntoNotes v5 |
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type: tomaarsen/ner-orgs |
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split: test |
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metrics: |
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- type: f1 |
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value: 0.7946954813359528 |
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name: F1 |
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- type: precision |
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value: 0.7958325880879986 |
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name: Precision |
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- type: recall |
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value: 0.793561619404316 |
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name: Recall |
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--- |
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# SpanMarker with bert-base-cased on FewNERD, CoNLL2003, and OntoNotes v5 |
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This is a [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) model trained on the [FewNERD, CoNLL2003, and OntoNotes v5](https://huggingface.co/datasets/tomaarsen/ner-orgs) dataset that can be used for Named Entity Recognition. This SpanMarker model uses [bert-base-cased](https://huggingface.co/bert-base-cased) 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:** [bert-base-cased](https://huggingface.co/bert-base-cased) |
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- **Maximum Sequence Length:** 256 tokens |
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- **Maximum Entity Length:** 8 words |
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- **Training Dataset:** [FewNERD, CoNLL2003, and OntoNotes v5](https://huggingface.co/datasets/tomaarsen/ner-orgs) |
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- **Language:** en |
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- **License:** cc-by-sa-4.0 |
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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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### Model Labels |
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| Label | Examples | |
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|:------|:---------------------------------------------| |
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| ORG | "Texas Chicken", "IAEA", "Church 's Chicken" | |
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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** | 0.7958 | 0.7936 | 0.7947 | |
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| ORG | 0.7958 | 0.7936 | 0.7947 | |
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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("tomaarsen/span-marker-bert-base-orgs") |
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# Run inference |
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entities = model.predict("Postponed: East Fife v Clydebank, St Johnstone v") |
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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("tomaarsen/span-marker-bert-base-orgs") |
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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("tomaarsen/span-marker-bert-base-orgs-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 | 23.5706 | 263 | |
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| Entities per sentence | 0 | 0.7865 | 39 | |
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### Training Hyperparameters |
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- learning_rate: 5e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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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: 3 |
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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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| 0.7131 | 3000 | 0.0061 | 0.7978 | 0.7830 | 0.7904 | 0.9764 | |
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| 1.4262 | 6000 | 0.0059 | 0.8170 | 0.7843 | 0.8004 | 0.9774 | |
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| 2.1393 | 9000 | 0.0061 | 0.8221 | 0.7938 | 0.8077 | 0.9772 | |
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| 2.8524 | 12000 | 0.0062 | 0.8211 | 0.8003 | 0.8106 | 0.9780 | |
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### Environmental Impact |
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Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon). |
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- **Carbon Emitted**: 0.248 kg of CO2 |
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- **Hours Used**: 1.766 hours |
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### Training Hardware |
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- **On Cloud**: No |
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- **GPU Model**: 1 x NVIDIA GeForce RTX 3090 |
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- **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K |
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- **RAM Size**: 31.78 GB |
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### Framework Versions |
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- Python: 3.9.16 |
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- SpanMarker: 1.5.1.dev |
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- Transformers: 4.30.0 |
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- PyTorch: 2.0.1+cu118 |
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- Datasets: 2.14.0 |
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- Tokenizers: 0.13.3 |
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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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