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--- |
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language: 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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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: Altitude measurements based on near - IR imaging in H and Hcont filters showed |
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that the deeper BS2 clouds were located near the methane condensation level ( |
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≈1.2bars ) , while BS1 was generally ∼500 mb above that level ( at lower pressures |
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) . |
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- text: However , our model predicts different performance for large enough memory |
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- access latency and validates the intuition that the dynamic programming algorithm |
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performs better on these machines . |
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- text: We established a P fertilizer need map based on integrating results from the |
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two systems . |
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- text: Here , we have addressed this limitation for the endodermal lineage by developing |
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a defined culture system to expand and differentiate human foregut stem cells |
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( hFSCs ) derived from hPSCs . hFSCs can self - renew while maintaining their |
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capacity to differentiate into pancreatic and hepatic cells . |
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- text: The accumulated percentage gain from selection amounted to 51%/1 % lower Striga |
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infestation ( measured by area under Striga number progress curve , ASNPC ) , |
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46%/62 % lower downy mildew incidence , and 49%/31 % higher panicle yield of the |
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C5 - FS compared to the mean of the genepool parents at Sadoré / Cinzana , respectively |
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. |
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pipeline_tag: token-classification |
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base_model: allenai/specter2_base |
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model-index: |
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- name: SpanMarker with allenai/specter2_base on my-data |
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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: my-data |
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type: unknown |
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split: test |
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metrics: |
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- type: f1 |
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value: 0.6906354515050167 |
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name: F1 |
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- type: precision |
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value: 0.7108433734939759 |
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name: Precision |
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- type: recall |
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value: 0.6715447154471544 |
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name: Recall |
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--- |
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# SpanMarker with allenai/specter2_base on my-data |
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This is a [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) model that can be used for Named Entity Recognition. This SpanMarker model uses [allenai/specter2_base](https://huggingface.co/allenai/specter2_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:** [allenai/specter2_base](https://huggingface.co/allenai/specter2_base) |
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- **Maximum Sequence Length:** 256 tokens |
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- **Maximum Entity Length:** 8 words |
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<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) --> |
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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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| Data | "Depth time - series", "defect", "an overall mitochondrial" | |
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| Material | "cross - shore measurement locations", "the subject 's fibroblasts", "COXI , COXII and COXIII subunits" | |
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| Method | "an approximation", "EFSA", "in vitro" | |
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| Process | "intake", "a significant reduction of synthesis", "translation" | |
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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** | 0.7108 | 0.6715 | 0.6906 | |
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| Data | 0.6591 | 0.6138 | 0.6356 | |
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| Material | 0.795 | 0.7910 | 0.7930 | |
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| Method | 0.5 | 0.45 | 0.4737 | |
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| Process | 0.6898 | 0.6293 | 0.6582 | |
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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-allenai/specter2_base-me") |
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# Run inference |
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entities = model.predict("We established a P fertilizer need map based on integrating results from the two systems .") |
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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-allenai/specter2_base-me") |
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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-allenai/specter2_base-me-finetuned") |
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``` |
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</details> |
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<!-- |
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### Out-of-Scope Use |
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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## Bias, Risks and Limitations |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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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 | 3 | 25.6049 | 106 | |
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| Entities per sentence | 0 | 5.2439 | 22 | |
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### Training Hyperparameters |
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- learning_rate: 5e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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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: 10 |
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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: 4.36.2 |
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- PyTorch: 2.0.1+cu118 |
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- Datasets: 2.16.1 |
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- Tokenizers: 0.15.0 |
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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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