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P0L3
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CliReNER-sciclimatebert

Token Classification
SpanMarker
Safetensors
English
ner
named-entity-recognition
generated_from_span_marker_trainer
climate-change
earth-science
Eval Results (legacy)
Model card Files
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  1. README.md +244 -1
  2. added_tokens.json +239 -0
  3. config.json +331 -0
  4. merges.txt +0 -0
  5. model.safetensors +3 -0
  6. special_tokens_map.json +51 -0
  7. tokenizer.json +0 -0
  8. tokenizer_config.json +1956 -0
  9. training_args.bin +3 -0
  10. vocab.json +0 -0
README.md CHANGED
@@ -1,3 +1,246 @@
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  ---
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- license: mit
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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+ language: en
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+ license: cc-by-sa-4.0
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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: While a significant positive impact of solid-state cultivation using white
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+ rot fungi on enzymatic digestibility was reported in some studies [ 68 , 69 ]
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+ , a negative effect of fungal pretreatment on enzymatic hydrolysis was noted by
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+ investigators like Shi et al . ( 2009 ) [ 33 ] , who reported a glucose yield
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+ of 55 . 6 mg g − 1 of cotton stalks pretreated with P . chrysosporium , which
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+ was approximately 17 % lower than the yield of untreated cotton stalks after enzymatic
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+ hydrolysis in spite of significant lignin degradation .
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+ - text: We quantify changes in the properties and amount of bottom water entering
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+ the basin by combining repeat hydrographic observations , direct velocity measurements
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+ and flow structure derived from a 0 . 1 ° global ocean sea-ice model that realistically
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+ simulates AABW formation sites and export pathways .
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+ - text: The impact of these differences on cloud forcing can be signi or more . cant
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+ and as high as 30 W m In recent years , observations from satellite data have
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+ been revised considerably after significant development efforts , especially after
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+ utilizing new high-quality reference measurements from active sensors in space
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+ , and some datasets have also improved polar cloud detection .
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+ - text: If the response is significant , how does the solar forcing impact the EASM
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+ rainfall variability ? In this study , we will address these questions based on
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+ the simulation results derived from one AD 850 control experiment ( CTRL ) and
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+ four solar-only forcing experiments [ spectral solar irradiance ( SSI ) experiments
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+ ] , which were conducted by the Community Earth System ( CESM-LME ) Model – Last
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+ Millennium Ensemble modeling project ( Otto-Bliesner et al . 2016 ) .
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+ - text: Measurements from single moorings at each gateway reveal that the speed of
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+ bottom water flow into the Australian Antarctic Basin varies with location , season
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+ and density ( Fig . 3a , c , e ) .
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+ pipeline_tag: token-classification
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+ library_name: span-marker
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+ metrics:
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+ - precision
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+ - recall
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+ - f1
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+ datasets:
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+ - P0L3/CliReNER_v_1_1_28_SILVER
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+ base_model: P0L3/sciclimatebert
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+ model-index:
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+ - name: SpanMarker with P0L3/sciclimatebert
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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: P0L3/CliReNER_v_1_1_28_SILVER
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+ split: eval
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+ metrics:
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+ - type: f1
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+ value: 0.5863799283154122
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+ name: F1
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+ - type: precision
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+ value: 0.5859598853868195
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+ name: Precision
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+ - type: recall
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+ value: 0.5868005738880918
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+ name: Recall
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  ---
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+
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+ # SpanMarker with P0L3/sciclimatebert
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+
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+ This is a [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) model trained on the [P0L3/CliReNER_v_1_1_28_SILVER](https://huggingface.co/datasets/P0L3/CliReNER_v_1_1_28_SILVER) dataset that can be used for Named Entity Recognition. This SpanMarker model uses [P0L3/sciclimatebert](https://huggingface.co/P0L3/sciclimatebert) as the underlying encoder.
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+
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+ ## Model Details
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+
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+ ### Model Description
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+ - **Model Type:** SpanMarker
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+ - **Encoder:** [P0L3/sciclimatebert](https://huggingface.co/P0L3/sciclimatebert)
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+ - **Maximum Sequence Length:** 512 tokens
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+ - **Maximum Entity Length:** 14 words
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+ - **Training Dataset:** [P0L3/CliReNER_v_1_1_28_SILVER](https://huggingface.co/datasets/P0L3/CliReNER_v_1_1_28_SILVER)
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+ - **Language:** en
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+ - **License:** cc-by-sa-4.0
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+
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+ ### Model Sources
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+
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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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+ | Asset | "mental health", "water resources", "raw material" |
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+ | Body Part | "plant leaves", "deep tissue compartment", "leaves" |
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+ | Body of Water | "Dhaleshwari river", "rivers", "peripheral rivers" |
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+ | Chemical | "marine algal toxin", "cathode materials", "domoic acid" |
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+ | Disease | "acute neurologic signs", "chronic epileptic syndrome", "seizures" |
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+ | Ecosystem | "cloud forests", "Tropical montane cloud forest", "polluted environment" |
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+ | Energy Source | "battery cells", "fossil fuels", "12-cell series battery-pack prototype" |
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+ | Field of Study | "study", "veterinary medicine", "reference laboratory" |
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+ | Geographical Feature | "heterogenous topography", "low point", "mountainous regions" |
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+ | Intellectual Artefact | "Veterinary medical records", "data", "Daily husbandry records" |
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+ | Location | "wild", "Westbrook", "beaches" |
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+ | Mathematical Expression | "Stepwise machine hour constraints", "difference", "gradient" |
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+ | Measuring Device | "MRI scan", "station", "EEG" |
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+ | Meteorological Phenomenon | "climatic variability", "climate change", "rainfall" |
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+ | Method | "serum monitoring", "clinical efficacy", "dosing" |
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+ | Natural Disaster | "seasonal air pollution", "heavy metal contamination", "environmental pollution" |
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+ | Natural Phenomenon | "biochemical changes", "changing ocean conditions", "algal blooms" |
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+ | Organism | "Zalophus californianus", "California sea lions", "species" |
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+ | Organization | "NOAA National Marine Fisheries Service", "reference laboratory", "long-term care facility" |
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+ | Other | "reports", "normal eating", "marine mammal health" |
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+ | Person | "staff", "clinicians", "Clinicians" |
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+ | Physical Artefact | "electric vehicle", "paved east – west road", "EVs" |
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+ | Physical Phenomenon | "normal food intake", "seasonal changes", "structural abnormalities" |
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+ | Policy | "safety", "pollution", "energy security" |
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+ | Quantity | "energy density", "200 mAhg − 1", ">" |
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+ | Satellite | "satellites", "TRMM", "Tropical Rainfall Measuring Mission" |
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+ | System | "climate", "system structure", "global overturning circulation" |
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+ | Time Period | "101 days", "periods of prolonged anorexia", "several decades" |
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+
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+ ## Uses
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+
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+ ### Direct Use for Inference
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+
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+ ```python
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+ from span_marker import SpanMarkerModel
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+
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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("Measurements from single moorings at each gateway reveal that the speed of bottom water flow into the Australian Antarctic Basin varies with location , season and density ( Fig . 3a , c , e ) .")
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+ ```
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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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+
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+ <details><summary>Click to expand</summary>
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+
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+ ```python
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+ from span_marker import SpanMarkerModel, Trainer
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+
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+ # Download from the 🤗 Hub
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+ model = SpanMarkerModel.from_pretrained("span_marker_model_id")
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+
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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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+
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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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+
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+ <!--
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+ ### Out-of-Scope Use
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+
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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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+ -->
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+
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+ <!--
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+ ## Bias, Risks and Limitations
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+
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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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+ -->
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+
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+ <!--
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+ ### Recommendations
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+
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+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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+ -->
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+
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+ ## Training Details
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+
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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 | 31.4819 | 97 |
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+ | Entities per sentence | 1 | 7.0100 | 22 |
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+
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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: 3012
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+ - gradient_accumulation_steps: 2
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+ - total_train_batch_size: 16
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+ - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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+ - lr_scheduler_type: linear
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+ - lr_scheduler_warmup_ratio: 0.1
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+ - num_epochs: 20
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+
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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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+ | 1.0 | 62 | 0.1522 | 0.0 | 0.0 | 0.0 | 0.6075 |
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+ | 2.0 | 124 | 0.1065 | 0.0 | 0.0 | 0.0 | 0.6075 |
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+ | 3.0 | 186 | 0.0703 | 0.4503 | 0.2209 | 0.2964 | 0.6975 |
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+ | 4.0 | 248 | 0.0539 | 0.5494 | 0.3831 | 0.4514 | 0.7647 |
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+ | 5.0 | 310 | 0.0499 | 0.5369 | 0.5222 | 0.5295 | 0.8056 |
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+ | 6.0 | 372 | 0.0453 | 0.5947 | 0.5452 | 0.5689 | 0.8153 |
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+ | 7.0 | 434 | 0.0461 | 0.6125 | 0.5897 | 0.6009 | 0.8316 |
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+ | 8.0 | 496 | 0.0452 | 0.6033 | 0.5739 | 0.5882 | 0.8256 |
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+ | 9.0 | 558 | 0.0483 | 0.5882 | 0.5882 | 0.5882 | 0.8283 |
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+ | 10.0 | 620 | 0.0486 | 0.6175 | 0.5882 | 0.6025 | 0.8268 |
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+ | 11.0 | 682 | 0.0491 | 0.5860 | 0.5868 | 0.5864 | 0.8234 |
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+
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+ ### Framework Versions
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+ - Python: 3.10.19
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+ - SpanMarker: 1.7.0
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+ - Transformers: 4.50.0
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+ - PyTorch: 2.9.1+cu126
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+ - Datasets: 3.0.0
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+ - Tokenizers: 0.21.4
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+
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+ ## Citation
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+
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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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+
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+ <!--
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+ ## Glossary
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+
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+ *Clearly define terms in order to be accessible across audiences.*
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+ -->
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+
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+ <!--
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+ ## Model Card Authors
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+
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+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
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+ -->
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+
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+ <!--
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+ ## Model Card Contact
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+
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+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
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+ -->
added_tokens.json ADDED
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