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README.md

@@ -1,3 +1,244 @@
 ---
-license: mit
+language: en
+license: cc-by-sa-4.0
+tags:
+- span-marker
+- token-classification
+- ner
+- named-entity-recognition
+- generated_from_span_marker_trainer
+widget:
+- text: While a significant positive impact of solid-state cultivation using white
+    rot fungi on enzymatic digestibility was reported in some studies [ 68 , 69 ]
+    , a negative effect of fungal pretreatment on enzymatic hydrolysis was noted by
+    investigators like Shi et al . ( 2009 ) [ 33 ] , who reported a glucose yield
+    of 55 . 6 mg g − 1 of cotton stalks pretreated with P . chrysosporium , which
+    was approximately 17 % lower than the yield of untreated cotton stalks after enzymatic
+    hydrolysis in spite of significant lignin degradation .
+- text: We quantify changes in the properties and amount of bottom water entering
+    the basin by combining repeat hydrographic observations , direct velocity measurements
+    and flow structure derived from a 0 . 1 ° global ocean sea-ice model that realistically
+    simulates AABW formation sites and export pathways .
+- text: The impact of these differences on cloud forcing can be signi or more . cant
+    and as high as 30 W m In recent years , observations from satellite data have
+    been revised considerably after significant development efforts , especially after
+    utilizing new high-quality reference measurements from active sensors in space
+    , and some datasets have also improved polar cloud detection .
+- text: If the response is significant , how does the solar forcing impact the EASM
+    rainfall variability ? In this study , we will address these questions based on
+    the simulation results derived from one AD 850 control experiment ( CTRL ) and
+    four solar-only forcing experiments [ spectral solar irradiance ( SSI ) experiments
+    ] , which were conducted by the Community Earth System ( CESM-LME ) Model – Last
+    Millennium Ensemble modeling project ( Otto-Bliesner et al . 2016 ) .
+- text: 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 ) .
+pipeline_tag: token-classification
+library_name: span-marker
+metrics:
+- precision
+- recall
+- f1
+datasets:
+- P0L3/CliReNER_v_1_1_28_SILVER
+base_model: distilbert/distilroberta-base
+model-index:
+- name: SpanMarker with distilbert/distilroberta-base
+  results:
+  - task:
+      type: token-classification
+      name: Named Entity Recognition
+    dataset:
+      name: Unknown
+      type: P0L3/CliReNER_v_1_1_28_SILVER
+      split: eval
+    metrics:
+    - type: f1
+      value: 0.6
+      name: F1
+    - type: precision
+      value: 0.6106983655274889
+      name: Precision
+    - type: recall
+      value: 0.5896700143472023
+      name: Recall
 ---
+
+# SpanMarker with distilbert/distilroberta-base
+
+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 [distilbert/distilroberta-base](https://huggingface.co/distilbert/distilroberta-base) as the underlying encoder.
+
+## Model Details
+
+### Model Description
+- **Model Type:** SpanMarker
+- **Encoder:** [distilbert/distilroberta-base](https://huggingface.co/distilbert/distilroberta-base)
+- **Maximum Sequence Length:** 512 tokens
+- **Maximum Entity Length:** 14 words
+- **Training Dataset:** [P0L3/CliReNER_v_1_1_28_SILVER](https://huggingface.co/datasets/P0L3/CliReNER_v_1_1_28_SILVER)
+- **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                                                                                    |
+|:--------------------------|:--------------------------------------------------------------------------------------------|
+| Asset                     | "raw material", "water resources", "mental health"                                          |
+| Body Part                 | "leaves", "plant leaves", "deep tissue compartment"                                         |
+| Body of Water             | "rivers", "Dhaleshwari river", "peripheral rivers"                                          |
+| Chemical                  | "domoic acid", "marine algal toxin", "cathode materials"                                    |
+| Disease                   | "acute neurologic signs", "chronic epileptic syndrome", "seizures"                          |
+| Ecosystem                 | "cloud forests", "Tropical montane cloud forest", "polluted environment"                    |
+| Energy Source             | "fossil fuels", "battery cells", "12-cell series battery-pack prototype"                    |
+| Field of Study            | "veterinary medicine", "study", "reference laboratory"                                      |
+| Geographical Feature      | "mountainous regions", "low point", "heterogenous topography"                               |
+| Intellectual Artefact     | "Veterinary medical records", "Daily husbandry records", "data"                             |
+| Location                  | "wild", "Westbrook", "beaches"                                                              |
+| Mathematical Expression   | "difference", "gradient", "Stepwise machine hour constraints"                               |
+| Measuring Device          | "EEG", "MRI scan", "station"                                                                |
+| Meteorological Phenomenon | "rainfall", "climate change", "climatic variability"                                        |
+| Method                    | "clinical efficacy", "dosing", "serum monitoring"                                           |
+| Natural Disaster          | "heavy metal contamination", "environmental pollution", "seasonal air pollution"            |
+| Natural Phenomenon        | "changing ocean conditions", "algal blooms", "biochemical changes"                          |
+| Organism                  | "California sea lions", "Zalophus californianus", "species"                                 |
+| Organization              | "NOAA National Marine Fisheries Service", "long-term care facility", "reference laboratory" |
+| Other                     | "reports", "marine mammal health", "normal eating"                                          |
+| Person                    | "Clinicians", "staff", "clinicians"                                                         |
+| Physical Artefact         | "paved east – west road", "EVs", "electric vehicle"                                         |
+| Physical Phenomenon       | "seasonal changes", "normal food intake", "structural abnormalities"                        |
+| Policy                    | "safety", "energy security", "pollution"                                                    |
+| Quantity                  | ">", "200 mAhg − 1", "energy density"                                                       |
+| Satellite                 | "satellites", "TRMM", "Tropical Rainfall Measuring Mission"                                 |
+| System                    | "system structure", "climate", "global overturning circulation"                             |
+| Time Period               | "several decades", "101 days", "periods of prolonged anorexia"                              |
+
+## 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("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 ) .")
+```
+
+### 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       | 3   | 31.4819 | 97  |
+| Entities per sentence | 1   | 7.0100  | 22  |
+
+### Training Hyperparameters
+- learning_rate: 5e-05
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 3012
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 16
+- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 20
+
+### Training Results
+| Epoch | Step | Validation Loss | Validation Precision | Validation Recall | Validation F1 | Validation Accuracy |
+|:-----:|:----:|:---------------:|:--------------------:|:-----------------:|:-------------:|:-------------------:|
+| 1.0   | 62   | 0.1543          | 0.0                  | 0.0               | 0.0           | 0.6075              |
+| 2.0   | 124  | 0.0953          | 0.3810               | 0.0115            | 0.0223        | 0.6096              |
+| 3.0   | 186  | 0.0573          | 0.5535               | 0.2970            | 0.3866        | 0.7244              |
+| 4.0   | 248  | 0.0461          | 0.5996               | 0.4792            | 0.5327        | 0.7932              |
+| 5.0   | 310  | 0.0437          | 0.6058               | 0.5380            | 0.5699        | 0.8192              |
+| 6.0   | 372  | 0.0433          | 0.6036               | 0.5308            | 0.5649        | 0.8174              |
+| 7.0   | 434  | 0.0442          | 0.6121               | 0.5681            | 0.5893        | 0.8268              |
+| 8.0   | 496  | 0.0449          | 0.6196               | 0.5725            | 0.5951        | 0.8310              |
+| 9.0   | 558  | 0.0469          | 0.6107               | 0.5897            | 0.6           | 0.8316              |
+
+### Framework Versions
+- Python: 3.10.19
+- SpanMarker: 1.7.0
+- Transformers: 4.50.0
+- PyTorch: 2.9.1+cu126
+- Datasets: 3.0.0
+- Tokenizers: 0.21.4
+
+## 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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config.json

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+{
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+    "SpanMarkerModel"
+  ],
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+    "_name_or_path": "distilbert/distilroberta-base",
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tokenizer_config.json

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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+      "single_word": false,
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+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "<s>",
+  "entity_max_length": 14,
+  "eos_token": "</s>",
+  "errors": "replace",
+  "extra_special_tokens": {},
+  "marker_max_length": 256,
+  "mask_token": "<mask>",
+  "model_max_length": 512,
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "tokenizer_class": "RobertaTokenizer",
+  "trim_offsets": true,
+  "unk_token": "<unk>"
+}

training_args.bin

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