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README.md

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+---
+library_name: span-marker
+tags:
+- span-marker
+- token-classification
+- ner
+- named-entity-recognition
+- generated_from_span_marker_trainer
+metrics:
+- precision
+- recall
+- f1
+widget:
+- text: The Bengal tiger is the most common subspecies of tiger, constituting approximately
+    80% of the entire tiger population, and is found in Bangladesh, Bhutan, Myanmar,
+    Nepal, and India.
+- text: In other countries, it is a non-commissioned rank (e.g. Spain, Italy, France,
+    the Netherlands and the Indonesian Police ranks).
+- text: The filling consists of fish, pork and bacon, and is seasoned with salt (unless
+    the pork is already salted).
+- text: This stood until August 20, 1993 when it was beaten by one 1 / 100th of a
+    second by Colin Jackson of Great Britain in Stuttgart, Germany, a subsequent record
+    that stood for 13 years.
+- text: Ann Patchett ’s novel " Bel Canto ", was another creative influence that helped
+    her manage a plentiful cast of characters.
+pipeline_tag: token-classification
+model-index:
+- name: SpanMarker
+  results:
+  - task:
+      type: token-classification
+      name: Named Entity Recognition
+    dataset:
+      name: Unknown
+      type: unknown
+      split: eval
+    metrics:
+    - type: f1
+      value: 0.9130661114003124
+      name: F1
+    - type: precision
+      value: 0.9148758606300855
+      name: Precision
+    - type: recall
+      value: 0.9112635078969243
+      name: Recall
+---
+
+# SpanMarker
+
+This is a [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) model that can be used for Named Entity Recognition.
+
+## Model Details
+
+### Model Description
+- **Model Type:** SpanMarker
+<!-- - **Encoder:** [Unknown](https://huggingface.co/unknown) -->
+- **Maximum Sequence Length:** 256 tokens
+- **Maximum Entity Length:** 6 words
+<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### 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                                                                 |
+|:------|:-------------------------------------------------------------------------|
+| ANIM  | "vertebrate", "moth", "G. firmus"                                        |
+| BIO   | "Aspergillus", "Cladophora", "Zythiostroma"                              |
+| CEL   | "pulsar", "celestial bodies", "neutron star"                             |
+| DIS   | "social anxiety disorder", "insulin resistance", "Asperger syndrome"     |
+| EVE   | "Spanish Civil War", "National Junior Angus Show", "French Revolution"   |
+| FOOD  | "Neera", "Bellini ( cocktail )", "soju"                                  |
+| INST  | "Apple II", "Encyclopaedia of Chess Openings", "Android"                 |
+| LOC   | "Kīlauea", "Hungary", "Vienna"                                           |
+| MEDIA | "CSI : Crime Scene Investigation", "Big Comic Spirits", "American Idol"  |
+| MYTH  | "Priam", "Oźwiena", "Odysseus"                                           |
+| ORG   | "San Francisco Giants", "Arm Holdings", "RTÉ One"                        |
+| PER   | "Amelia Bence", "Tito Lusiardo", "James Cameron"                         |
+| PLANT | "vernal squill", "Sarracenia purpurea", "Drosera rotundifolia"           |
+| TIME  | "prehistory", "Age of Enlightenment", "annual paid holiday"              |
+| VEHI  | "Short 360", "Ferrari 355 Challenge", "Solution F / Chretien Helicopter" |
+
+## 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("Ann Patchett ’s novel \" Bel Canto \", was another creative influence that helped her manage a plentiful cast of characters.")
+```
+
+### 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       | 2   | 21.6493 | 237 |
+| Entities per sentence | 0   | 1.5369  | 36  |
+
+### Training Hyperparameters
+- learning_rate: 1e-05
+- train_batch_size: 16
+- eval_batch_size: 16
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 1
+- mixed_precision_training: Native AMP
+
+### Training Results
+| Epoch  | Step  | Validation Loss | Validation Precision | Validation Recall | Validation F1 | Validation Accuracy |
+|:------:|:-----:|:---------------:|:--------------------:|:-----------------:|:-------------:|:-------------------:|
+| 0.0576 | 1000  | 0.0142          | 0.8714               | 0.7729            | 0.8192        | 0.9698              |
+| 0.1153 | 2000  | 0.0107          | 0.8316               | 0.8815            | 0.8558        | 0.9744              |
+| 0.1729 | 3000  | 0.0092          | 0.8717               | 0.8797            | 0.8757        | 0.9780              |
+| 0.2306 | 4000  | 0.0082          | 0.8811               | 0.8886            | 0.8848        | 0.9798              |
+| 0.2882 | 5000  | 0.0084          | 0.8523               | 0.9163            | 0.8831        | 0.9790              |
+| 0.3459 | 6000  | 0.0079          | 0.8700               | 0.9113            | 0.8902        | 0.9802              |
+| 0.4035 | 7000  | 0.0070          | 0.9107               | 0.8859            | 0.8981        | 0.9822              |
+| 0.4611 | 8000  | 0.0069          | 0.9259               | 0.8797            | 0.9022        | 0.9827              |
+| 0.5188 | 9000  | 0.0067          | 0.9061               | 0.8965            | 0.9013        | 0.9829              |
+| 0.5764 | 10000 | 0.0066          | 0.9034               | 0.8996            | 0.9015        | 0.9829              |
+| 0.6341 | 11000 | 0.0064          | 0.9160               | 0.8996            | 0.9077        | 0.9839              |
+| 0.6917 | 12000 | 0.0066          | 0.8952               | 0.9121            | 0.9036        | 0.9832              |
+| 0.7494 | 13000 | 0.0062          | 0.9165               | 0.9009            | 0.9086        | 0.9841              |
+| 0.8070 | 14000 | 0.0062          | 0.9010               | 0.9121            | 0.9065        | 0.9835              |
+| 0.8647 | 15000 | 0.0062          | 0.9084               | 0.9127            | 0.9105        | 0.9842              |
+| 0.9223 | 16000 | 0.0060          | 0.9151               | 0.9098            | 0.9125        | 0.9846              |
+| 0.9799 | 17000 | 0.0060          | 0.9149               | 0.9113            | 0.9131        | 0.9848              |
+
+### Framework Versions
+- Python: 3.8.16
+- SpanMarker: 1.5.0
+- Transformers: 4.29.0.dev0
+- PyTorch: 1.10.1
+- Datasets: 2.15.0
+- Tokenizers: 0.13.2
+
+## 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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+{
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+  "<start>": 50265
+}

config.json

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+{
+  "architectures": [
+    "SpanMarkerModel"
+  ],
+  "encoder": {
+    "_name_or_path": "roberta-base",
+    "add_cross_attention": false,
+    "architectures": [
+      "RobertaForMaskedLM"
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+    "finetuning_task": null,
+    "forced_bos_token_id": null,
+    "forced_eos_token_id": null,
+    "hidden_act": "gelu",
+    "hidden_dropout_prob": 0.1,
+    "hidden_size": 768,
+    "id2label": {
+      "0": "O",
+      "1": "B-PER",
+      "2": "I-PER",
+      "3": "B-ORG",
+      "4": "I-ORG",
+      "5": "B-LOC",
+      "6": "I-LOC",
+      "7": "B-ANIM",
+      "8": "I-ANIM",
+      "9": "B-BIO",
+      "10": "I-BIO",
+      "11": "B-CEL",
+      "12": "I-CEL",
+      "13": "B-DIS",
+      "14": "I-DIS",
+      "15": "B-EVE",
+      "16": "I-EVE",
+      "17": "B-FOOD",
+      "18": "I-FOOD",
+      "19": "B-INST",
+      "20": "I-INST",
+      "21": "B-MEDIA",
+      "22": "I-MEDIA",
+      "23": "B-MYTH",
+      "24": "I-MYTH",
+      "25": "B-PLANT",
+      "26": "I-PLANT",
+      "27": "B-TIME",
+      "28": "I-TIME",
+      "29": "B-VEHI",
+      "30": "I-VEHI"
+    },
+    "initializer_range": 0.02,
+    "intermediate_size": 3072,
+    "is_decoder": false,
+    "is_encoder_decoder": false,
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+      "B-BIO": 9,
+      "B-CEL": 11,
+      "B-DIS": 13,
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+      "B-LOC": 5,
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+      "B-MYTH": 23,
+      "B-ORG": 3,
+      "B-PER": 1,
+      "B-PLANT": 25,
+      "B-TIME": 27,
+      "B-VEHI": 29,
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+      "I-DIS": 14,
+      "I-EVE": 16,
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+      "I-LOC": 6,
+      "I-MEDIA": 22,
+      "I-MYTH": 24,
+      "I-ORG": 4,
+      "I-PER": 2,
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+      "I-TIME": 28,
+      "I-VEHI": 30,
+      "O": 0
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+    "length_penalty": 1.0,
+    "max_length": 20,
+    "max_position_embeddings": 514,
+    "min_length": 0,
+    "model_type": "roberta",
+    "no_repeat_ngram_size": 0,
+    "num_attention_heads": 12,
+    "num_beam_groups": 1,
+    "num_beams": 1,
+    "num_hidden_layers": 12,
+    "num_return_sequences": 1,
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+    "use_cache": true,
+    "vocab_size": 50267
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+  "entity_max_length": 6,
+  "id2label": {
+    "0": "O",
+    "1": "ANIM",
+    "2": "BIO",
+    "3": "CEL",
+    "4": "DIS",
+    "5": "EVE",
+    "6": "FOOD",
+    "7": "INST",
+    "8": "LOC",
+    "9": "MEDIA",
+    "10": "MYTH",
+    "11": "ORG",
+    "12": "PER",
+    "13": "PLANT",
+    "14": "TIME",
+    "15": "VEHI"
+  },
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+    "BIO": 2,
+    "CEL": 3,
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+    "EVE": 5,
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+    "ORG": 11,
+    "PER": 12,
+    "PLANT": 13,
+    "TIME": 14,
+    "VEHI": 15
+  },
+  "marker_max_length": 128,
+  "max_next_context": null,
+  "max_prev_context": null,
+  "model_max_length": 256,
+  "model_max_length_default": 512,
+  "model_type": "span-marker",
+  "span_marker_version": "1.5.0",
+  "torch_dtype": "float32",
+  "trained_with_document_context": false,
+  "transformers_version": "4.29.0.dev0",
+  "vocab_size": 50267
+}

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special_tokens_map.json

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tokenizer_config.json

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+  "cls_token": "<s>",
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