span-marker tensorboard safetensors token-classification ner named-entity-recognition generated_from_span_marker_trainer en dataset:YurtsAI/named_entity_recognition_document_context base_model:FacebookAI/roberta-large base_model:finetune:FacebookAI/roberta-large model-index region:us
---
base_model: roberta-large
datasets:
- YurtsAI/named_entity_recognition_document_context
language:
- en
library_name: span-marker
metrics:
- precision
- recall
- f1
pipeline_tag: token-classification
tags:
- span-marker
- token-classification
- ner
- named-entity-recognition
- generated_from_span_marker_trainer
widget:
- text: '* * phone call transcript: university research paper discussion * * * * date:
    * * 09041942 * * time: * * 3:45 pm * * participants: * * dr. emily carter (ec)
    - principal investigator dr. john smith (js) - co-investigator--- * * ec: * *
    hey john, got a minute to discuss the latest draft of our paper on crispr-cas9?'
- text: monday is a chill day – beach time at barceloneta and maybe some shopping
    at la rambla.
- text: don't forget to fast for at least 8 hours before the procedure – that means
    no food or drink after midnight!
- text: whether it's buying a house in 5 years, saving for a killer vacation next
    summer, or just building an emergency fund, write it down.
- text: '- * * full integration: * * all recipes from the rbso must be incorporated
    into event menus by november 1, 2023.'
model-index:
- name: SpanMarker with roberta-large on YurtsAI/named_entity_recognition_document_context
  results:
  - task:
      type: token-classification
      name: Named Entity Recognition
    dataset:
      name: Unknown
      type: YurtsAI/named_entity_recognition_document_context
      split: eval
    metrics:
    - type: f1
      value: 0.8349078585045542
      name: F1
    - type: precision
      value: 0.8308950630296387
      name: Precision
    - type: recall
      value: 0.8389596015495296
      name: Recall
---

# SpanMarker with roberta-large on YurtsAI/named_entity_recognition_document_context

This is a [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) model trained on the [YurtsAI/named_entity_recognition_document_context](https://huggingface.co/datasets/YurtsAI/named_entity_recognition_document_context) dataset that can be used for Named Entity Recognition. This SpanMarker model uses [roberta-large](https://huggingface.co/roberta-large) as the underlying encoder.

## Model Details

### Model Description
- **Model Type:** SpanMarker
- **Encoder:** [roberta-large](https://huggingface.co/roberta-large)
- **Maximum Sequence Length:** 256 tokens
- **Maximum Entity Length:** 11 words
- **Training Dataset:** [YurtsAI/named_entity_recognition_document_context](https://huggingface.co/datasets/YurtsAI/named_entity_recognition_document_context)
- **Language:** en
<!-- - **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                                                                                                                      |
|:--------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------|
| DATETIME__absolute                                | "14:00 hrs", "15th november 2023 at 10:00 am", "october 15th , 2023"                                                          |
| DATETIME__authored                                | "25 february 26", "sunday , 21 august , 1938", "1961-05-08"                                                                   |
| DATETIME__range                                   | "29th of oct. , 2023", "september 2021 to august 2023", "jan 2022 - dec 2022"                                                 |
| DATETIME__relative                                | "eod friday", "dec 15 , 11:59 pm", "10/15"                                                                                    |
| GENERAL__art-broadcastprogram                     | "stranger things", "live q & a", "product design concept sketchbook for kids"                                                 |
| GENERAL__art-film                                 | "the crown", "kill bill", "stranger things"                                                                                   |
| GENERAL__art-music                                |                                                                                                                               |
| GENERAL__art-other                                | "statue of liberty", "broadway show", "wicked"                                                                                |
| GENERAL__art-painting                             | "draw your dream house", "design a superhero costume"                                                                         |
| GENERAL__art-writtenart                           | "optimization of quantum algorithms for cryptographic applications", "introduction to algorithms", "intro to cs '' by j. doe" |
| GENERAL__building-airport                         | "ory", "charles de gaulle", "cdg"                                                                                             |
| GENERAL__building-hospital                        | "green valley clinic", "department of oncology", "st. mary 's hospital"                                                       |
| GENERAL__building-hotel                           | "le jules verne", "hôtel ritz", "the beverly hills hotel"                                                                     |
| GENERAL__building-library                         | "ancient library", "the grand library", "jefferson library"                                                                   |
| GENERAL__building-other                           | "louvre museum", "engineering building", "eiffel tower"                                                                       |
| GENERAL__building-restaurant                      | "l'ambroisie", "bella 's bistro", "in-n-out burger"                                                                           |
| GENERAL__building-sportsfacility                  | "fenway"                                                                                                                      |
| GENERAL__building-theater                         | "gershwin theatre", "opera house", "broadway"                                                                                 |
| GENERAL__event-attack/battle/war/militaryconflict | "1863 battle of ridgefield", "battle of gettysburg", "war of 1812"                                                            |
| GENERAL__event-other                              | "annual science fair", "summer splash '23", "research methodology workshop"                                                   |
| GENERAL__event-sportsevent                        | "international olympiad in informatics", "ftx", "ioi"                                                                         |
| GENERAL__location-GPE                             | "fr", "paris ,", "italy"                                                                                                      |
| GENERAL__location-bodiesofwater                   | "river x", "river blue", "seine river"                                                                                        |
| GENERAL__location-island                          | "maldives", "similan islands", "ellis island"                                                                                 |
| GENERAL__location-mountain                        | "andes mountains", "swiss alps", "pine ridge"                                                                                 |
| GENERAL__location-other                           | "times square", "old market", "venice beach"                                                                                  |
| GENERAL__location-park                            | "central park", "ueno park", "universal studios"                                                                              |
| GENERAL__location-road/railway/highway/transit    | "i-95", "underground railroad", "hollywood walk of fame"                                                                      |
| GENERAL__organization-company                     | "green earth organics", "xyz corporation", "north atlantic fisheries"                                                         |
| GENERAL__organization-education                   | "graduate school", "xyz", "xyz university"                                                                                    |
| GENERAL__organization-government/governmentagency | "department of economic development", "moe", "ministry of environment"                                                        |
| GENERAL__organization-media/newspaper             | "pinterest", "yelp", "insta"                                                                                                  |
| GENERAL__organization-other                       | "historical society", "grants office", "admissions committee"                                                                 |
| GENERAL__organization-religion                    | "buddhist", "zen buddhist", "shinto"                                                                                          |
| GENERAL__organization-showorganization            | "phare", "the soundbytes"                                                                                                     |
| GENERAL__organization-sportsteam                  | "varsity soccer team", "red sox"                                                                                              |
| GENERAL__other-astronomything                     |                                                                                                                               |
| GENERAL__other-award                              | "team excellence award", "innovation award", "employee of the month"                                                          |
| GENERAL__other-biologything                       | "fodmap", "troponin i", "cmp"                                                                                                 |
| GENERAL__other-chemicalthing                      | "co2", "pm2.5", "nitrate"                                                                                                     |
| GENERAL__other-currency                           | "usd", "inr", "$ $ $"                                                                                                         |
| GENERAL__other-disease                            | "mi", "irritable bowel syndrome", "myocardial infarction"                                                                     |
| GENERAL__other-educationaldegree                  | "executive mba", "phd in quantum computing ,", "phd"                                                                          |
| GENERAL__other-god                                | "inari", "athena", "inari taisha"                                                                                             |
| GENERAL__other-language                           | "french", "english", "spanish"                                                                                                |
| GENERAL__other-law                                | "cas", "clean air standards", "environmental protection act ( epa ) 2023"                                                     |
| GENERAL__other-livingthing                        | "eastern box turtle", "monarch butterfly", "western burrowing owl"                                                            |
| GENERAL__other-medical                            | "asa", "dapt", "clopidogrel"                                                                                                  |
| GENERAL__person-artist/author                     | "carol", "picasso", "warhol"                                                                                                  |
| GENERAL__person-other                             | "jamie", "sarah", "mark"                                                                                                      |
| GENERAL__person-politician                        | "jane doe", "vespasian", "constantine i"                                                                                      |
| GENERAL__person-scholar                           | "dr. smith", "dr. lee", "dr. johnson"                                                                                         |
| GENERAL__person-soldier                           | "davis", "lt. sarah johnson", "col. r. johnson"                                                                               |
| GENERAL__product-airplane                         | "hmmwvs", "uh-60s", "m1a2s"                                                                                                   |
| GENERAL__product-car                              | "hmmwvs", "high mobility multipurpose wheeled vehicles", "mine-resistant ambush protected"                                    |
| GENERAL__product-food                             | "pumpkin spice", "quinoa salad", "golden jubilee feast"                                                                       |
| GENERAL__product-game                             | "stardew valley", "valorant", "call of duty : warzone"                                                                        |
| GENERAL__product-other                            | "engagement metrics", "xj-200", "smart goal templates"                                                                        |
| GENERAL__product-ship                             | "liberty island ferry", "hms victory", "thames river cruise"                                                                  |
| GENERAL__product-software                         | "instagram", "svm", "r"                                                                                                       |
| GENERAL__product-train                            | "n'ex", "shinkansen", "tgv"                                                                                                   |
| GENERAL__product-weapon                           | "m1 abrams", "m4 carbine", "m4 carbines"                                                                                      |

## Evaluation

### Metrics
| Label                                             | Precision | Recall | F1     |
|:--------------------------------------------------|:----------|:-------|:-------|
| **all**                                           | 0.8309    | 0.8390 | 0.8349 |
| DATETIME__absolute                                | 0.8744    | 0.8577 | 0.8660 |
| DATETIME__authored                                | 0.9956    | 0.9935 | 0.9946 |
| DATETIME__range                                   | 0.8451    | 0.9262 | 0.8838 |
| DATETIME__relative                                | 0.8266    | 0.7498 | 0.7863 |
| GENERAL__art-broadcastprogram                     | 0.6538    | 0.6296 | 0.6415 |
| GENERAL__art-film                                 | 0.8       | 1.0    | 0.8889 |
| GENERAL__art-music                                | 0.0       | 0.0    | 0.0    |
| GENERAL__art-other                                | 0.625     | 0.7143 | 0.6667 |
| GENERAL__art-painting                             | 0.0       | 0.0    | 0.0    |
| GENERAL__art-writtenart                           | 0.7373    | 0.8047 | 0.7695 |
| GENERAL__building-airport                         | 0.8668    | 0.9689 | 0.9150 |
| GENERAL__building-hospital                        | 0.8378    | 0.9323 | 0.8826 |
| GENERAL__building-hotel                           | 0.7577    | 0.8603 | 0.8057 |
| GENERAL__building-library                         | 0.0       | 0.0    | 0.0    |
| GENERAL__building-other                           | 0.7597    | 0.8409 | 0.7982 |
| GENERAL__building-restaurant                      | 0.7953    | 0.8695 | 0.8307 |
| GENERAL__building-sportsfacility                  | 0.0       | 0.0    | 0.0    |
| GENERAL__building-theater                         | 0.6       | 0.6667 | 0.6316 |
| GENERAL__event-attack/battle/war/militaryconflict | 0.8438    | 0.9310 | 0.8852 |
| GENERAL__event-other                              | 0.6019    | 0.6382 | 0.6195 |
| GENERAL__event-sportsevent                        | 0.0       | 0.0    | 0.0    |
| GENERAL__location-GPE                             | 0.7232    | 0.7888 | 0.7546 |
| GENERAL__location-bodiesofwater                   | 0.6724    | 0.975  | 0.7959 |
| GENERAL__location-island                          | 0.7455    | 0.9111 | 0.8200 |
| GENERAL__location-mountain                        | 0.7436    | 0.8529 | 0.7945 |
| GENERAL__location-other                           | 0.7186    | 0.7793 | 0.7477 |
| GENERAL__location-park                            | 0.7899    | 0.8704 | 0.8282 |
| GENERAL__location-road/railway/highway/transit    | 0.6325    | 0.7095 | 0.6688 |
| GENERAL__organization-company                     | 0.8665    | 0.8605 | 0.8635 |
| GENERAL__organization-education                   | 0.8256    | 0.8608 | 0.8428 |
| GENERAL__organization-government/governmentagency | 0.8344    | 0.8318 | 0.8331 |
| GENERAL__organization-media/newspaper             | 0.6667    | 0.4    | 0.5    |
| GENERAL__organization-other                       | 0.7790    | 0.8105 | 0.7944 |
| GENERAL__organization-religion                    | 0.6667    | 0.8    | 0.7273 |
| GENERAL__organization-showorganization            | 0.0       | 0.0    | 0.0    |
| GENERAL__organization-sportsteam                  | 0.0       | 0.0    | 0.0    |
| GENERAL__other-astronomything                     | 0.0       | 0.0    | 0.0    |
| GENERAL__other-award                              | 0.8216    | 0.8859 | 0.8525 |
| GENERAL__other-biologything                       | 0.7246    | 0.8961 | 0.8013 |
| GENERAL__other-chemicalthing                      | 0.7687    | 0.8047 | 0.7863 |
| GENERAL__other-currency                           | 0.6304    | 0.6744 | 0.6517 |
| GENERAL__other-disease                            | 0.8594    | 0.9048 | 0.8815 |
| GENERAL__other-educationaldegree                  | 0.7119    | 0.75   | 0.7304 |
| GENERAL__other-god                                | 0.8       | 0.5714 | 0.6667 |
| GENERAL__other-language                           | 0.6818    | 1.0    | 0.8108 |
| GENERAL__other-law                                | 0.7978    | 0.8462 | 0.8212 |
| GENERAL__other-livingthing                        | 0.7385    | 0.9320 | 0.8240 |
| GENERAL__other-medical                            | 0.7778    | 0.8343 | 0.8050 |
| GENERAL__person-artist/author                     | 0.625     | 0.3846 | 0.4762 |
| GENERAL__person-other                             | 0.8839    | 0.8979 | 0.8908 |
| GENERAL__person-politician                        | 0.7534    | 0.7432 | 0.7483 |
| GENERAL__person-scholar                           | 0.8640    | 0.8769 | 0.8704 |
| GENERAL__person-soldier                           | 0.7674    | 0.7586 | 0.7630 |
| GENERAL__product-airplane                         | 0.6774    | 0.6364 | 0.6562 |
| GENERAL__product-car                              | 0.9286    | 0.7879 | 0.8525 |
| GENERAL__product-food                             | 0.7798    | 0.7859 | 0.7828 |
| GENERAL__product-game                             | 0.75      | 0.75   | 0.75   |
| GENERAL__product-other                            | 0.7175    | 0.7537 | 0.7351 |
| GENERAL__product-ship                             | 0.0       | 0.0    | 0.0    |
| GENERAL__product-software                         | 0.8093    | 0.8403 | 0.8245 |
| GENERAL__product-train                            | 0.75      | 0.375  | 0.5    |
| GENERAL__product-weapon                           | 0.7794    | 0.8833 | 0.8281 |

## Uses

### Direct Use for Inference

```python
from span_marker import SpanMarkerModel

# Download from the 🤗 Hub
model = SpanMarkerModel.from_pretrained("YurtsAI/named_entity_recognition_document_context")
# Run inference
entities = model.predict("monday is a chill day – beach time at barceloneta and maybe some shopping at la rambla.")
```

### 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("YurtsAI/ner-document-context")

# 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("YurtsAI/named_entity_recognition_document_context-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       | 1   | 14.6796 | 691 |
| Entities per sentence | 0   | 0.4235  | 35  |

### Training Hyperparameters
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3

### Training Results
| Epoch  | Step  | Validation Loss | Validation Precision | Validation Recall | Validation F1 | Validation Accuracy |
|:------:|:-----:|:---------------:|:--------------------:|:-----------------:|:-------------:|:-------------------:|
| 0.0299 | 500   | 0.0254          | 0.5244               | 0.0116            | 0.0228        | 0.9292              |
| 0.0597 | 1000  | 0.0144          | 0.5380               | 0.3492            | 0.4235        | 0.9444              |
| 0.0896 | 1500  | 0.0099          | 0.7134               | 0.4410            | 0.5450        | 0.9534              |
| 0.1194 | 2000  | 0.0088          | 0.6461               | 0.6571            | 0.6516        | 0.9596              |
| 0.1493 | 2500  | 0.0074          | 0.7177               | 0.6363            | 0.6745        | 0.9628              |
| 0.1791 | 3000  | 0.0075          | 0.6612               | 0.7342            | 0.6958        | 0.9637              |
| 0.2090 | 3500  | 0.0073          | 0.6686               | 0.7286            | 0.6973        | 0.9634              |
| 0.2388 | 4000  | 0.0061          | 0.7552               | 0.7044            | 0.7289        | 0.9693              |
| 0.2687 | 4500  | 0.0062          | 0.7385               | 0.7150            | 0.7266        | 0.9682              |
| 0.2986 | 5000  | 0.0070          | 0.6667               | 0.7792            | 0.7186        | 0.9654              |
| 0.3284 | 5500  | 0.0063          | 0.6984               | 0.7774            | 0.7358        | 0.9689              |
| 0.3583 | 6000  | 0.0055          | 0.7941               | 0.7023            | 0.7454        | 0.9706              |
| 0.3881 | 6500  | 0.0055          | 0.7540               | 0.7640            | 0.7589        | 0.9722              |
| 0.4180 | 7000  | 0.0053          | 0.7700               | 0.7614            | 0.7657        | 0.9732              |
| 0.4478 | 7500  | 0.0053          | 0.7791               | 0.7698            | 0.7744        | 0.9742              |
| 0.4777 | 8000  | 0.0054          | 0.7396               | 0.8062            | 0.7715        | 0.9729              |
| 0.5075 | 8500  | 0.0051          | 0.7653               | 0.7944            | 0.7796        | 0.9741              |
| 0.5374 | 9000  | 0.0050          | 0.7773               | 0.7844            | 0.7808        | 0.9747              |
| 0.5672 | 9500  | 0.0049          | 0.7954               | 0.7711            | 0.7830        | 0.9757              |
| 0.5971 | 10000 | 0.0049          | 0.7844               | 0.7876            | 0.7860        | 0.9754              |
| 0.6270 | 10500 | 0.0047          | 0.7898               | 0.7940            | 0.7919        | 0.9761              |
| 0.6568 | 11000 | 0.0047          | 0.7852               | 0.7929            | 0.7890        | 0.9761              |
| 0.6867 | 11500 | 0.0047          | 0.8001               | 0.7908            | 0.7954        | 0.9770              |
| 0.7165 | 12000 | 0.0050          | 0.7643               | 0.8145            | 0.7886        | 0.9755              |
| 0.7464 | 12500 | 0.0047          | 0.7991               | 0.7892            | 0.7941        | 0.9764              |
| 0.7762 | 13000 | 0.0046          | 0.7948               | 0.8084            | 0.8015        | 0.9774              |
| 0.8061 | 13500 | 0.0046          | 0.7841               | 0.8154            | 0.7994        | 0.9771              |
| 0.8359 | 14000 | 0.0043          | 0.8283               | 0.7776            | 0.8021        | 0.9783              |
| 0.8658 | 14500 | 0.0044          | 0.8054               | 0.7993            | 0.8023        | 0.9773              |
| 0.8957 | 15000 | 0.0047          | 0.7704               | 0.8152            | 0.7922        | 0.9758              |
| 0.9255 | 15500 | 0.0043          | 0.8018               | 0.8149            | 0.8083        | 0.9782              |
| 0.9554 | 16000 | 0.0043          | 0.8255               | 0.7938            | 0.8093        | 0.9789              |
| 0.9852 | 16500 | 0.0042          | 0.8201               | 0.8008            | 0.8104        | 0.9787              |
| 1.0151 | 17000 | 0.0044          | 0.7947               | 0.8175            | 0.8059        | 0.9784              |
| 1.0449 | 17500 | 0.0044          | 0.7942               | 0.8195            | 0.8066        | 0.9777              |
| 1.0748 | 18000 | 0.0043          | 0.8124               | 0.8110            | 0.8117        | 0.9789              |
| 1.1046 | 18500 | 0.0043          | 0.7987               | 0.8157            | 0.8071        | 0.9788              |
| 1.1345 | 19000 | 0.0043          | 0.8037               | 0.8171            | 0.8103        | 0.9789              |
| 1.1644 | 19500 | 0.0042          | 0.8178               | 0.8076            | 0.8127        | 0.9796              |
| 1.1942 | 20000 | 0.0044          | 0.7803               | 0.8389            | 0.8085        | 0.9780              |
| 1.2241 | 20500 | 0.0043          | 0.8040               | 0.8210            | 0.8124        | 0.9790              |
| 1.2539 | 21000 | 0.0043          | 0.8038               | 0.8245            | 0.8141        | 0.9788              |
| 1.2838 | 21500 | 0.0041          | 0.8318               | 0.7973            | 0.8142        | 0.9794              |
| 1.3136 | 22000 | 0.0041          | 0.8106               | 0.8211            | 0.8158        | 0.9796              |
| 1.3435 | 22500 | 0.0041          | 0.8288               | 0.8046            | 0.8165        | 0.9796              |
| 1.3733 | 23000 | 0.0041          | 0.8218               | 0.8170            | 0.8194        | 0.9799              |
| 1.4032 | 23500 | 0.0042          | 0.8164               | 0.8171            | 0.8168        | 0.9799              |
| 1.4330 | 24000 | 0.0041          | 0.8105               | 0.8248            | 0.8176        | 0.9793              |
| 1.4629 | 24500 | 0.0042          | 0.8073               | 0.8196            | 0.8134        | 0.9791              |
| 1.4928 | 25000 | 0.0040          | 0.8211               | 0.8162            | 0.8187        | 0.9797              |
| 1.5226 | 25500 | 0.0040          | 0.8195               | 0.8225            | 0.8210        | 0.9800              |
| 1.5525 | 26000 | 0.0040          | 0.8372               | 0.8018            | 0.8191        | 0.9799              |
| 1.5823 | 26500 | 0.0040          | 0.8263               | 0.8161            | 0.8212        | 0.9802              |
| 1.6122 | 27000 | 0.0039          | 0.8275               | 0.8141            | 0.8208        | 0.9802              |
| 1.6420 | 27500 | 0.0040          | 0.8264               | 0.8198            | 0.8231        | 0.9804              |
| 1.6719 | 28000 | 0.0040          | 0.8218               | 0.8195            | 0.8206        | 0.9799              |
| 1.7017 | 28500 | 0.0039          | 0.8286               | 0.8195            | 0.8240        | 0.9803              |
| 1.7316 | 29000 | 0.0041          | 0.8004               | 0.8357            | 0.8177        | 0.9788              |
| 1.7615 | 29500 | 0.0040          | 0.8138               | 0.8304            | 0.8220        | 0.9801              |
| 1.7913 | 30000 | 0.0040          | 0.8160               | 0.8309            | 0.8234        | 0.9804              |
| 1.8212 | 30500 | 0.0039          | 0.8204               | 0.8262            | 0.8233        | 0.9802              |
| 1.8510 | 31000 | 0.0038          | 0.8292               | 0.8228            | 0.8260        | 0.9810              |
| 1.8809 | 31500 | 0.0039          | 0.8247               | 0.8246            | 0.8246        | 0.9806              |
| 1.9107 | 32000 | 0.0038          | 0.8267               | 0.8258            | 0.8262        | 0.9810              |
| 1.9406 | 32500 | 0.0039          | 0.8102               | 0.8398            | 0.8248        | 0.9805              |
| 1.9704 | 33000 | 0.0039          | 0.8321               | 0.8185            | 0.8253        | 0.9809              |
| 2.0003 | 33500 | 0.0038          | 0.8325               | 0.8261            | 0.8293        | 0.9814              |
| 2.0302 | 34000 | 0.0038          | 0.8352               | 0.8228            | 0.8289        | 0.9813              |
| 2.0600 | 34500 | 0.0041          | 0.8144               | 0.8369            | 0.8255        | 0.9809              |
| 2.0899 | 35000 | 0.0039          | 0.8274               | 0.8281            | 0.8277        | 0.9813              |
| 2.1197 | 35500 | 0.0039          | 0.8198               | 0.8353            | 0.8275        | 0.9812              |
| 2.1496 | 36000 | 0.0039          | 0.8211               | 0.8358            | 0.8284        | 0.9811              |
| 2.1794 | 36500 | 0.0039          | 0.8242               | 0.8300            | 0.8271        | 0.9809              |
| 2.2093 | 37000 | 0.0039          | 0.8194               | 0.8317            | 0.8255        | 0.9808              |
| 2.2391 | 37500 | 0.0039          | 0.8258               | 0.8344            | 0.8301        | 0.9814              |
| 2.2690 | 38000 | 0.0039          | 0.8292               | 0.8302            | 0.8297        | 0.9816              |
| 2.2989 | 38500 | 0.0039          | 0.8281               | 0.8315            | 0.8298        | 0.9813              |
| 2.3287 | 39000 | 0.0039          | 0.8174               | 0.8386            | 0.8279        | 0.9808              |
| 2.3586 | 39500 | 0.0039          | 0.8208               | 0.8364            | 0.8285        | 0.9810              |
| 2.3884 | 40000 | 0.0039          | 0.8230               | 0.8379            | 0.8304        | 0.9815              |
| 2.4183 | 40500 | 0.0038          | 0.8355               | 0.8273            | 0.8314        | 0.9816              |
| 2.4481 | 41000 | 0.0038          | 0.8290               | 0.8347            | 0.8319        | 0.9816              |
| 2.4780 | 41500 | 0.0038          | 0.8233               | 0.8403            | 0.8317        | 0.9815              |
| 2.5078 | 42000 | 0.0039          | 0.8186               | 0.8417            | 0.8300        | 0.9814              |
| 2.5377 | 42500 | 0.0038          | 0.8321               | 0.8343            | 0.8332        | 0.9818              |
| 2.5675 | 43000 | 0.0038          | 0.8239               | 0.8396            | 0.8317        | 0.9816              |
| 2.5974 | 43500 | 0.0038          | 0.8267               | 0.8378            | 0.8322        | 0.9816              |
| 2.6273 | 44000 | 0.0038          | 0.8325               | 0.8343            | 0.8334        | 0.9818              |
| 2.6571 | 44500 | 0.0038          | 0.8254               | 0.8399            | 0.8326        | 0.9817              |
| 2.6870 | 45000 | 0.0038          | 0.8339               | 0.8338            | 0.8339        | 0.9820              |
| 2.7168 | 45500 | 0.0038          | 0.8301               | 0.8381            | 0.8341        | 0.9819              |
| 2.7467 | 46000 | 0.0038          | 0.8309               | 0.8371            | 0.8340        | 0.9818              |
| 2.7765 | 46500 | 0.0038          | 0.8296               | 0.8377            | 0.8337        | 0.9817              |
| 2.8064 | 47000 | 0.0037          | 0.8337               | 0.8349            | 0.8343        | 0.9820              |
| 2.8362 | 47500 | 0.0037          | 0.8303               | 0.8387            | 0.8345        | 0.9820              |
| 2.8661 | 48000 | 0.0037          | 0.8289               | 0.8401            | 0.8344        | 0.9819              |
| 2.8960 | 48500 | 0.0037          | 0.8299               | 0.8400            | 0.8349        | 0.9820              |
| 2.9258 | 49000 | 0.0037          | 0.8289               | 0.8401            | 0.8344        | 0.9819              |
| 2.9557 | 49500 | 0.0037          | 0.8322               | 0.8380            | 0.8351        | 0.9821              |
| 2.9855 | 50000 | 0.0037          | 0.8312               | 0.8384            | 0.8348        | 0.9820              |

### Framework Versions
- Python: 3.11.7
- SpanMarker: 1.5.0
- Transformers: 4.42.1
- PyTorch: 2.1.1+cu121
- Datasets: 2.14.5
- Tokenizers: 0.19.1

## Citation

### BibTeX
```
@software{Aarsen_SpanMarker,
    author = {Aarsen, Tom},
    license = {Apache-2.0},
    title = {{SpanMarker for Named Entity Recognition}},
    url = {https://github.com/tomaarsen/SpanMarkerNER}
}
```

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