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---
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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