YurtsAI/ner-document-context

README.md

@@ -0,0 +1,347 @@
+---
+base_model: roberta-base
+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: We have Kanye West, Beyoncé, and Taylor Swift performing at the beachside
+    park on the island of Maui.
+- text: This book, published by Epic Games and sponsored by the University of Hawaii,
+    features recipes inspired by the popular game League of Legends and a foreword
+    by renowned food scholar, Dr. Thomas Johnson, a professor at Harvard University.
+- text: The National Institute of Technology has partnered with CafeCorp to provide
+    a menu planning template for businesses in the downtown area.
+- text: The marketing efforts for the Chicago Bulls basketball team in Wrigley Park
+    were a huge success, with 80% of attendees speaking Spanish.
+- text: The most important thing was to try using the coconut oil from a tiny store
+    near the river, and a sprinkle of Japanese spices I learned from my friend who
+    speaks fluent Japanese.
+model-index:
+- name: SpanMarker with roberta-base 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: 0.3902777777777778
+      name: F1
+    - type: precision
+      value: 0.6189427312775331
+      name: Precision
+    - type: recall
+      value: 0.28498985801217036
+      name: Recall
+---
+
+# SpanMarker with roberta-base 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-base](https://huggingface.co/roberta-base) as the underlying encoder.
+
+## Model Details
+
+### Model Description
+- **Model Type:** SpanMarker
+- **Encoder:** [roberta-base](https://huggingface.co/roberta-base)
+- **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                                                                                                                      |
+|:-----------------------------------------|:------------------------------------------------------------------------------------------------------------------------------|
+| art-broadcastprogram                     | "television program", "Origin of the Gods", "reality show"                                                                    |
+| art-film                                 | "a video of a successful grant proposal", "'The Matrix '", "film crew"                                                        |
+| art-music                                | "a new album by Beyoncé", "Yesterday by The Beatles", "favorite music CD"                                                     |
+| art-other                                | "art therapy", "play", "Mona Lisa"                                                                                            |
+| art-painting                             | "vibrant street art scene", "through art", "painting"                                                                         |
+| art-writtenart                           | "'The Lost Gods '", "Book 1", "environmental science book"                                                                    |
+| building-airport                         | "airport", "major airport", "an airport"                                                                                      |
+| building-hospital                        | "New York hospital", "local hospital", "hospital"                                                                             |
+| building-hotel                           | "hotel", "new hotel in Austin", "a giant hotel"                                                                               |
+| building-library                         | "new library", "library", "new , state-of-the-art library"                                                                    |
+| building-other                           | "10-story building", "headquarters building", "factory building"                                                              |
+| building-restaurant                      | "new restaurant", "our upscale restaurant", "restaurant"                                                                      |
+| building-sportsfacility                  | "sports facility", "Union Park Sports Complex", "city 's sports center"                                                       |
+| building-theater                         | "the local theater", "theater in downtown", "theater"                                                                         |
+| datetime-absolute                        | "January 10 , 2020", "January 17 , 2025 at 14:00", "March 25th"                                                               |
+| datetime-authored                        | "2023-02-22", "2019-04-15", "2020-02-15"                                                                                      |
+| datetime-range                           | "2010-2015", "Q4 2019", "Friday to Sunday"                                                                                    |
+| datetime-relative                        | "next week 's appointment", "last Saturday", "next week"                                                                      |
+| event-attack/battle/war/militaryconflict | "attacks/wars", "The", "A"                                                                                                    |
+| event-disaster                           | "My", "To", "disaster"                                                                                                        |
+| event-election                           | "the election for the mayor", "upcoming election", "election season"                                                          |
+| event-other                              | "conference", "annual 4th of july BBQ", "charity gala"                                                                        |
+| event-protest                            | "protest", "protest last saturday", "protest rally"                                                                           |
+| event-sportsevent                        | "sports event", "annual tennis tournament", "biggest sports event of the year"                                                |
+| location-bodiesofwater                   | "ocean", "Lake Como", "Lake Michigan"                                                                                         |
+| location-gpe                             | "Italy", "Texas", "city"                                                                                                      |
+| location-island                          | "Island Radio", "Caribbean island", "island"                                                                                  |
+| location-mountain                        | "mountain terrain", "the mountain", "mountain"                                                                                |
+| location-other                           | "low-lying areas of the city", "advertising hub", "backyard"                                                                  |
+| location-park                            | "park", "location-park", "the park"                                                                                           |
+| location-road/railway/highway/transit    | "Greyhound network", "road", "train journey"                                                                                  |
+| organization-company                     | "local company", "Verizon", "a company"                                                                                       |
+| organization-education                   | "Harvard University", "UW", "University of Arizona"                                                                           |
+| organization-government/governmentagency | "Red Cross", "local government", "SEC"                                                                                        |
+| organization-media/newspaper             | "The New York Times", "media organizations", "Army Times"                                                                     |
+| organization-other                       | "Cognizant", "Better World Foundation", "conservation organization"                                                           |
+| organization-politicalparty              | "Spaceship of Progress Party", "Libertarian Party", "Green Party"                                                             |
+| organization-religion                    | "local church", "the power of prayer", "diamatists"                                                                           |
+| organization-showorganization            | "Royal Shakespeare Company", "Earth 's Edge Theater Company", "Cosmic Theater group"                                          |
+| organization-sportsleague                | "International Swimming Federation", "NBA league", "NFL"                                                                      |
+| organization-sportsteam                  | "soccer team", "Syracuse Orange football team", "Seattle Seahawks"                                                            |
+| other-astronomything                     | "latest discoveries in the field of astronomy", "Galactic Conference Best Recipe Award-winning recipe book", "astronomy camp" |
+| other-award                              | "other-award", "annual tech show awards", "Nobel Peace Prize"                                                                 |
+| other-biologything                       | "salmon 's gene for cold adaptation", "terrain", "the forces that drive you"                                                  |
+| other-chemicalthing                      | "Overall", "The", "In"                                                                                                        |
+| other-currency                           | "US dollars", "Japanese Yen", "$ 500,000"                                                                                     |
+| other-disease                            | "malaria", "type 1 diabetes", "the common cold"                                                                               |
+| other-educationaldegree                  | "master 's degree", "thesis", "Ph.D in food science"                                                                          |
+| other-god                                | "Peter Pan", "divine", "Zeus the god"                                                                                         |
+| other-language                           | "English", "Amharic", "Sanskrit"                                                                                              |
+| other-law                                | "legislation", "professorial separation laws", "Clean Air Act"                                                                |
+| other-livingthing                        | "We", "To", "flowers"                                                                                                         |
+| other-medical                            | "antibiotics", "medical treatment", "necessary testing protocols"                                                             |
+| person-actor                             | "Emma Stone", "Dr. Steven Spielberg", "Jennifer Lawrence"                                                                     |
+| person-artist/author                     | "Chuck Close", "artist 's new album", "Jane Smith"                                                                            |
+| person-athlete                           | "athlete friend", "LeBron James", "John and Sally"                                                                            |
+| person-director                          | "John Oliver", "favorite director", "Dr. Johnson"                                                                             |
+| person-other                             | "your", "HR representative", "therapist or counselor"                                                                         |
+| person-politician                        | "To", "At", "Secretary of State"                                                                                              |
+| person-scholar                           | "Dr. John Smith", "Dr. Johnson", "a scholar of comparative religion"                                                          |
+| person-soldier                           | "veterans", "the brave soldiers", "a soldier"                                                                                 |
+| product-airplane                         | "Cessna 172", "company 's fleet of private airplanes", "airline"                                                              |
+| product-car                              | "leased car", "your car", "car"                                                                                               |
+| product-food                             | "StarBites", "food truck business", "ice cream"                                                                               |
+| product-game                             | "the 'Train to Nowhere ' game", "board game", "screen protector"                                                              |
+| product-other                            | "new medicine", "acting software", "table"                                                                                    |
+| product-ship                             | "research ship", "ship", "a ship"                                                                                             |
+| product-software                         | "software", "instruction manual", "pizza ordering app"                                                                        |
+| product-train                            | "Universal Sonicator", "train", "the train"                                                                                   |
+| product-weapon                           | "Flip Flops", "Sno Blaster", "SecurityFirst"                                                                                  |
+
+## Evaluation
+
+### Metrics
+| Label                                    | Precision | Recall | F1     |
+|:-----------------------------------------|:----------|:-------|:-------|
+| **all**                                  | 0.6189    | 0.2850 | 0.3903 |
+| art-broadcastprogram                     | 0.0       | 0.0    | 0.0    |
+| art-film                                 | 0.0       | 0.0    | 0.0    |
+| art-music                                | 0.6667    | 0.2    | 0.3077 |
+| art-other                                | 0.0       | 0.0    | 0.0    |
+| art-painting                             | 0.0       | 0.0    | 0.0    |
+| art-writtenart                           | 0.0       | 0.0    | 0.0    |
+| building-airport                         | 0.7143    | 0.7692 | 0.7407 |
+| building-hospital                        | 0.6667    | 0.7778 | 0.7179 |
+| building-hotel                           | 0.7857    | 0.6875 | 0.7333 |
+| building-library                         | 0.8182    | 0.75   | 0.7826 |
+| building-other                           | 0.0       | 0.0    | 0.0    |
+| building-restaurant                      | 0.8571    | 0.375  | 0.5217 |
+| building-sportsfacility                  | 0.6667    | 0.5    | 0.5714 |
+| building-theater                         | 0.9       | 0.5625 | 0.6923 |
+| datetime-absolute                        | 0.3333    | 0.0769 | 0.125  |
+| datetime-authored                        | 0.55      | 0.8462 | 0.6667 |
+| datetime-range                           | 0.75      | 0.5    | 0.6    |
+| datetime-relative                        | 0.0       | 0.0    | 0.0    |
+| event-attack/battle/war/militaryconflict | 0.8       | 0.2857 | 0.4211 |
+| event-disaster                           | 0.5385    | 0.5    | 0.5185 |
+| event-election                           | 0.75      | 0.5    | 0.6    |
+| event-other                              | 0.0       | 0.0    | 0.0    |
+| event-protest                            | 0.5455    | 0.4615 | 0.5000 |
+| event-sportsevent                        | 0.625     | 0.3846 | 0.4762 |
+| location-bodiesofwater                   | 0.8333    | 0.3571 | 0.5    |
+| location-gpe                             | 0.375     | 0.2143 | 0.2727 |
+| location-island                          | 0.7143    | 0.3333 | 0.4545 |
+| location-mountain                        | 0.5882    | 0.625  | 0.6061 |
+| location-other                           | 0.0       | 0.0    | 0.0    |
+| location-park                            | 0.6667    | 0.5    | 0.5714 |
+| location-road/railway/highway/transit    | 0.8       | 0.5333 | 0.64   |
+| organization-company                     | 0.0       | 0.0    | 0.0    |
+| organization-education                   | 0.3077    | 0.2857 | 0.2963 |
+| organization-government/governmentagency | 0.25      | 0.0909 | 0.1333 |
+| organization-media/newspaper             | 0.5833    | 0.4667 | 0.5185 |
+| organization-other                       | 1.0       | 0.0769 | 0.1429 |
+| organization-politicalparty              | 0.75      | 0.2727 | 0.4000 |
+| organization-religion                    | 1.0       | 0.3077 | 0.4706 |
+| organization-showorganization            | 0.75      | 0.25   | 0.375  |
+| organization-sportsleague                | 0.8571    | 0.4286 | 0.5714 |
+| organization-sportsteam                  | 0.4286    | 0.5    | 0.4615 |
+| other-astronomything                     | 0.0       | 0.0    | 0.0    |
+| other-award                              | 1.0       | 0.2143 | 0.3529 |
+| other-biologything                       | 0.0       | 0.0    | 0.0    |
+| other-chemicalthing                      | 0.4       | 0.3077 | 0.3478 |
+| other-currency                           | 1.0       | 0.2143 | 0.3529 |
+| other-disease                            | 0.5714    | 0.3077 | 0.4    |
+| other-educationaldegree                  | 0.5833    | 0.5833 | 0.5833 |
+| other-god                                | 0.8       | 0.2222 | 0.3478 |
+| other-language                           | 0.8       | 0.2857 | 0.4211 |
+| other-law                                | 0.6667    | 0.5    | 0.5714 |
+| other-livingthing                        | 0.0       | 0.0    | 0.0    |
+| other-medical                            | 0.0       | 0.0    | 0.0    |
+| person-actor                             | 0.3448    | 0.5    | 0.4082 |
+| person-artist/author                     | 0.6667    | 0.1429 | 0.2353 |
+| person-athlete                           | 0.6667    | 0.2353 | 0.3478 |
+| person-director                          | 0.2       | 0.0714 | 0.1053 |
+| person-other                             | 0.0       | 0.0    | 0.0    |
+| person-politician                        | 0.6667    | 0.0952 | 0.1667 |
+| person-scholar                           | 0.4118    | 0.4667 | 0.4375 |
+| person-soldier                           | 0.0       | 0.0    | 0.0    |
+| product-airplane                         | 0.75      | 0.3333 | 0.4615 |
+| product-car                              | 1.0       | 0.2143 | 0.3529 |
+| product-food                             | 0.0       | 0.0    | 0.0    |
+| product-game                             | 1.0       | 0.1333 | 0.2353 |
+| product-other                            | 0.5       | 0.0909 | 0.1538 |
+| product-ship                             | 0.75      | 0.3    | 0.4286 |
+| product-software                         | 1.0       | 0.4167 | 0.5882 |
+| product-train                            | 0.5556    | 0.3571 | 0.4348 |
+| product-weapon                           | 0.3333    | 0.0625 | 0.1053 |
+
+## 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("We have Kanye West, Beyoncé, and Taylor Swift performing at the beachside park on the island of Maui.")
+```
+
+### 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/named_entity_recognition_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   | 18.4126 | 309 |
+| Entities per sentence | 0   | 0.9794  | 5   |
+
+### Training Hyperparameters
+- learning_rate: 1e-05
+- train_batch_size: 4
+- eval_batch_size: 4
+- seed: 42
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 8
+- 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 |
+|:------:|:----:|:---------------:|:--------------------:|:-----------------:|:-------------:|:-------------------:|
+| 0.4322 | 500  | 0.0503          | 0.0                  | 0.0               | 0.0           | 0.8898              |
+| 0.8643 | 1000 | 0.0435          | 1.0                  | 0.0010            | 0.0020        | 0.8900              |
+| 1.2965 | 1500 | 0.0383          | 0.2841               | 0.0254            | 0.0466        | 0.8908              |
+| 1.7286 | 2000 | 0.0326          | 0.5556               | 0.0710            | 0.1259        | 0.8951              |
+| 2.1608 | 2500 | 0.0294          | 0.5806               | 0.1826            | 0.2778        | 0.9032              |
+| 2.5929 | 3000 | 0.0278          | 0.6259               | 0.2698            | 0.3770        | 0.9109              |
+
+### Framework Versions
+- Python: 3.12.2
+- SpanMarker: 1.5.0
+- Transformers: 4.41.2
+- PyTorch: 2.3.1
+- Datasets: 2.20.0
+- 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}
+}
+```
+
+<!--
+## 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

@@ -0,0 +1,4 @@
+{
+  "<end>": 50266,
+  "<start>": 50265
+}

config.json

@@ -0,0 +1,665 @@
+{
+  "architectures": [
+    "SpanMarkerModel"
+  ],
+  "encoder": {
+    "_name_or_path": "roberta-base",
+    "add_cross_attention": false,
+    "architectures": [
+      "RobertaForMaskedLM"
+    ],
+    "attention_probs_dropout_prob": 0.1,
+    "bad_words_ids": null,
+    "begin_suppress_tokens": null,
+    "bos_token_id": 0,
+    "chunk_size_feed_forward": 0,
+    "classifier_dropout": null,
+    "cross_attention_hidden_size": null,
+    "decoder_start_token_id": null,
+    "diversity_penalty": 0.0,
+    "do_sample": false,
+    "early_stopping": false,
+    "encoder_no_repeat_ngram_size": 0,
+    "eos_token_id": 2,
+    "exponential_decay_length_penalty": null,
+    "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-art-broadcastprogram",
+      "2": "I-art-broadcastprogram",
+      "3": "B-art-film",
+      "4": "I-art-film",
+      "5": "B-art-music",
+      "6": "I-art-music",
+      "7": "B-art-other",
+      "8": "I-art-other",
+      "9": "B-art-painting",
+      "10": "I-art-painting",
+      "11": "B-art-writtenart",
+      "12": "I-art-writtenart",
+      "13": "B-building-airport",
+      "14": "I-building-airport",
+      "15": "B-building-hospital",
+      "16": "I-building-hospital",
+      "17": "B-building-hotel",
+      "18": "I-building-hotel",
+      "19": "B-building-library",
+      "20": "I-building-library",
+      "21": "B-building-other",
+      "22": "I-building-other",
+      "23": "B-building-restaurant",
+      "24": "I-building-restaurant",
+      "25": "B-building-sportsfacility",
+      "26": "I-building-sportsfacility",
+      "27": "B-building-theater",
+      "28": "I-building-theater",
+      "29": "B-event-attack/battle/war/militaryconflict",
+      "30": "I-event-attack/battle/war/militaryconflict",
+      "31": "B-event-disaster",
+      "32": "I-event-disaster",
+      "33": "B-event-election",
+      "34": "I-event-election",
+      "35": "B-event-other",
+      "36": "I-event-other",
+      "37": "B-event-protest",
+      "38": "I-event-protest",
+      "39": "B-event-sportsevent",
+      "40": "I-event-sportsevent",
+      "41": "B-location-gpe",
+      "42": "I-location-gpe",
+      "43": "B-location-bodiesofwater",
+      "44": "I-location-bodiesofwater",
+      "45": "B-location-island",
+      "46": "I-location-island",
+      "47": "B-location-mountain",
+      "48": "I-location-mountain",
+      "49": "B-location-other",
+      "50": "I-location-other",
+      "51": "B-location-park",
+      "52": "I-location-park",
+      "53": "B-location-road/railway/highway/transit",
+      "54": "I-location-road/railway/highway/transit",
+      "55": "B-organization-company",
+      "56": "I-organization-company",
+      "57": "B-organization-education",
+      "58": "I-organization-education",
+      "59": "B-organization-government/governmentagency",
+      "60": "I-organization-government/governmentagency",
+      "61": "B-organization-media/newspaper",
+      "62": "I-organization-media/newspaper",
+      "63": "B-organization-other",
+      "64": "I-organization-other",
+      "65": "B-organization-politicalparty",
+      "66": "I-organization-politicalparty",
+      "67": "B-organization-religion",
+      "68": "I-organization-religion",
+      "69": "B-organization-showorganization",
+      "70": "I-organization-showorganization",
+      "71": "B-organization-sportsleague",
+      "72": "I-organization-sportsleague",
+      "73": "B-organization-sportsteam",
+      "74": "I-organization-sportsteam",
+      "75": "B-other-astronomything",
+      "76": "I-other-astronomything",
+      "77": "B-other-award",
+      "78": "I-other-award",
+      "79": "B-other-biologything",
+      "80": "I-other-biologything",
+      "81": "B-other-chemicalthing",
+      "82": "I-other-chemicalthing",
+      "83": "B-other-currency",
+      "84": "I-other-currency",
+      "85": "B-other-disease",
+      "86": "I-other-disease",
+      "87": "B-other-educationaldegree",
+      "88": "I-other-educationaldegree",
+      "89": "B-other-god",
+      "90": "I-other-god",
+      "91": "B-other-language",
+      "92": "I-other-language",
+      "93": "B-other-law",
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