Model save

README.md

@@ -0,0 +1,238 @@
+---
+library_name: span-marker
+tags:
+- span-marker
+- token-classification
+- ner
+- named-entity-recognition
+- generated_from_span_marker_trainer
+datasets:
+- SpeedOfMagic/ontonotes_english
+metrics:
+- precision
+- recall
+- f1
+widget:
+- text: Late Friday night, the Senate voted 87 - 7 to approve an estimated $13.5 billion
+    measure that had been stripped of hundreds of provisions that would have widened,
+    rather than narrowed, the federal budget deficit.
+- text: Among classes for which details were available, yields ranged from 8.78%,
+    or 75 basis points over two - year Treasury securities, to 10.05%, or 200 basis
+    points over 10 - year Treasurys.
+- text: According to statistics, in the past five years, Tianjin Bonded Area has attracted
+    a total of over 3000 enterprises from 73 countries and regions all over the world
+    and 25 domestic provinces, cities and municipalities to invest, reaching a total
+    agreed investment value of more than 3 billion US dollars and a total agreed foreign
+    investment reaching more than 2 billion US dollars.
+- text: But Dirk Van Dongen, president of the National Association of Wholesaler -
+    Distributors, said that last month's rise "isn't as bad an omen" as the 0.9% figure
+    suggests.
+- text: Robert White, Canadian Auto Workers union president, used the impending Scarborough
+    shutdown to criticize the U.S. - Canada free trade agreement and its champion,
+    Prime Minister Brian Mulroney.
+pipeline_tag: token-classification
+model-index:
+- name: SpanMarker
+  results:
+  - task:
+      type: token-classification
+      name: Named Entity Recognition
+    dataset:
+      name: Unknown
+      type: SpeedOfMagic/ontonotes_english
+      split: test
+    metrics:
+    - type: f1
+      value: 0.9077127659574469
+      name: F1
+    - type: precision
+      value: 0.9045852107076597
+      name: Precision
+    - type: recall
+      value: 0.9108620229516947
+      name: Recall
+---
+
+# SpanMarker
+
+This is a [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) model trained on the [SpeedOfMagic/ontonotes_english](https://huggingface.co/datasets/SpeedOfMagic/ontonotes_english) dataset 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:** 8 words
+- **Training Dataset:** [SpeedOfMagic/ontonotes_english](https://huggingface.co/datasets/SpeedOfMagic/ontonotes_english)
+<!-- - **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                                                                                               |
+|:------------|:-------------------------------------------------------------------------------------------------------|
+| CARDINAL    | "tens of thousands", "One point three million", "two"                                                  |
+| DATE        | "Sunday", "a year", "two thousand one"                                                                 |
+| EVENT       | "World War Two", "Katrina", "Hurricane Katrina"                                                        |
+| FAC         | "Route 80", "the White House", "Dylan 's Candy Bars"                                                   |
+| GPE         | "America", "Atlanta", "Miami"                                                                          |
+| LANGUAGE    | "English", "Russian", "Arabic"                                                                         |
+| LAW         | "Roe", "the Patriot Act", "FISA"                                                                       |
+| LOC         | "Asia", "the Gulf Coast", "the West Bank"                                                              |
+| MONEY       | "twenty - seven million dollars", "one hundred billion dollars", "less than fourteen thousand dollars" |
+| NORP        | "American", "Muslim", "Americans"                                                                      |
+| ORDINAL     | "third", "First", "first"                                                                              |
+| ORG         | "Wal - Mart", "Wal - Mart 's", "a Wal - Mart"                                                          |
+| PERCENT     | "seventeen percent", "sixty - seven percent", "a hundred percent"                                      |
+| PERSON      | "Kira Phillips", "Rick Sanchez", "Bob Shapiro"                                                         |
+| PRODUCT     | "Columbia", "Discovery Shuttle", "Discovery"                                                           |
+| QUANTITY    | "forty - five miles", "six thousand feet", "a hundred and seventy pounds"                              |
+| TIME        | "tonight", "evening", "Tonight"                                                                        |
+| WORK_OF_ART | "A Tale of Two Cities", "Newsnight", "Headline News"                                                   |
+
+## Evaluation
+
+### Metrics
+| Label       | Precision | Recall | F1     |
+|:------------|:----------|:-------|:-------|
+| **all**     | 0.9046    | 0.9109 | 0.9077 |
+| CARDINAL    | 0.8579    | 0.8524 | 0.8552 |
+| DATE        | 0.8634    | 0.8893 | 0.8762 |
+| EVENT       | 0.6719    | 0.6935 | 0.6825 |
+| FAC         | 0.7211    | 0.7852 | 0.7518 |
+| GPE         | 0.9725    | 0.9647 | 0.9686 |
+| LANGUAGE    | 0.9286    | 0.5909 | 0.7222 |
+| LAW         | 0.7941    | 0.7297 | 0.7606 |
+| LOC         | 0.7632    | 0.8101 | 0.7859 |
+| MONEY       | 0.8914    | 0.8885 | 0.8900 |
+| NORP        | 0.9311    | 0.9643 | 0.9474 |
+| ORDINAL     | 0.8227    | 0.9282 | 0.8723 |
+| ORG         | 0.9217    | 0.9073 | 0.9145 |
+| PERCENT     | 0.9145    | 0.9198 | 0.9171 |
+| PERSON      | 0.9638    | 0.9643 | 0.9640 |
+| PRODUCT     | 0.6778    | 0.8026 | 0.7349 |
+| QUANTITY    | 0.7850    | 0.8    | 0.7925 |
+| TIME        | 0.6794    | 0.6730 | 0.6762 |
+| WORK_OF_ART | 0.6562    | 0.6442 | 0.6502 |
+
+## Uses
+
+### Direct Use for Inference
+
+```python
+from span_marker import SpanMarkerModel
+
+# Download from the 🤗 Hub
+model = SpanMarkerModel.from_pretrained("supreethrao/instructNER_ontonotes5_xl")
+# Run inference
+entities = model.predict("Robert White, Canadian Auto Workers union president, used the impending Scarborough shutdown to criticize the U.S. - Canada free trade agreement and its champion, Prime Minister Brian Mulroney.")
+```
+
+### 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("supreethrao/instructNER_ontonotes5_xl")
+
+# 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("supreethrao/instructNER_ontonotes5_xl-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   | 18.1647 | 210 |
+| Entities per sentence | 0   | 1.3655  | 32  |
+
+### Training Hyperparameters
+- learning_rate: 5e-05
+- train_batch_size: 16
+- eval_batch_size: 16
+- seed: 42
+- distributed_type: multi-GPU
+- num_devices: 2
+- total_train_batch_size: 32
+- total_eval_batch_size: 32
+- 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
+- mixed_precision_training: Native AMP
+
+### Framework Versions
+- Python: 3.10.13
+- SpanMarker: 1.5.0
+- Transformers: 4.35.2
+- PyTorch: 2.1.1
+- Datasets: 2.15.0
+- Tokenizers: 0.15.0
+
+## 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.*
+-->

all_results.json

@@ -0,0 +1,119 @@
+{
+    "epoch": 3.0,
+    "test_CARDINAL": {
+        "f1": 0.8551502145922747,
+        "number": 935,
+        "precision": 0.8579117330462863,
+        "recall": 0.8524064171122995
+    },
+    "test_DATE": {
+        "f1": 0.8761552680221812,
+        "number": 1599,
+        "precision": 0.8633879781420765,
+        "recall": 0.8893058161350844
+    },
+    "test_EVENT": {
+        "f1": 0.6825396825396826,
+        "number": 62,
+        "precision": 0.671875,
+        "recall": 0.6935483870967742
+    },
+    "test_FAC": {
+        "f1": 0.7517730496453903,
+        "number": 135,
+        "precision": 0.7210884353741497,
+        "recall": 0.7851851851851852
+    },
+    "test_GPE": {
+        "f1": 0.9686098654708519,
+        "number": 2239,
+        "precision": 0.9725348941918055,
+        "recall": 0.964716391246092
+    },
+    "test_LANGUAGE": {
+        "f1": 0.7222222222222223,
+        "number": 22,
+        "precision": 0.9285714285714286,
+        "recall": 0.5909090909090909
+    },
+    "test_LAW": {
+        "f1": 0.7605633802816901,
+        "number": 37,
+        "precision": 0.7941176470588235,
+        "recall": 0.7297297297297297
+    },
+    "test_LOC": {
+        "f1": 0.7859078590785907,
+        "number": 179,
+        "precision": 0.7631578947368421,
+        "recall": 0.8100558659217877
+    },
+    "test_MONEY": {
+        "f1": 0.8899521531100479,
+        "number": 314,
+        "precision": 0.8913738019169329,
+        "recall": 0.8885350318471338
+    },
+    "test_NORP": {
+        "f1": 0.947429906542056,
+        "number": 841,
+        "precision": 0.931113662456946,
+        "recall": 0.9643281807372176
+    },
+    "test_ORDINAL": {
+        "f1": 0.8722891566265061,
+        "number": 195,
+        "precision": 0.8227272727272728,
+        "recall": 0.9282051282051282
+    },
+    "test_ORG": {
+        "f1": 0.9144625773776026,
+        "number": 1791,
+        "precision": 0.921724333522405,
+        "recall": 0.9073143495254048
+    },
+    "test_PERCENT": {
+        "f1": 0.9171428571428571,
+        "number": 349,
+        "precision": 0.9145299145299145,
+        "recall": 0.9197707736389685
+    },
+    "test_PERSON": {
+        "f1": 0.9640432486799095,
+        "number": 1988,
+        "precision": 0.9638009049773756,
+        "recall": 0.9642857142857143
+    },
+    "test_PRODUCT": {
+        "f1": 0.7349397590361447,
+        "number": 76,
+        "precision": 0.6777777777777778,
+        "recall": 0.8026315789473685
+    },
+    "test_QUANTITY": {
+        "f1": 0.7924528301886793,
+        "number": 105,
+        "precision": 0.7850467289719626,
+        "recall": 0.8
+    },
+    "test_TIME": {
+        "f1": 0.6761904761904762,
+        "number": 211,
+        "precision": 0.6794258373205742,
+        "recall": 0.6729857819905213
+    },
+    "test_WORK_OF_ART": {
+        "f1": 0.65015479876161,
+        "number": 163,
+        "precision": 0.65625,
+        "recall": 0.6441717791411042
+    },
+    "test_loss": 0.00661951769143343,
+    "test_overall_accuracy": 0.982111989942905,
+    "test_overall_f1": 0.9077127659574469,
+    "test_overall_precision": 0.9045852107076597,
+    "test_overall_recall": 0.9108620229516947,
+    "test_runtime": 34.2561,
+    "test_samples_per_second": 277.382,
+    "test_steps_per_second": 8.67
+}

final_checkpoint/README.md

@@ -0,0 +1,238 @@
+---
+library_name: span-marker
+tags:
+- span-marker
+- token-classification
+- ner
+- named-entity-recognition
+- generated_from_span_marker_trainer
+datasets:
+- SpeedOfMagic/ontonotes_english
+metrics:
+- precision
+- recall
+- f1
+widget:
+- text: Late Friday night, the Senate voted 87 - 7 to approve an estimated $13.5 billion
+    measure that had been stripped of hundreds of provisions that would have widened,
+    rather than narrowed, the federal budget deficit.
+- text: Among classes for which details were available, yields ranged from 8.78%,
+    or 75 basis points over two - year Treasury securities, to 10.05%, or 200 basis
+    points over 10 - year Treasurys.
+- text: According to statistics, in the past five years, Tianjin Bonded Area has attracted
+    a total of over 3000 enterprises from 73 countries and regions all over the world
+    and 25 domestic provinces, cities and municipalities to invest, reaching a total
+    agreed investment value of more than 3 billion US dollars and a total agreed foreign
+    investment reaching more than 2 billion US dollars.
+- text: But Dirk Van Dongen, president of the National Association of Wholesaler -
+    Distributors, said that last month's rise "isn't as bad an omen" as the 0.9% figure
+    suggests.
+- text: Robert White, Canadian Auto Workers union president, used the impending Scarborough
+    shutdown to criticize the U.S. - Canada free trade agreement and its champion,
+    Prime Minister Brian Mulroney.
+pipeline_tag: token-classification
+model-index:
+- name: SpanMarker
+  results:
+  - task:
+      type: token-classification
+      name: Named Entity Recognition
+    dataset:
+      name: Unknown
+      type: SpeedOfMagic/ontonotes_english
+      split: test
+    metrics:
+    - type: f1
+      value: 0.9077127659574469
+      name: F1
+    - type: precision
+      value: 0.9045852107076597
+      name: Precision
+    - type: recall
+      value: 0.9108620229516947
+      name: Recall
+---
+
+# SpanMarker
+
+This is a [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) model trained on the [SpeedOfMagic/ontonotes_english](https://huggingface.co/datasets/SpeedOfMagic/ontonotes_english) dataset 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:** 8 words
+- **Training Dataset:** [SpeedOfMagic/ontonotes_english](https://huggingface.co/datasets/SpeedOfMagic/ontonotes_english)
+<!-- - **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                                                                                               |
+|:------------|:-------------------------------------------------------------------------------------------------------|
+| CARDINAL    | "tens of thousands", "One point three million", "two"                                                  |
+| DATE        | "Sunday", "a year", "two thousand one"                                                                 |
+| EVENT       | "World War Two", "Katrina", "Hurricane Katrina"                                                        |
+| FAC         | "Route 80", "the White House", "Dylan 's Candy Bars"                                                   |
+| GPE         | "America", "Atlanta", "Miami"                                                                          |
+| LANGUAGE    | "English", "Russian", "Arabic"                                                                         |
+| LAW         | "Roe", "the Patriot Act", "FISA"                                                                       |
+| LOC         | "Asia", "the Gulf Coast", "the West Bank"                                                              |
+| MONEY       | "twenty - seven million dollars", "one hundred billion dollars", "less than fourteen thousand dollars" |
+| NORP        | "American", "Muslim", "Americans"                                                                      |
+| ORDINAL     | "third", "First", "first"                                                                              |
+| ORG         | "Wal - Mart", "Wal - Mart 's", "a Wal - Mart"                                                          |
+| PERCENT     | "seventeen percent", "sixty - seven percent", "a hundred percent"                                      |
+| PERSON      | "Kira Phillips", "Rick Sanchez", "Bob Shapiro"                                                         |
+| PRODUCT     | "Columbia", "Discovery Shuttle", "Discovery"                                                           |
+| QUANTITY    | "forty - five miles", "six thousand feet", "a hundred and seventy pounds"                              |
+| TIME        | "tonight", "evening", "Tonight"                                                                        |
+| WORK_OF_ART | "A Tale of Two Cities", "Newsnight", "Headline News"                                                   |
+
+## Evaluation
+
+### Metrics
+| Label       | Precision | Recall | F1     |
+|:------------|:----------|:-------|:-------|
+| **all**     | 0.9046    | 0.9109 | 0.9077 |
+| CARDINAL    | 0.8579    | 0.8524 | 0.8552 |
+| DATE        | 0.8634    | 0.8893 | 0.8762 |
+| EVENT       | 0.6719    | 0.6935 | 0.6825 |
+| FAC         | 0.7211    | 0.7852 | 0.7518 |
+| GPE         | 0.9725    | 0.9647 | 0.9686 |
+| LANGUAGE    | 0.9286    | 0.5909 | 0.7222 |
+| LAW         | 0.7941    | 0.7297 | 0.7606 |
+| LOC         | 0.7632    | 0.8101 | 0.7859 |
+| MONEY       | 0.8914    | 0.8885 | 0.8900 |
+| NORP        | 0.9311    | 0.9643 | 0.9474 |
+| ORDINAL     | 0.8227    | 0.9282 | 0.8723 |
+| ORG         | 0.9217    | 0.9073 | 0.9145 |
+| PERCENT     | 0.9145    | 0.9198 | 0.9171 |
+| PERSON      | 0.9638    | 0.9643 | 0.9640 |
+| PRODUCT     | 0.6778    | 0.8026 | 0.7349 |
+| QUANTITY    | 0.7850    | 0.8    | 0.7925 |
+| TIME        | 0.6794    | 0.6730 | 0.6762 |
+| WORK_OF_ART | 0.6562    | 0.6442 | 0.6502 |
+
+## Uses
+
+### Direct Use for Inference
+
+```python
+from span_marker import SpanMarkerModel
+
+# Download from the 🤗 Hub
+model = SpanMarkerModel.from_pretrained("supreethrao/instructNER_ontonotes5_xl")
+# Run inference
+entities = model.predict("Robert White, Canadian Auto Workers union president, used the impending Scarborough shutdown to criticize the U.S. - Canada free trade agreement and its champion, Prime Minister Brian Mulroney.")
+```
+
+### 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("supreethrao/instructNER_ontonotes5_xl")
+
+# 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("supreethrao/instructNER_ontonotes5_xl-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   | 18.1647 | 210 |
+| Entities per sentence | 0   | 1.3655  | 32  |
+
+### Training Hyperparameters
+- learning_rate: 5e-05
+- train_batch_size: 16
+- eval_batch_size: 16
+- seed: 42
+- distributed_type: multi-GPU
+- num_devices: 2
+- total_train_batch_size: 32
+- total_eval_batch_size: 32
+- 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
+- mixed_precision_training: Native AMP
+
+### Framework Versions
+- Python: 3.10.13
+- SpanMarker: 1.5.0
+- Transformers: 4.35.2
+- PyTorch: 2.1.1
+- Datasets: 2.15.0
+- Tokenizers: 0.15.0
+
+## 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.*
+-->

final_checkpoint/added_tokens.json

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

final_checkpoint/config.json

@@ -0,0 +1,249 @@
+{
+  "architectures": [
+    "SpanMarkerModel"
+  ],
+  "encoder": {
+    "_name_or_path": "numind/generic-entity_recognition_NER-v1",
+    "add_cross_attention": false,
+    "architectures": [
+      "RobertaModel"
+    ],
+    "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-PERSON",
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