iahlt/span-marker-xlm-roberta-base-nemo-mt-he

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,219 @@
+---
+library_name: span-marker
+tags:
+- span-marker
+- token-classification
+- ner
+- named-entity-recognition
+- generated_from_span_marker_trainer
+datasets:
+- imvladikon/nemo_corpus
+metrics:
+- precision
+- recall
+- f1
+widget:
+- text: אלי ויזל, פרופסור ב אוניברסיטת בוסטון, ש סילבר התאמץ הרבה למען זכייתו ב פרס
+    נובל ל שלום, תמך בגלוי ב מועמדותו ל משרת ה מושל.
+- text: מאמרו של תום שגב, " ה קרב על סן סימון היה או לא היה " (" ה ארץ " 105), הגיע
+    ל ידי רק ב ימים אלה.
+- text: 'רק ב דבריו של ה רב אברהם טולדאנו, משגיח ב ישיבת ה רעיון ה יהודי ו מספר 4
+    ב רשימת כך ל ה כנסת, היו כבר הוראות מעשיות: " אלוקים ייקום דמו ו אנו ניקום את
+    הוא.'
+- text: מרכז ה מידע ל זכויות ה אדם ב ה שטחים, " בצלם ", מפרסם מ פעם ל פעם דפי מידע
+    ו ב המ פרטים על ה נעשה ב ה שטחים ב תחומים שונים.
+- text: גרוסבורד נהג לבדו ב ה מכונית, ב דרכו מ ה עיר מיניאפוליס ב אינדיאנה ל נמל ה
+    תעופה של היא.
+pipeline_tag: token-classification
+model-index:
+- name: SpanMarker
+  results:
+  - task:
+      type: token-classification
+      name: Named Entity Recognition
+    dataset:
+      name: Unknown
+      type: imvladikon/nemo_corpus
+      split: test
+    metrics:
+    - type: f1
+      value: 0.7757111597374179
+      name: F1
+    - type: precision
+      value: 0.7912946428571429
+      name: Precision
+    - type: recall
+      value: 0.7607296137339056
+      name: Recall
+---
+
+# SpanMarker
+
+This is a [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) model trained on the [imvladikon/nemo_corpus](https://huggingface.co/datasets/imvladikon/nemo_corpus) 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:** 512 tokens
+- **Maximum Entity Length:** 100 words
+- **Training Dataset:** [imvladikon/nemo_corpus](https://huggingface.co/datasets/imvladikon/nemo_corpus)
+<!-- - **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                                        |
+|:------|:------------------------------------------------|
+| ANG   | "יידיש", "אנגלית", "גרמנית"                     |
+| DUC   | "סובארו", "מרצדס", "דינמיט"                     |
+| EVE   | "מצדה", "הצהרת בלפור", "ה שואה"                 |
+| FAC   | "ברזילי", "תל - ה שומר", "כלא עזה"              |
+| GPE   | "שפרעם", "רצועת עזה", "ה שטחים"                 |
+| LOC   | "חאן יונס", "גיבאליה", "שייח רדואן"             |
+| ORG   | "ה ארץ", "מרחב ה גליל", "כך"                    |
+| PER   | "נימר חוסיין", "איברהים נימר חוסיין", "רמי רהב" |
+| WOA   | "ה ארץ", "קדיש", "קיטש ו מוות"                  |
+
+## Evaluation
+
+### Metrics
+| Label   | Precision | Recall | F1     |
+|:--------|:----------|:-------|:-------|
+| **all** | 0.7913    | 0.7607 | 0.7757 |
+| ANG     | 0.0       | 0.0    | 0.0    |
+| DUC     | 0.0       | 0.0    | 0.0    |
+| FAC     | 0.3571    | 0.4545 | 0.4    |
+| GPE     | 0.7817    | 0.7897 | 0.7857 |
+| LOC     | 0.5263    | 0.4878 | 0.5063 |
+| ORG     | 0.7854    | 0.7623 | 0.7736 |
+| PER     | 0.8725    | 0.8202 | 0.8456 |
+| WOA     | 0.0       | 0.0    | 0.0    |
+
+## Uses
+
+### Direct Use for Inference
+
+```python
+from span_marker import SpanMarkerModel
+
+# Download from the 🤗 Hub
+model = SpanMarkerModel.from_pretrained("span_marker_model_id")
+# Run inference
+entities = model.predict("גרוסבורד נהג לבדו ב ה מכונית, ב דרכו מ ה עיר מיניאפוליס ב אינדיאנה ל נמל ה תעופה של היא.")
+```
+
+### Downstream Use
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+```python
+from span_marker import SpanMarkerModel, Trainer
+
+# Download from the 🤗 Hub
+model = SpanMarkerModel.from_pretrained("span_marker_model_id")
+
+# Specify a Dataset with "tokens" and "ner_tag" columns
+dataset = load_dataset("conll2003") # For example CoNLL2003
+
+# Initialize a Trainer using the pretrained model & dataset
+trainer = Trainer(
+    model=model,
+    train_dataset=dataset["train"],
+    eval_dataset=dataset["validation"],
+)
+trainer.train()
+trainer.save_model("span_marker_model_id-finetuned")
+```
+</details>
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Set Metrics
+| Training set          | Min | Median  | Max |
+|:----------------------|:----|:--------|:----|
+| Sentence length       | 0   | 25.7252 | 117 |
+| Entities per sentence | 0   | 1.2722  | 20  |
+
+### Training Hyperparameters
+- learning_rate: 1e-05
+- train_batch_size: 2
+- eval_batch_size: 2
+- seed: 42
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 4
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 2
+- mixed_precision_training: Native AMP
+
+### Training Results
+| Epoch  | Step | Validation Loss | Validation Precision | Validation Recall | Validation F1 | Validation Accuracy |
+|:------:|:----:|:---------------:|:--------------------:|:-----------------:|:-------------:|:-------------------:|
+| 0.4393 | 1000 | 0.0083          | 0.7632               | 0.5812            | 0.6598        | 0.9477              |
+| 0.8785 | 2000 | 0.0056          | 0.8366               | 0.6774            | 0.7486        | 0.9609              |
+| 1.3178 | 3000 | 0.0052          | 0.8322               | 0.7655            | 0.7975        | 0.9714              |
+| 1.7571 | 4000 | 0.0053          | 0.8008               | 0.7735            | 0.7870        | 0.9712              |
+
+### Framework Versions
+- Python: 3.10.12
+- SpanMarker: 1.5.0
+- Transformers: 4.35.2
+- PyTorch: 2.1.0+cu118
+- 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.*
+-->

config.json

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+{
+  "architectures": [
+    "SpanMarkerModel"
+  ],
+  "encoder": {
+    "_name_or_path": "xlm-roberta-base",
+    "add_cross_attention": false,
+    "architectures": [
+      "XLMRobertaForMaskedLM"
+    ],
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+      "1": "B-ANG",
+      "2": "I-ANG",
+      "3": "E-ANG",
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+      "9": "E-EVE",
+      "10": "S-EVE",
+      "11": "I-EVE",
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+      "13": "B-FAC",
+      "14": "E-FAC",
+      "15": "I-FAC",
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+      "18": "E-GPE",
+      "19": "I-GPE",
+      "20": "S-LOC",
+      "21": "B-LOC",
+      "22": "E-LOC",
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+  "model_max_length_default": 512,
+  "model_type": "span-marker",
+  "span_marker_version": "1.5.0",
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+  "transformers_version": "4.35.2",
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