End of training

README.md

@@ -0,0 +1,212 @@
+---
+language:
+- de
+license: mit
+library_name: span-marker
+tags:
+- span-marker
+- token-classification
+- ner
+- named-entity-recognition
+- generated_from_span_marker_trainer
+datasets:
+- wikiann
+metrics:
+- precision
+- recall
+- f1
+widget:
+- text: Weitere Zulassungen folgten für Victoria und New South Wales 1975 und 1982
+    am High Court of Australia.
+- text: Ihr Name geht auf die Bethlehemskapelle in Prag zurück, die für die Böhmischen
+    Brüder eine wichtige Rolle spielt.
+- text: Sein Bundesliga-Debüt gab der Angreifer am 23.
+- text: Er qualifizierte sich für die Teilnahme an den Olympischen Spielen 2008 in
+    Peking und erreichte dort über 200 m die Viertelfinalrunde.
+- text: Damit trat sie die Nachfolge des Sozialdemokraten Jens Stoltenberg an.
+pipeline_tag: token-classification
+base_model: numind/generic-entity_recognition_NER-multilingual-v1
+model-index:
+- name: SpanMarker with numind/generic-entity_recognition_NER-multilingual-v1 on wikiann
+  results:
+  - task:
+      type: token-classification
+      name: Named Entity Recognition
+    dataset:
+      name: Unknown
+      type: wikiann
+      split: eval
+    metrics:
+    - type: f1
+      value: 0.9069700043471961
+      name: F1
+    - type: precision
+      value: 0.9069700043471961
+      name: Precision
+    - type: recall
+      value: 0.9069700043471961
+      name: Recall
+---
+
+# SpanMarker with numind/generic-entity_recognition_NER-multilingual-v1 on wikiann
+
+This is a [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) model trained on the [wikiann](https://huggingface.co/datasets/wikiann) dataset that can be used for Named Entity Recognition. This SpanMarker model uses [numind/generic-entity_recognition_NER-multilingual-v1](https://huggingface.co/numind/generic-entity_recognition_NER-multilingual-v1) as the underlying encoder.
+
+## Model Details
+
+### Model Description
+- **Model Type:** SpanMarker
+- **Encoder:** [numind/generic-entity_recognition_NER-multilingual-v1](https://huggingface.co/numind/generic-entity_recognition_NER-multilingual-v1)
+- **Maximum Sequence Length:** 256 tokens
+- **Maximum Entity Length:** 9 words
+- **Training Dataset:** [wikiann](https://huggingface.co/datasets/wikiann)
+- **Language:** de
+- **License:** mit
+
+### 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                                                            |
+|:------|:--------------------------------------------------------------------|
+| LOC   | "Savoyer Voralpen", "Bagan", "Zechin"                               |
+| ORG   | "NHL Entry Draft", "SKA Sankt Petersburg", "Minnesota Wild"         |
+| PER   | "Antonina Wladimirowna Kriwoschapka", "Lou Salomé", "Jaan Kirsipuu" |
+
+## Evaluation
+
+### Metrics
+| Label   | Precision | Recall | F1     |
+|:--------|:----------|:-------|:-------|
+| **all** | 0.9070    | 0.9070 | 0.9070 |
+| LOC     | 0.9036    | 0.9298 | 0.9165 |
+| ORG     | 0.8638    | 0.8446 | 0.8541 |
+| PER     | 0.9507    | 0.9405 | 0.9455 |
+
+## 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("Sein Bundesliga-Debüt gab der Angreifer am 23.")
+```
+
+### 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       | 1   | 9.7693 | 85  |
+| Entities per sentence | 1   | 1.3821 | 20  |
+
+### Training Hyperparameters
+- learning_rate: 5e-05
+- train_batch_size: 64
+- eval_batch_size: 128
+- seed: 42
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 128
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 10
+- mixed_precision_training: Native AMP
+
+### Training Results
+| Epoch  | Step | Validation Loss | Validation Precision | Validation Recall | Validation F1 | Validation Accuracy |
+|:------:|:----:|:---------------:|:--------------------:|:-----------------:|:-------------:|:-------------------:|
+| 1.2658 | 200  | 0.0172          | 0.8842               | 0.8534            | 0.8686        | 0.9586              |
+| 2.5316 | 400  | 0.0145          | 0.8977               | 0.8889            | 0.8933        | 0.9670              |
+| 3.7975 | 600  | 0.0161          | 0.8962               | 0.9006            | 0.8984        | 0.9688              |
+| 5.0633 | 800  | 0.0180          | 0.8982               | 0.8996            | 0.8989        | 0.9689              |
+| 6.3291 | 1000 | 0.0201          | 0.9014               | 0.9008            | 0.9011        | 0.9694              |
+| 7.5949 | 1200 | 0.0201          | 0.9010               | 0.9057            | 0.9033        | 0.9702              |
+| 8.8608 | 1400 | 0.0217          | 0.9062               | 0.9036            | 0.9049        | 0.9702              |
+
+### 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.*
+-->

added_tokens.json

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+}

config.json

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+{
+  "architectures": [
+    "SpanMarkerModel"
+  ],
+  "encoder": {
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+    "add_cross_attention": false,
+    "architectures": [
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+    "id2label": {
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+      "1": "B-PER",
+      "2": "I-PER",
+      "3": "B-ORG",
+      "4": "I-ORG",
+      "5": "B-LOC",
+      "6": "I-LOC"
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+      "B-PER": 1,
+      "I-LOC": 6,
+      "I-ORG": 4,
+      "I-PER": 2,
+      "O": 0
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+    "max_position_embeddings": 512,
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+    "pooler_fc_size": 768,
+    "pooler_num_attention_heads": 12,
+    "pooler_num_fc_layers": 3,
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+    "pooler_type": "first_token_transform",
+    "position_embedding_type": "absolute",
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+    "problem_type": null,
+    "pruned_heads": {},
+    "remove_invalid_values": false,
+    "repetition_penalty": 1.0,
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+    "tie_encoder_decoder": false,
+    "tie_word_embeddings": true,
+    "tokenizer_class": null,
+    "top_k": 50,
+    "top_p": 1.0,
+    "torch_dtype": "float32",
+    "torchscript": false,
+    "transformers_version": "4.35.2",
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+    "typical_p": 1.0,
+    "use_bfloat16": false,
+    "use_cache": true,
+    "vocab_size": 119552
+  },
+  "entity_max_length": 9,
+  "id2label": {
+    "0": "O",
+    "1": "LOC",
+    "2": "ORG",
+    "3": "PER"
+  },
+  "id2reduced_id": {
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+    "1": 3,
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+    "3": 2,
+    "4": 2,
+    "5": 1,
+    "6": 1
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+  "label2id": {
+    "LOC": 1,
+    "O": 0,
+    "ORG": 2,
+    "PER": 3
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+  "marker_max_length": 128,
+  "max_next_context": null,
+  "max_prev_context": null,
+  "model_max_length": 256,
+  "model_max_length_default": 512,
+  "model_type": "span-marker",
+  "span_marker_version": "1.5.0",
+  "torch_dtype": "float32",
+  "trained_with_document_context": false,
+  "transformers_version": "4.35.2",
+  "vocab_size": 119552
+}

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tokenizer_config.json

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