End of training

README.md

@@ -0,0 +1,223 @@
+---
+language:
+- es
+license: cc-by-sa-4.0
+library_name: span-marker
+tags:
+- span-marker
+- token-classification
+- ner
+- named-entity-recognition
+- generated_from_span_marker_trainer
+datasets:
+- conll2002
+metrics:
+- precision
+- recall
+- f1
+widget:
+- text: Por otro lado, el primer ministro portugués, Antonio Guterres, presidente
+    de turno del Consejo Europeo, recibió hoy al ministro del Interior de Colombia,
+    Hugo de la Calle, enviado especial del presidente de su país, Andrés Pastrana.
+- text: Los consejeros de la Presidencia, Gaspar Zarrías, de Justicia, Carmen Hermosín,
+    y de Asuntos Sociales, Isaías Pérez Saldaña, darán comienzo mañana a los turnos
+    de comparecencias de los miembros del Gobierno andaluz en el Parlamento autonómico
+    para informar de las líneas de actuación de sus departamentos.
+- text: '(SV2147) PP: PROBLEMAS INTERNOS PSOE INTERFIEREN EN POLITICA DE LA JUNTA
+    Córdoba (EFE).'
+- text: Cuando vino a Soria, en febrero de 1998, para sustituir al entonces destituido
+    Antonio Gómez, estaba dirigiendo al Badajoz B en tercera división y consiguió
+    con el Numancia la permanencia en la última jornada frente al Hércules.
+- text: El ministro ecuatoriano de Defensa, Hugo Unda, aseguró hoy que las Fuerzas
+    Armadas respetarán la decisión del Parlamento sobre la amnistía para los involucrados
+    en la asonada golpista del pasado 21 de enero, cuando fue derrocado el presidente
+    Jamil Mahuad.
+pipeline_tag: token-classification
+base_model: bert-base-cased
+model-index:
+- name: SpanMarker with bert-base-cased on conll2002
+  results:
+  - task:
+      type: token-classification
+      name: Named Entity Recognition
+    dataset:
+      name: Unknown
+      type: conll2002
+      split: test
+    metrics:
+    - type: f1
+      value: 0.8200812536273941
+      name: F1
+    - type: precision
+      value: 0.8331367924528302
+      name: Precision
+    - type: recall
+      value: 0.8074285714285714
+      name: Recall
+---
+
+# SpanMarker with bert-base-cased on conll2002
+
+This is a [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) model trained on the [conll2002](https://huggingface.co/datasets/conll2002) dataset that can be used for Named Entity Recognition. This SpanMarker model uses [bert-base-cased](https://huggingface.co/bert-base-cased) as the underlying encoder.
+
+## Model Details
+
+### Model Description
+- **Model Type:** SpanMarker
+- **Encoder:** [bert-base-cased](https://huggingface.co/bert-base-cased)
+- **Maximum Sequence Length:** 256 tokens
+- **Maximum Entity Length:** 8 words
+- **Training Dataset:** [conll2002](https://huggingface.co/datasets/conll2002)
+- **Language:** es
+- **License:** cc-by-sa-4.0
+
+### 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   | "Victoria", "Australia", "Melbourne"                              |
+| MISC  | "Ley", "Ciudad", "CrimeNet"                                       |
+| ORG   | "Tribunal Supremo", "EFE", "Commonwealth"                         |
+| PER   | "Abogado General del Estado", "Daryl Williams", "Abogado General" |
+
+## Evaluation
+
+### Metrics
+| Label   | Precision | Recall | F1     |
+|:--------|:----------|:-------|:-------|
+| **all** | 0.8331    | 0.8074 | 0.8201 |
+| LOC     | 0.8471    | 0.7759 | 0.8099 |
+| MISC    | 0.7092    | 0.4264 | 0.5326 |
+| ORG     | 0.7854    | 0.8558 | 0.8191 |
+| PER     | 0.9471    | 0.9329 | 0.9400 |
+
+## 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("(SV2147) PP: PROBLEMAS INTERNOS PSOE INTERFIEREN EN POLITICA DE LA JUNTA Córdoba (EFE).")
+```
+
+### 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   | 31.8014 | 1238 |
+| Entities per sentence | 0   | 2.2583  | 160  |
+
+### Training Hyperparameters
+- learning_rate: 5e-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: 1
+- mixed_precision_training: Native AMP
+
+### Training Results
+| Epoch  | Step | Validation Loss | Validation Precision | Validation Recall | Validation F1 | Validation Accuracy |
+|:------:|:----:|:---------------:|:--------------------:|:-----------------:|:-------------:|:-------------------:|
+| 0.1164 | 200  | 0.0260          | 0.6907               | 0.5358            | 0.6035        | 0.9264              |
+| 0.2328 | 400  | 0.0199          | 0.7567               | 0.6384            | 0.6925        | 0.9414              |
+| 0.3491 | 600  | 0.0176          | 0.7773               | 0.7273            | 0.7515        | 0.9563              |
+| 0.4655 | 800  | 0.0157          | 0.8066               | 0.7598            | 0.7825        | 0.9601              |
+| 0.5819 | 1000 | 0.0158          | 0.8031               | 0.7413            | 0.7710        | 0.9605              |
+| 0.6983 | 1200 | 0.0156          | 0.7975               | 0.7598            | 0.7782        | 0.9609              |
+| 0.8147 | 1400 | 0.0139          | 0.8210               | 0.7615            | 0.7901        | 0.9625              |
+| 0.9310 | 1600 | 0.0129          | 0.8426               | 0.7848            | 0.8127        | 0.9651              |
+
+### Framework Versions
+- Python: 3.10.12
+- SpanMarker: 1.5.0
+- Transformers: 4.38.2
+- PyTorch: 2.2.1+cu121
+- Datasets: 2.18.0
+- Tokenizers: 0.15.2
+
+## 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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+    "architectures": [
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+    "gradient_checkpointing": false,
+    "hidden_act": "gelu",
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+    "hidden_size": 768,
+    "id2label": {
+      "0": "O",
+      "1": "B-PER",
+      "2": "I-PER",
+      "3": "B-ORG",
+      "4": "I-ORG",
+      "5": "B-LOC",
+      "6": "I-LOC",
+      "7": "B-MISC",
+      "8": "I-MISC"
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+    "is_decoder": false,
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+      "B-ORG": 3,
+      "B-PER": 1,
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+      "I-MISC": 8,
+      "I-ORG": 4,
+      "I-PER": 2,
+      "O": 0
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+    "position_embedding_type": "absolute",
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+    "pruned_heads": {},
+    "remove_invalid_values": false,
+    "repetition_penalty": 1.0,
+    "return_dict": true,
+    "return_dict_in_generate": false,
+    "sep_token_id": null,
+    "suppress_tokens": null,
+    "task_specific_params": null,
+    "temperature": 1.0,
+    "tf_legacy_loss": false,
+    "tie_encoder_decoder": false,
+    "tie_word_embeddings": true,
+    "tokenizer_class": null,
+    "top_k": 50,
+    "top_p": 1.0,
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+    "torchscript": false,
+    "transformers_version": "4.38.2",
+    "type_vocab_size": 2,
+    "typical_p": 1.0,
+    "use_bfloat16": false,
+    "use_cache": true,
+    "vocab_size": 29000
+  },
+  "entity_max_length": 8,
+  "id2label": {
+    "0": "O",
+    "1": "LOC",
+    "2": "MISC",
+    "3": "ORG",
+    "4": "PER"
+  },
+  "id2reduced_id": {
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+  "label2id": {
+    "LOC": 1,
+    "MISC": 2,
+    "O": 0,
+    "ORG": 3,
+    "PER": 4
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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": "4.38.2",
+  "vocab_size": 29000
+}

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