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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+{
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+ "<start>": 28996
+}

config.json

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+{
+ "architectures": [
+ "SpanMarkerModel"
+ ],
+ "encoder": {
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+ "add_cross_attention": false,
+ "architectures": [
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+ "gradient_checkpointing": false,
+ "hidden_act": "gelu",
+ "hidden_dropout_prob": 0.1,
+ "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"
+ },
+ "initializer_range": 0.02,
+ "intermediate_size": 3072,
+ "is_decoder": false,
+ "is_encoder_decoder": false,
+ "label2id": {
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+ "B-MISC": 7,
+ "B-ORG": 3,
+ "B-PER": 1,
+ "I-LOC": 6,
+ "I-MISC": 8,
+ "I-ORG": 4,
+ "I-PER": 2,
+ "O": 0
+ },
+ "layer_norm_eps": 1e-12,
+ "length_penalty": 1.0,
+ "max_length": 20,
+ "max_position_embeddings": 512,
+ "min_length": 0,
+ "model_type": "bert",
+ "no_repeat_ngram_size": 0,
+ "num_attention_heads": 12,
+ "num_beam_groups": 1,
+ "num_beams": 1,
+ "num_hidden_layers": 12,
+ "num_return_sequences": 1,
+ "output_attentions": false,
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+ "problem_type": null,
+ "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,
+ "torch_dtype": null,
+ "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": {
+ "0": 0,
+ "1": 4,
+ "2": 4,
+ "3": 3,
+ "4": 3,
+ "5": 1,
+ "6": 1,
+ "7": 2,
+ "8": 2
+ },
+ "label2id": {
+ "LOC": 1,
+ "MISC": 2,
+ "O": 0,
+ "ORG": 3,
+ "PER": 4
+ },
+ "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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special_tokens_map.json

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+ "mask_token": "[MASK]",
+ "pad_token": "[PAD]",
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tokenizer_config.json

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+ "add_prefix_space": true,
+ "added_tokens_decoder": {
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+ "lstrip": false,
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+ "tokenizer_class": "BertTokenizer",
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+}

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