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README.md

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+---
+language:
+- tl
+license: gpl-3.0
+library_name: span-marker
+tags:
+- span-marker
+- token-classification
+- ner
+- named-entity-recognition
+- generated_from_span_marker_trainer
+datasets:
+- ljvmiranda921/tlunified-ner
+metrics:
+- precision
+- recall
+- f1
+widget:
+- text: MANILA - Binalewala ng Philippine National Police (PNP) nitong Sabado ang
+    posibleng paglulunsad ng tinatawag na " sympathy attacks " ng Moro National Liberation
+    Front (MNLF) at Abu Sayyaf matapos arestuhin si Indanan, Sulu Mayor Alvarez Isnaji.
+- text: Pinatawan din ng apat na buwang suspensyon si Herma Gonzales - Escudero, chief
+    revenue officer III ng BIR - Cotabato City, dahil sa kasong dishonesty at limang
+    kaso ng perjury sa Municipal Trial Court ng Cotabato City . Bunga ito ng kanyang
+    kabiguan na ideklara sa kanyang SALN noong 2002 - 2004 ang 200 metro kwadradong
+    lote sa South Cotabato at Toyota Revo noong 2001 SALN at undervaluation ng kanyang
+    mga ari - arian sa lalawigan noong 2000 - 2004 SALN.
+- text: Sa tila pagpapabaya sa mga magsasaka, sinabi ni Escudero na hindi mangyayari
+    ang pangarap ng Department of Agriculture (DA) na maging self - sufficient ang
+    Pilipinas sa bigas.
+- text: MANILA - Tiniyak ng pinuno ng Government Service Insurance System (GSIS) na
+    tatapatan nito ang pro - Meralco advertisement ni Judy Ann Santos upang isulong
+    ang kanyang posisyon na dapat ibaba ang singil sa kuryente.
+- text: Idinagdag ni South Cotabato Rep Darlene Antonino - Custodio, na illegal na
+    ipagpaliban ang halalan sa ARMM kung ang gagamitin lamang basehan ay ang ipapasang
+    panukala ng Kongreso.
+pipeline_tag: token-classification
+co2_eq_emissions:
+  emissions: 22.090476722294312
+  source: codecarbon
+  training_type: fine-tuning
+  on_cloud: false
+  cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K
+  ram_total_size: 31.777088165283203
+  hours_used: 0.238
+  hardware_used: 1 x NVIDIA GeForce RTX 3090
+base_model: bert-base-multilingual-cased
+model-index:
+- name: SpanMarker with bert-base-multilingual-cased on TLUnified
+  results:
+  - task:
+      type: token-classification
+      name: Named Entity Recognition
+    dataset:
+      name: TLUnified
+      type: ljvmiranda921/tlunified-ner
+      split: test
+    metrics:
+    - type: f1
+      value: 0.8886810102899907
+      name: F1
+    - type: precision
+      value: 0.8736971183323115
+      name: Precision
+    - type: recall
+      value: 0.9041878172588832
+      name: Recall
+---
+
+# SpanMarker with bert-base-multilingual-cased on TLUnified
+
+This is a [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) model trained on the [TLUnified](https://huggingface.co/datasets/ljvmiranda921/tlunified-ner) dataset that can be used for Named Entity Recognition. This SpanMarker model uses [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) as the underlying encoder.
+
+## Model Details
+
+### Model Description
+- **Model Type:** SpanMarker
+- **Encoder:** [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased)
+- **Maximum Sequence Length:** 256 tokens
+- **Maximum Entity Length:** 8 words
+- **Training Dataset:** [TLUnified](https://huggingface.co/datasets/ljvmiranda921/tlunified-ner)
+- **Language:** tl
+- **License:** gpl-3.0
+
+### 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   | "Israel", "Batasan", "United States"                                                                |
+| ORG   | "MMDA", "International Monitoring Team", "Coordinating Committees for the Cessation of Hostilities" |
+| PER   | "Puno", "Fernando", "Villavicencio"                                                                 |
+
+## Evaluation
+
+### Metrics
+| Label   | Precision | Recall | F1     |
+|:--------|:----------|:-------|:-------|
+| **all** | 0.8737    | 0.9042 | 0.8887 |
+| LOC     | 0.8830    | 0.9084 | 0.8955 |
+| ORG     | 0.7579    | 0.8587 | 0.8052 |
+| PER     | 0.9264    | 0.9220 | 0.9242 |
+
+## Uses
+
+### Direct Use for Inference
+
+```python
+from span_marker import SpanMarkerModel
+
+# Download from the 🤗 Hub
+model = SpanMarkerModel.from_pretrained("tomaarsen/span-marker-mbert-base-tlunified")
+# Run inference
+entities = model.predict("Idinagdag ni South Cotabato Rep Darlene Antonino - Custodio, na illegal na ipagpaliban ang halalan sa ARMM kung ang gagamitin lamang basehan ay ang ipapasang panukala ng Kongreso.")
+```
+
+### 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("tomaarsen/span-marker-mbert-base-tlunified")
+
+# 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("tomaarsen/span-marker-mbert-base-tlunified-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   | 31.7625 | 150 |
+| Entities per sentence | 0   | 2.0661  | 38  |
+
+### Training Hyperparameters
+- learning_rate: 5e-05
+- train_batch_size: 16
+- eval_batch_size: 16
+- seed: 42
+- 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
+
+### Training Results
+| Epoch  | Step | Validation Loss | Validation Precision | Validation Recall | Validation F1 | Validation Accuracy |
+|:------:|:----:|:---------------:|:--------------------:|:-----------------:|:-------------:|:-------------------:|
+| 0.6803 | 400  | 0.0074          | 0.8552               | 0.8835            | 0.8691        | 0.9774              |
+| 1.3605 | 800  | 0.0072          | 0.8709               | 0.9034            | 0.8869        | 0.9798              |
+| 2.0408 | 1200 | 0.0070          | 0.8753               | 0.9053            | 0.8900        | 0.9812              |
+| 2.7211 | 1600 | 0.0065          | 0.8876               | 0.9003            | 0.8939        | 0.9807              |
+
+### Environmental Impact
+Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
+- **Carbon Emitted**: 0.022 kg of CO2
+- **Hours Used**: 0.238 hours
+
+### Training Hardware
+- **On Cloud**: No
+- **GPU Model**: 1 x NVIDIA GeForce RTX 3090
+- **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K
+- **RAM Size**: 31.78 GB
+
+### Framework Versions
+- Python: 3.9.16
+- SpanMarker: 1.5.1.dev
+- Transformers: 4.30.0
+- PyTorch: 2.0.1+cu118
+- Datasets: 2.14.0
+- Tokenizers: 0.13.3
+
+## 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>": 119547
+}

config.json

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+{
+  "architectures": [
+    "SpanMarkerModel"
+  ],
+  "encoder": {
+    "_name_or_path": "bert-base-multilingual-cased",
+    "add_cross_attention": false,
+    "architectures": [
+      "BertForMaskedLM"
+    ],
+    "attention_probs_dropout_prob": 0.1,
+    "bad_words_ids": null,
+    "begin_suppress_tokens": null,
+    "bos_token_id": null,
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+    "classifier_dropout": null,
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+    "directionality": "bidi",
+    "diversity_penalty": 0.0,
+    "do_sample": false,
+    "early_stopping": false,
+    "encoder_no_repeat_ngram_size": 0,
+    "eos_token_id": null,
+    "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-PER",
+      "2": "I-PER",
+      "3": "B-ORG",
+      "4": "I-ORG",
+      "5": "B-LOC",
+      "6": "I-LOC"
+    },
+    "initializer_range": 0.02,
+    "intermediate_size": 3072,
+    "is_decoder": false,
+    "is_encoder_decoder": false,
+    "label2id": {
+      "B-LOC": 5,
+      "B-ORG": 3,
+      "B-PER": 1,
+      "I-LOC": 6,
+      "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,
+    "output_hidden_states": false,
+    "output_scores": false,
+    "pad_token_id": 0,
+    "pooler_fc_size": 768,
+    "pooler_num_attention_heads": 12,
+    "pooler_num_fc_layers": 3,
+    "pooler_size_per_head": 128,
+    "pooler_type": "first_token_transform",
+    "position_embedding_type": "absolute",
+    "prefix": null,
+    "problem_type": null,
+    "pruned_heads": {},
+    "remove_invalid_values": false,
+    "repetition_penalty": 1.0,
+    "return_dict": true,
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+    "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": "4.30.0",
+    "type_vocab_size": 2,
+    "typical_p": 1.0,
+    "use_bfloat16": false,
+    "use_cache": true,
+    "vocab_size": 119549
+  },
+  "entity_max_length": 8,
+  "id2label": {
+    "0": "O",
+    "1": "LOC",
+    "2": "ORG",
+    "3": "PER"
+  },
+  "id2reduced_id": {
+    "0": 0,
+    "1": 3,
+    "2": 3,
+    "3": 2,
+    "4": 2,
+    "5": 1,
+    "6": 1
+  },
+  "label2id": {
+    "LOC": 1,
+    "O": 0,
+    "ORG": 2,
+    "PER": 3
+  },
+  "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": "1.5.1.dev",
+  "torch_dtype": "float32",
+  "trained_with_document_context": false,
+  "transformers_version": "4.30.0",
+  "vocab_size": 119549
+}

pytorch_model.bin

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special_tokens_map.json

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+{
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+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tokenizer_config.json

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+{
+  "add_prefix_space": true,
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_lower_case": false,
+  "entity_max_length": 8,
+  "marker_max_length": 128,
+  "mask_token": "[MASK]",
+  "model_max_length": 256,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}