span-marker-luke-base-conll2003/checkpoint-4415

README.md

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+---
+library_name: span-marker
+tags:
+- span-marker
+- token-classification
+- ner
+- named-entity-recognition
+- generated_from_span_marker_trainer
+datasets:
+- conll2003
+metrics:
+- precision
+- recall
+- f1
+widget:
+- text: Atlanta Games silver medal winner Edwards has called on other leading athletes
+    to take part in the Sarajevo meeting--a goodwill gesture towards Bosnia as it
+    recovers from the war in the Balkans--two days after the grand prix final in Milan.
+- text: Portsmouth:Middlesex 199 and 426 (J. Pooley 111,M. Ramprakash 108,M. Gatting
+    83), Hampshire 232 and 109-5.
+- text: Poland's Foreign Minister Dariusz Rosati will visit Yugoslavia on September
+    3 and 4 to revive a dialogue between the two governments which was effectively
+    frozen in 1992,PAP news agency reported on Friday.
+- text: The authorities are apparently extremely afraid of any political and social
+    discontent," said Xiao,in Manila to attend an Amnesty International conference
+    on human rights in China.
+- text: American Nate Miller successfully defended his WBA cruiserweight title when
+    he knocked out compatriot James Heath in the seventh round of their bout on Saturday.
+pipeline_tag: token-classification
+model-index:
+- name: SpanMarker
+  results:
+  - task:
+      type: token-classification
+      name: Named Entity Recognition
+    dataset:
+      name: Unknown
+      type: conll2003
+      split: eval
+    metrics:
+    - type: f1
+      value: 0.9550004205568171
+      name: F1
+    - type: precision
+      value: 0.9542780299209951
+      name: Precision
+    - type: recall
+      value: 0.9557239057239058
+      name: Recall
+---
+
+# SpanMarker
+
+This is a [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) model trained on the [conll2003](https://huggingface.co/datasets/conll2003) 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:** 8 words
+- **Training Dataset:** [conll2003](https://huggingface.co/datasets/conll2003)
+<!-- - **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                                                      |
+|:------|:--------------------------------------------------------------|
+| LOC   | "Germany", "BRUSSELS", "Britain"                              |
+| MISC  | "German", "British", "EU-wide"                                |
+| ORG   | "European Commission", "EU", "European Union"                 |
+| PER   | "Werner Zwingmann", "Nikolaus van der Pas", "Peter Blackburn" |
+
+## 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("Portsmouth:Middlesex 199 and 426 (J. Pooley 111,M. Ramprakash 108,M. Gatting 83), Hampshire 232 and 109-5.")
+```
+
+### 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   | 14.5019 | 113 |
+| Entities per sentence | 0   | 1.6736  | 20  |
+
+### Training Hyperparameters
+- learning_rate: 1e-05
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 16
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 5
+
+### Training Results
+| Epoch | Step | Validation Loss | Validation Precision | Validation Recall | Validation F1 | Validation Accuracy |
+|:-----:|:----:|:---------------:|:--------------------:|:-----------------:|:-------------:|:-------------------:|
+| 1.0   | 883  | 0.0123          | 0.9293               | 0.9274            | 0.9284        | 0.9848              |
+| 2.0   | 1766 | 0.0089          | 0.9412               | 0.9456            | 0.9434        | 0.9882              |
+| 3.0   | 2649 | 0.0077          | 0.9499               | 0.9505            | 0.9502        | 0.9893              |
+| 4.0   | 3532 | 0.0070          | 0.9527               | 0.9537            | 0.9532        | 0.9900              |
+| 5.0   | 4415 | 0.0068          | 0.9543               | 0.9557            | 0.9550        | 0.9902              |
+
+### Framework Versions
+- Python: 3.10.12
+- SpanMarker: 1.5.0
+- Transformers: 4.36.0
+- PyTorch: 2.0.0
+- Datasets: 2.16.1
+- 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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+{
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+  "<start>": 50265
+}

config.json

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+{
+  "architectures": [
+    "SpanMarkerModel"
+  ],
+  "encoder": {
+    "_name_or_path": "studio-ousia/luke-base",
+    "add_cross_attention": false,
+    "architectures": [
+      "LukeForMaskedLM"
+    ],
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+    "do_sample": false,
+    "early_stopping": false,
+    "encoder_no_repeat_ngram_size": 0,
+    "entity_emb_size": 256,
+    "entity_vocab_size": 500000,
+    "eos_token_id": 2,
+    "exponential_decay_length_penalty": null,
+    "finetuning_task": null,
+    "forced_bos_token_id": null,
+    "forced_eos_token_id": null,
+    "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": {
+      "B-LOC": 5,
+      "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-05,
+    "length_penalty": 1.0,
+    "max_length": 20,
+    "max_position_embeddings": 514,
+    "min_length": 0,
+    "model_type": "luke",
+    "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_past": true,
+    "output_scores": false,
+    "pad_token_id": 1,
+    "position_embedding_type": "absolute",
+    "prefix": null,
+    "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": "float32",
+    "torchscript": false,
+    "transformers_version": "4.36.0",
+    "type_vocab_size": 1,
+    "typical_p": 1.0,
+    "use_bfloat16": false,
+    "use_cache": true,
+    "use_entity_aware_attention": true,
+    "vocab_size": 50272
+  },
+  "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": null,
+  "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": "4.36.0",
+  "vocab_size": 50272
+}

model.safetensors

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special_tokens_map.json

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+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "mask_token": {
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+    "single_word": false
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+  "unk_token": "<unk>"
+}

tokenizer_config.json

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+{
+  "add_prefix_space": true,
+  "added_tokens_decoder": {
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+      "lstrip": false,
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+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": true,
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+  "eos_token": "</s>",
+  "errors": "replace",
+  "mask_token": "<mask>",
+  "model_max_length": 512,
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "tokenizer_class": "RobertaTokenizer",
+  "trim_offsets": true,
+  "unk_token": "<unk>"
+}

training_args.bin

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