End of training

README.md

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+---
+base_model: roberta-base
+datasets:
+- conll2003
+language:
+- en
+library_name: span-marker
+license: apache-2.0
+metrics:
+- precision
+- recall
+- f1
+pipeline_tag: token-classification
+tags:
+- span-marker
+- token-classification
+- ner
+- named-entity-recognition
+- generated_from_span_marker_trainer
+widget:
+- text: '" The worst thing that could happen for financial markets is that if Clinton
+    and Dole start to trade shots in the middle of the ring with one-upmanship, "
+    said Hugh Johnson, chief investment officer at First Albany Corp. " That''s when
+    Wall Street will need to worry . "'
+- text: Poland revived diplomatic ties at ambassadorial level with Yugoslavia in April
+    but economic links are almost moribund, despite the end of a three-year U.N. trade
+    embargo imposed to punish Belgrade for its support of Bosnian Serbs.
+- text: '" We believe that the Israeli settlement policy in the occupied areas is
+    an obstacle to the establishment of peace, " German Foreign Ministry spokesman
+    Martin Erdmann said.'
+- text: U.S. Agriculture Department officials said Friday that Mexican avocados--which
+    are restricted from entering the continental United States--will not likely be
+    entering U.S. markets any time soon, even if the controversial ban were lifted
+    today.
+- text: 3. Tristan Hoffman (Netherlands) TVM same time
+model-index:
+- name: SpanMarker with roberta-base on conll2003
+  results:
+  - task:
+      type: token-classification
+      name: Named Entity Recognition
+    dataset:
+      name: Unknown
+      type: conll2003
+      split: test
+    metrics:
+    - type: f1
+      value: 0.9022464022464022
+      name: F1
+    - type: precision
+      value: 0.8943980514961726
+      name: Precision
+    - type: recall
+      value: 0.9102337110481586
+      name: Recall
+---
+
+# SpanMarker with roberta-base on conll2003
+
+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. This SpanMarker model uses [roberta-base](https://huggingface.co/roberta-base) as the underlying encoder.
+
+## Model Details
+
+### Model Description
+- **Model Type:** SpanMarker
+- **Encoder:** [roberta-base](https://huggingface.co/roberta-base)
+- **Maximum Sequence Length:** 256 tokens
+- **Maximum Entity Length:** 6 words
+- **Training Dataset:** [conll2003](https://huggingface.co/datasets/conll2003)
+- **Language:** en
+- **License:** apache-2.0
+
+### 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   | "BRUSSELS", "Britain", "Germany"                              |
+| MISC  | "British", "EU-wide", "German"                                |
+| ORG   | "EU", "European Commission", "European Union"                 |
+| PER   | "Werner Zwingmann", "Nikolaus van der Pas", "Peter Blackburn" |
+
+## Evaluation
+
+### Metrics
+| Label   | Precision | Recall | F1     |
+|:--------|:----------|:-------|:-------|
+| **all** | 0.8944    | 0.9102 | 0.9022 |
+| LOC     | 0.9220    | 0.9215 | 0.9217 |
+| MISC    | 0.7332    | 0.7949 | 0.7628 |
+| ORG     | 0.8764    | 0.8964 | 0.8863 |
+| PER     | 0.9605    | 0.9629 | 0.9617 |
+
+## 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("3. Tristan Hoffman (Netherlands) TVM same time")
+```
+
+### 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: 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 |
+|:------:|:----:|:---------------:|:--------------------:|:-----------------:|:-------------:|:-------------------:|
+| 0.2775 | 500  | 0.0282          | 0.9105               | 0.8355            | 0.8714        | 0.9670              |
+| 0.5549 | 1000 | 0.0166          | 0.9215               | 0.9205            | 0.9210        | 0.9824              |
+| 0.8324 | 1500 | 0.0151          | 0.9247               | 0.9346            | 0.9296        | 0.9853              |
+
+### Framework Versions
+- Python: 3.10.12
+- SpanMarker: 1.5.0
+- Transformers: 4.41.2
+- PyTorch: 2.3.0+cu121
+- Datasets: 2.20.0
+- Tokenizers: 0.19.1
+
+## 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": [
+      "RobertaForMaskedLM"
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+    "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,
+    "is_encoder_decoder": false,
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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
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+    "min_length": 0,
+    "model_type": "roberta",
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+    "num_beam_groups": 1,
+    "num_beams": 1,
+    "num_hidden_layers": 12,
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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,
+    "torch_dtype": null,
+    "torchscript": false,
+    "transformers_version": "4.41.2",
+    "type_vocab_size": 1,
+    "typical_p": 1.0,
+    "use_bfloat16": false,
+    "use_cache": true,
+    "vocab_size": 50272
+  },
+  "entity_max_length": 6,
+  "id2label": {
+    "0": "O",
+    "1": "LOC",
+    "2": "MISC",
+    "3": "ORG",
+    "4": "PER"
+  },
+  "id2reduced_id": {
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+    "1": 4,
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+    "3": 3,
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+    "5": 1,
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+  "label2id": {
+    "LOC": 1,
+    "MISC": 2,
+    "O": 0,
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+    "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": "4.41.2",
+  "vocab_size": 50272
+}

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special_tokens_map.json

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tokenizer_config.json

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