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README.md

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+---
+language: en
+license: cc-by-sa-4.0
+library_name: span-marker
+tags:
+- span-marker
+- token-classification
+- ner
+- named-entity-recognition
+- generated_from_span_marker_trainer
+metrics:
+- precision
+- recall
+- f1
+widget:
+- text: Inductively Coupled Plasma - Mass Spectrometry ( ICP - MS ) analysis of Longcliffe
+    SP52 limestone was undertaken to identify other impurities present , and the effect
+    of sorbent mass and SO2 concentration on elemental partitioning in the carbonator
+    between solid sorbent and gaseous phase was investigated , using a bubbler sampling
+    system .
+- text: We extensively evaluate our work against benchmark and competitive protocols
+    across a range of metrics over three real connectivity and GPS traces such as
+    Sassy [ 44 ] , San Francisco Cabs [ 45 ] and Infocom 2006 [ 33 ] .
+- text: In this research , we developed a robust two - layer classifier that can accurately
+    classify normal hearing ( NH ) from hearing impaired ( HI ) infants with congenital
+    sensori - neural hearing loss ( SNHL ) based on their Magnetic Resonance ( MR
+    ) images .
+- text: In situ Peak Force Tapping AFM was employed for determining morphology and
+    nano - mechanical properties of the surface layer .
+- text: By means of a criterion of Gilmer for polynomially dense subsets of the ring
+    of integers of a number field , we show that , if h∈K[X ] maps every element of
+    OK of degree n to an algebraic integer , then h(X ) is integral - valued over
+    OK , that is , h(OK)⊂OK .
+pipeline_tag: token-classification
+base_model: allenai/scibert_scivocab_uncased
+model-index:
+- name: SpanMarker with allenai/scibert_scivocab_uncased on my-data
+  results:
+  - task:
+      type: token-classification
+      name: Named Entity Recognition
+    dataset:
+      name: my-data
+      type: unknown
+      split: test
+    metrics:
+    - type: f1
+      value: 0.685430463576159
+      name: F1
+    - type: precision
+      value: 0.6981450252951096
+      name: Precision
+    - type: recall
+      value: 0.6731707317073171
+      name: Recall
+---
+
+# SpanMarker with allenai/scibert_scivocab_uncased on my-data
+
+This is a [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) model that can be used for Named Entity Recognition. This SpanMarker model uses [allenai/scibert_scivocab_uncased](https://huggingface.co/allenai/scibert_scivocab_uncased) as the underlying encoder.
+
+## Model Details
+
+### Model Description
+- **Model Type:** SpanMarker
+- **Encoder:** [allenai/scibert_scivocab_uncased](https://huggingface.co/allenai/scibert_scivocab_uncased)
+- **Maximum Sequence Length:** 256 tokens
+- **Maximum Entity Length:** 8 words
+<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
+- **Language:** en
+- **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                                                                                                |
+|:---------|:--------------------------------------------------------------------------------------------------------|
+| Data     | "an overall mitochondrial", "defect", "Depth time - series"                                             |
+| Material | "cross - shore measurement locations", "the subject 's fibroblasts", "COXI , COXII and COXIII subunits" |
+| Method   | "EFSA", "an approximation", "in vitro"                                                                  |
+| Process  | "translation", "intake", "a significant reduction of synthesis"                                         |
+
+## Evaluation
+
+### Metrics
+| Label    | Precision | Recall | F1     |
+|:---------|:----------|:-------|:-------|
+| **all**  | 0.6981    | 0.6732 | 0.6854 |
+| Data     | 0.6269    | 0.6402 | 0.6335 |
+| Material | 0.8085    | 0.7562 | 0.7815 |
+| Method   | 0.4211    | 0.4    | 0.4103 |
+| Process  | 0.6891    | 0.6488 | 0.6683 |
+
+## Uses
+
+### Direct Use for Inference
+
+```python
+from span_marker import SpanMarkerModel
+
+# Download from the 🤗 Hub
+model = SpanMarkerModel.from_pretrained("span-marker-allenai/scibert_scivocab_uncased-me")
+# Run inference
+entities = model.predict("In situ Peak Force Tapping AFM was employed for determining morphology and nano - mechanical properties of the surface layer .")
+```
+
+### 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-allenai/scibert_scivocab_uncased-me")
+
+# 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-allenai/scibert_scivocab_uncased-me-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       | 3   | 25.6049 | 106 |
+| Entities per sentence | 0   | 5.2439  | 22  |
+
+### Training Hyperparameters
+- learning_rate: 5e-05
+- train_batch_size: 8
+- eval_batch_size: 8
+- 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: 10
+
+### Training Results
+| Epoch  | Step | Validation Loss | Validation Precision | Validation Recall | Validation F1 | Validation Accuracy |
+|:------:|:----:|:---------------:|:--------------------:|:-----------------:|:-------------:|:-------------------:|
+| 2.0134 | 300  | 0.0476          | 0.7297               | 0.5821            | 0.6476        | 0.7880              |
+| 4.0268 | 600  | 0.0532          | 0.7537               | 0.6775            | 0.7136        | 0.8281              |
+| 6.0403 | 900  | 0.0655          | 0.7162               | 0.7080            | 0.7121        | 0.8357              |
+| 8.0537 | 1200 | 0.0761          | 0.7143               | 0.7061            | 0.7102        | 0.8251              |
+
+### Framework Versions
+- Python: 3.10.12
+- SpanMarker: 1.5.0
+- Transformers: 4.36.2
+- PyTorch: 2.0.1+cu118
+- 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>": 31090
+}

config.json

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+{
+  "architectures": [
+    "SpanMarkerModel"
+  ],
+  "encoder": {
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+    "hidden_act": "gelu",
+    "hidden_dropout_prob": 0.1,
+    "hidden_size": 768,
+    "id2label": {
+      "0": "O",
+      "1": "Data",
+      "2": "Material",
+      "3": "Method",
+      "4": "Process"
+    },
+    "initializer_range": 0.02,
+    "intermediate_size": 3072,
+    "is_decoder": false,
+    "is_encoder_decoder": false,
+    "label2id": {
+      "Data": 1,
+      "Material": 2,
+      "Method": 3,
+      "O": 0,
+      "Process": 4
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+    "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,
+    "position_embedding_type": "absolute",
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+    "pruned_heads": {},
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+    "return_dict": true,
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+    "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": "4.36.2",
+    "type_vocab_size": 2,
+    "typical_p": 1.0,
+    "use_bfloat16": false,
+    "use_cache": true,
+    "vocab_size": 31096
+  },
+  "entity_max_length": 8,
+  "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": "4.36.2",
+  "vocab_size": 31096
+}

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special_tokens_map.json

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

tokenizer_config.json

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+  "added_tokens_decoder": {
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+      "lstrip": false,
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+  "clean_up_tokenization_spaces": true,
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+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "entity_max_length": 8,
+  "marker_max_length": 128,
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+  "tokenizer_class": "BertTokenizer",
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+}

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