README.md

---
language:
- en
license: apache-2.0
library_name: span-marker
tags:
- span-marker
- token-classification
- ner
- named-entity-recognition
- generated_from_span_marker_trainer
base_model: roberta-large
datasets:
- Jerado/enron_intangibles_ner
metrics:
- precision
- recall
- f1
widget:
- text: Negotiated rates in these types of deals (basis for new builds) have been
    allowed to stand for the life of the contracts, in the case of Kern River and
    Mojave.
- text: It seems that there is a single significant policy concern for the ASIC policy
    committee.
- text: 'The appropriate price is in Enpower, but the revenue has never appeared (Deal
    #590753).'
- text: FYI, to me, a prepayment for a service contract would generally be amortized
    over the life of the contract.
- text: 'From: d..steffes @ enron.com To: john.shelk @ enron.com, l..nicolay @ enron.com,
    richard.shapiro @ enron.com, sarah.novosel @ enron.com Subject: Southern Co.''s
    Testimony The first order of business is getting the cost / benefit analysis done.'
pipeline_tag: token-classification
model-index:
- name: SpanMarker with roberta-large on Jerado/enron_intangibles_ner
  results:
  - task:
      type: token-classification
      name: Named Entity Recognition
    dataset:
      name: Unknown
      type: Jerado/enron_intangibles_ner
      split: test
    metrics:
    - type: f1
      value: 0.4390243902439024
      name: F1
    - type: precision
      value: 0.42857142857142855
      name: Precision
    - type: recall
      value: 0.45
      name: Recall
---

# SpanMarker with roberta-large on Jerado/enron_intangibles_ner

This is a [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) model trained on the [Jerado/enron_intangibles_ner](https://huggingface.co/datasets/Jerado/enron_intangibles_ner) dataset that can be used for Named Entity Recognition. This SpanMarker model uses [roberta-large](https://huggingface.co/roberta-large) as the underlying encoder.

## Model Details

### Model Description
- **Model Type:** SpanMarker
- **Encoder:** [roberta-large](https://huggingface.co/roberta-large)
- **Maximum Sequence Length:** 256 tokens
- **Maximum Entity Length:** 6 words
- **Training Dataset:** [Jerado/enron_intangibles_ner](https://huggingface.co/datasets/Jerado/enron_intangibles_ner)
- **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                                    |
|:-----------|:--------------------------------------------|
| Intangible | "deal", "sample EES deal", "Enpower system" |

## Evaluation

### Metrics
| Label      | Precision | Recall | F1     |
|:-----------|:----------|:-------|:-------|
| **all**    | 0.4286    | 0.45   | 0.4390 |
| Intangible | 0.4286    | 0.45   | 0.4390 |

## 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("It seems that there is a single significant policy concern for the ASIC policy committee.")
```

### 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>

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## Training Details

### Training Set Metrics
| Training set          | Min | Median  | Max |
|:----------------------|:----|:--------|:----|
| Sentence length       | 1   | 19.8706 | 216 |
| Entities per sentence | 0   | 0.1865  | 6   |

### 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: 11
- mixed_precision_training: Native AMP

### Training Results
| Epoch   | Step | Validation Loss | Validation Precision | Validation Recall | Validation F1 | Validation Accuracy |
|:-------:|:----:|:---------------:|:--------------------:|:-----------------:|:-------------:|:-------------------:|
| 3.3557  | 500  | 0.0075          | 0.4444               | 0.1667            | 0.2424        | 0.9753              |
| 6.7114  | 1000 | 0.0084          | 0.5714               | 0.3333            | 0.4211        | 0.9793              |
| 10.0671 | 1500 | 0.0098          | 0.6111               | 0.4583            | 0.5238        | 0.9815              |

### Framework Versions
- Python: 3.10.12
- SpanMarker: 1.5.0
- Transformers: 4.40.0
- PyTorch: 2.2.1+cu121
- Datasets: 2.19.0
- 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}
}
```

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