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---
library_name: span-marker
tags:
- span-marker
- token-classification
- ner
- named-entity-recognition
- generated_from_span_marker_trainer
metrics:
- precision
- recall
- f1
widget:
- text: 'In the 2017 publication "The Routledge Handbook of Collective Intentionality",
edited by Kirk Ludwig and Marija Jankovic, and released by Routledge, leading
scholars explored the complex concept of collective intentionality and its implications
for various disciplines, including philosophy, cognitive science, and social theory
. A thought-provoking 2015 article titled "The Uncivilization Thesis: A Critique"
by Megan Gittins, published in Environmental Ethics, offered a critical examination
of the controversial "uncivilization thesis" and its implications for our understanding
of the relationship between civilization and environmental sustainability.'
- text: In the "The Selfish Gene", renowned biologist Richard Dawkins introduced the
revolutionary concept of the "selfish gene" in 1976, published by Oxford University
Press . This influential work challenged traditional views of evolution and sparked
widespread discussions about the nature of altruism and cooperation . Fans of
science writing might appreciate "A Short History of Nearly Everything" by Bill
Bryson, a captivating exploration of the vast realms of scientific knowledge published
by Broadway Books in 2003.
- text: '"The Pragmatic Turn" (2020, University of Pennsylvania Press) provides key
insights into pragmatist philosophy, edited by John J. Stuhr . For provocative
science, try "Introducing Consciousness", Alex Westrin and Vidyut Lokhande''s
2018 work published via Icon Books, challenging dominant models of self-awareness.'
- text: Have you read "The Selfish Gene" by Richard Dawkins? Published in 1976 by
Oxford University Press, this seminal work introduced the gene-centric view of
evolution and proposed the controversial concept of the "extended phenotype ."
Dawkins' ideas sparked intense debates and influenced diverse fields like evolutionary
biology, psychology, and memetics . Daniel C. Dennett's "Darwin's Dangerous Idea"
(1995, Simon & Schuster) is another must-read that explores the far-reaching implications
of evolutionary theory, from the origins of life to the nature of human consciousness
and free will.
- text: '"The Sociology of Philosophies", an insightful book penned by Randall Collins
and published in 1998 by Harvard University Press, examined the social factors
that influence the development and trajectory of philosophical thought throughout
history . Collins'' analysis shed light on how philosophical ideas are shaped
by the broader cultural, political, and intellectual contexts in which they emerge
. In a 2012 article from Philosophy of the Social Sciences, titled "The Relevance
of the Sociology of Philosophy", Isaac Reed further expounded on the importance
of this interdisciplinary approach, highlighting its potential to deepen our understanding
of the dynamics that shape human knowledge and inquiry.'
pipeline_tag: token-classification
model-index:
- name: SpanMarker
results:
- task:
type: token-classification
name: Named Entity Recognition
dataset:
name: Unknown
type: unknown
split: eval
metrics:
- type: f1
value: 0.0
name: F1
- type: precision
value: 0.0
name: Precision
- type: recall
value: 0.0
name: Recall
---
# SpanMarker
This is a [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) model 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:** 16 words
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **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 |
|:-----------------|:----------------------------------------------------------------------------------------------------------------------------------------------|
| person | "Barney Glaser", "Malcolm Gladwell", "Charles Duhigg" |
| publication_date | "2000", "1967", "2018" |
| publisher | "Little , Brown and Company", "Sociology Press", "Avery" |
| work_of_art | "`` The Tipping Point : How Little Things Can Make a Big Difference ''", "`` The Power of Habit ''", "`` The Discovery of Grounded Theory ''" |
## Evaluation
### Metrics
| Label | Precision | Recall | F1 |
|:-----------------|:----------|:-------|:----|
| **all** | 0.0 | 0.0 | 0.0 |
| person | 0.0 | 0.0 | 0.0 |
| publication_date | 0.0 | 0.0 | 0.0 |
| publisher | 0.0 | 0.0 | 0.0 |
| work_of_art | 0.0 | 0.0 | 0.0 |
## 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("\"The Pragmatic Turn\" (2020, University of Pennsylvania Press) provides key insights into pragmatist philosophy, edited by John J. Stuhr . For provocative science, try \"Introducing Consciousness\", Alex Westrin and Vidyut Lokhande's 2018 work published via Icon Books, challenging dominant models of self-awareness.")
```
### 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 | 47 | 104.6034 | 200 |
| Entities per sentence | 3 | 4.0036 | 5 |
### Training Hyperparameters
- learning_rate: 2e-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
- num_epochs: 3
### Training Results
| Epoch | Step | Validation Loss | Validation Precision | Validation Recall | Validation F1 | Validation Accuracy |
|:-----:|:----:|:---------------:|:--------------------:|:-----------------:|:-------------:|:-------------------:|
| 1.0 | 563 | 0.0206 | 0.0 | 0.0 | 0.0 | 0.8513 |
| 2.0 | 1126 | 0.0173 | 0.0 | 0.0 | 0.0 | 0.8513 |
| 3.0 | 1689 | 0.0162 | 0.0 | 0.0 | 0.0 | 0.8513 |
### Framework Versions
- Python: 3.10.13
- SpanMarker: 1.5.1.dev
- Transformers: 4.39.3
- PyTorch: 2.1.2
- Datasets: 2.16.0
- 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}
}
```
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