File size: 6,961 Bytes
f00c051 ee44a17 f00c051 ee44a17 f00c051 ee44a17 f00c051 ee44a17 f00c051 ee44a17 f00c051 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 |
---
language:
- en
library_name: span-marker
tags:
- span-marker
- token-classification
- ner
- named-entity-recognition
- generated_from_span_marker_trainer
datasets:
- tomaarsen/ner-orgs
metrics:
- precision
- recall
- f1
widget:
- text: Hallacas are also commonly consumed in eastern Cuba parts of Colombia, Ecuador,
Aruba, and Curaçao.
- text: The co-production of Yvon Michel's GYM and Jean Bédard's Interbox promotions
and televised via HBO, has trumped a proposed HBO -televised rematch between Jean
Pascal and RING and WBC 175-pound champion Chad Dawson that was slated for the
same date at Bell Centre in Montreal.
- text: The synoptic conditions see a low over southern Norway, bringing warm south
and southwesterly flows of air up from the inner continental areas of Russia and
Belarus.
- text: The RCIS recommended amongst other things that the Australian Security Intelligence
Organisation (ASIO) areas of investigation be widened to include terrorism.
- text: The large network had multiple campuses in Minnesota, Wisconsin, and South
Dakota.
pipeline_tag: token-classification
co2_eq_emissions:
emissions: 532.6472478623315
source: codecarbon
training_type: fine-tuning
on_cloud: false
cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K
ram_total_size: 31.777088165283203
hours_used: 3.696
hardware_used: 1 x NVIDIA GeForce RTX 3090
base_model: bert-base-cased
model-index:
- name: SpanMarker with bert-base-cased on FewNERD, CoNLL2003, OntoNotes v5, and MultiNERD
results:
- task:
type: token-classification
name: Named Entity Recognition
dataset:
name: FewNERD, CoNLL2003, OntoNotes v5, and MultiNERD
type: tomaarsen/ner-orgs
split: test
metrics:
- type: f1
value: 0.8311343653918766
name: F1
- type: precision
value: 0.8334090564894745
name: Precision
- type: recall
value: 0.8288720574945131
name: Recall
---
# SpanMarker with bert-base-cased on FewNERD, CoNLL2003, OntoNotes v5, and MultiNERD
This is a [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) model trained on the [FewNERD, CoNLL2003, OntoNotes v5, and MultiNERD](https://huggingface.co/datasets/tomaarsen/ner-orgs) dataset that can be used for Named Entity Recognition. This SpanMarker model uses [bert-base-cased](https://huggingface.co/bert-base-cased) as the underlying encoder.
## Model Details
### Model Description
- **Model Type:** SpanMarker
- **Encoder:** [bert-base-cased](https://huggingface.co/bert-base-cased)
- **Maximum Sequence Length:** 256 tokens
- **Maximum Entity Length:** 8 words
- **Training Dataset:** [FewNERD, CoNLL2003, OntoNotes v5, and MultiNERD](https://huggingface.co/datasets/tomaarsen/ner-orgs)
- **Language:** en
<!-- - **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 |
|:------|:---------------------------------------------|
| ORG | "IAEA", "Church 's Chicken", "Texas Chicken" |
## Evaluation
### Metrics
| Label | Precision | Recall | F1 |
|:--------|:----------|:-------|:-------|
| **all** | 0.8334 | 0.8289 | 0.8311 |
| ORG | 0.8334 | 0.8289 | 0.8311 |
## Uses
### Direct Use for Inference
```python
from span_marker import SpanMarkerModel
# Download from the 🤗 Hub
model = SpanMarkerModel.from_pretrained("tomaarsen/span-marker-bert-base-orgs")
# Run inference
entities = model.predict("The large network had multiple campuses in Minnesota, Wisconsin, and South Dakota.")
```
### 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("tomaarsen/span-marker-bert-base-orgs")
# 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("tomaarsen/span-marker-bert-base-orgs-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 | 22.1911 | 267 |
| Entities per sentence | 0 | 0.8144 | 39 |
### Training Hyperparameters
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- 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: 3
### Training Results
| Epoch | Step | Validation Loss |
|:------:|:-----:|:---------------:|
| 0.3273 | 3000 | 0.0052 |
| 0.6546 | 6000 | 0.0047 |
| 0.9819 | 9000 | 0.0045 |
| 1.3092 | 12000 | 0.0047 |
| 1.6365 | 15000 | 0.0045 |
| 1.9638 | 18000 | 0.0046 |
| 2.2911 | 21000 | 0.0054 |
| 2.6184 | 24000 | 0.0053 |
| 2.9457 | 27000 | 0.0052 |
### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Carbon Emitted**: 0.533 kg of CO2
- **Hours Used**: 3.696 hours
### Training Hardware
- **On Cloud**: No
- **GPU Model**: 1 x NVIDIA GeForce RTX 3090
- **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K
- **RAM Size**: 31.78 GB
### Framework Versions
- Python: 3.9.16
- SpanMarker: 1.5.1.dev
- Transformers: 4.30.0
- PyTorch: 2.0.1+cu118
- Datasets: 2.14.0
- Tokenizers: 0.13.3
## 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.*
--> |