File size: 6,135 Bytes
9f0771c 5488480 9f0771c 8171496 9f0771c 8171496 9f0771c 8171496 9f0771c 8171496 7633c29 8171496 0f8a1bf 8171496 0f8a1bf 8171496 0f8a1bf 8171496 9f0771c 8171496 9f0771c 61ba558 9f0771c 8171496 7633c29 8171496 7633c29 8171496 7633c29 8171496 9f0771c 5488480 |
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 |
---
library_name: transformers
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_keras_callback
model-index:
- name: huseyincenik/conll_ner_with_bert
results: []
datasets:
- tner/conll2003
language:
- en
metrics:
- accuracy
pipeline_tag: token-classification
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# huseyincenik/conll_ner_with_bert
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the CoNLL-2003 dataset for Named Entity Recognition (NER).
## Model description
This model has been trained to perform Named Entity Recognition (NER) and is based on the BERT architecture. It was fine-tuned on the CoNLL-2003 dataset, a standard dataset for NER tasks.
## Intended uses & limitations
### Intended Uses
- **Named Entity Recognition**: This model is designed to identify and classify named entities in text into categories such as location (LOC), organization (ORG), person (PER), and miscellaneous (MISC).
### Limitations
- **Domain Specificity**: The model was fine-tuned on the CoNLL-2003 dataset, which consists of news articles. It may not generalize well to other domains or types of text not represented in the training data.
- **Subword Tokens**: The model may occasionally tag subword tokens as entities, requiring post-processing to handle these cases.
## Training and evaluation data
- **Training Dataset**: CoNLL-2003
- **Training Evaluation Metrics**:
| Label | Precision | Recall | F1-Score | Support |
|---------|-----------|--------|----------|---------|
| B-PER | 0.98 | 0.98 | 0.98 | 11273 |
| I-PER | 0.98 | 0.99 | 0.99 | 9323 |
| B-ORG | 0.88 | 0.92 | 0.90 | 10447 |
| I-ORG | 0.81 | 0.92 | 0.86 | 5137 |
| B-LOC | 0.86 | 0.94 | 0.90 | 9621 |
| I-LOC | 1.00 | 0.08 | 0.14 | 1267 |
| B-MISC | 0.81 | 0.73 | 0.77 | 4793 |
| I-MISC | 0.83 | 0.36 | 0.50 | 1329 |
| **Micro Avg** | **0.90** | **0.90** | **0.90** | **53190** |
| **Macro Avg** | **0.89** | **0.74** | **0.75** | **53190** |
| **Weighted Avg** | **0.90** | **0.90** | **0.89** | **53190** |
- **Validation Evaluation Metrics**:
| Label | Precision | Recall | F1-Score | Support |
|---------|-----------|--------|----------|---------|
| B-PER | 0.97 | 0.98 | 0.97 | 3018 |
| I-PER | 0.98 | 0.98 | 0.98 | 2741 |
| B-ORG | 0.86 | 0.91 | 0.88 | 2056 |
| I-ORG | 0.77 | 0.81 | 0.79 | 900 |
| B-LOC | 0.86 | 0.94 | 0.90 | 2618 |
| I-LOC | 1.00 | 0.10 | 0.18 | 281 |
| B-MISC | 0.77 | 0.74 | 0.76 | 1231 |
| I-MISC | 0.77 | 0.34 | 0.48 | 390 |
| **Micro Avg** | **0.90** | **0.89** | **0.89** | **13235** |
| **Macro Avg** | **0.87** | **0.73** | **0.74** | **13235** |
| **Weighted Avg** | **0.90** | **0.89** | **0.88** | **13235** |
- **Test Evaluation Metrics**:
| Label | Precision | Recall | F1-Score | Support |
|---------|-----------|--------|----------|---------|
| B-PER | 0.96 | 0.95 | 0.96 | 2714 |
| I-PER | 0.98 | 0.99 | 0.98 | 2487 |
| B-ORG | 0.81 | 0.87 | 0.84 | 2588 |
| I-ORG | 0.74 | 0.87 | 0.80 | 1050 |
| B-LOC | 0.81 | 0.90 | 0.85 | 2121 |
| I-LOC | 0.89 | 0.12 | 0.22 | 276 |
| B-MISC | 0.75 | 0.67 | 0.71 | 996 |
| I-MISC | 0.85 | 0.49 | 0.62 | 241 |
| **Micro Avg** | **0.87** | **0.88** | **0.87** | **12473** |
| **Macro Avg** | **0.85** | **0.73** | **0.75** | **12473** |
| **Weighted Avg** | **0.87** | **0.88** | **0.86** | **12473** |
## Training procedure
### Training Hyperparameters
- **Optimizer**: AdamWeightDecay
- Learning Rate: 2e-05
- Decay Schedule: PolynomialDecay
- Warmup Steps: 0.1
- Weight Decay Rate: 0.01
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 0.1016 | 0.0254 | 0 |
| 0.0228 | 0.0180 | 1 |
### Optimizer Details
```python
from transformers import create_optimizer
batch_size = 32
num_train_epochs = 2
num_train_steps = (len(tokenized_conll["train"]) // batch_size) * num_train_epochs
optimizer, lr_schedule = create_optimizer(
init_lr=2e-5,
num_train_steps=num_train_steps,
weight_decay_rate=0.01,
num_warmup_steps=0.1
)
```
## How to Use
### Using a Pipeline
```python
from transformers import pipeline
pipe = pipeline("token-classification", model="huseyincenik/conll_ner_with_bert")
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("huseyincenik/conll_ner_with_bert")
model = AutoModelForTokenClassification.from_pretrained("huseyincenik/conll_ner_with_bert")
```
Abbreviation|Description
-|-
O|Outside of a named entity
B-MISC |Beginning of a miscellaneous entity right after another miscellaneous entity
I-MISC | Miscellaneous entity
B-PER |Beginning of a person’s name right after another person’s name
I-PER |Person’s name
B-ORG |Beginning of an organization right after another organization
I-ORG |organization
B-LOC |Beginning of a location right after another location
I-LOC |Location
### CoNLL-2003 English Dataset Statistics
This dataset was derived from the Reuters corpus which consists of Reuters news stories. You can read more about how this dataset was created in the CoNLL-2003 paper.
#### # of training examples per entity type
Dataset|LOC|MISC|ORG|PER
-|-|-|-|-
Train|7140|3438|6321|6600
Dev|1837|922|1341|1842
Test|1668|702|1661|1617
#### # of articles/sentences/tokens per dataset
Dataset |Articles |Sentences |Tokens
-|-|-|-
Train |946 |14,987 |203,621
Dev |216 |3,466 |51,362
Test |231 |3,684 |46,435
### Framework versions
- Transformers 4.45.0.dev0
- TensorFlow 2.17.0
- Datasets 2.21.0
- Tokenizers 0.19.1 |