File size: 4,770 Bytes
3da5e9f a652903 3da5e9f a652903 3da5e9f a652903 c9b064c a652903 c9b064c a652903 c9b064c a652903 c9b064c 3da5e9f a652903 3da5e9f c9b064c 3da5e9f 84c248f 3da5e9f c9b064c a652903 c9b064c a652903 3da5e9f |
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 |
---
license: apache-2.0
base_model: distilbert-base-cased
tags:
- generated_from_trainer
datasets:
- shipping_label_ner
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: ner_bert_model
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: shipping_label_ner
type: shipping_label_ner
config: shipping_label_ner
split: validation
args: shipping_label_ner
metrics:
- name: Precision
type: precision
value: 0.8192771084337349
- name: Recall
type: recall
value: 0.9066666666666666
- name: F1
type: f1
value: 0.8607594936708859
- name: Accuracy
type: accuracy
value: 0.903954802259887
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ner_bert_model
This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the shipping_label_ner dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4675
- Precision: 0.8193
- Recall: 0.9067
- F1: 0.8608
- Accuracy: 0.9040
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 7 | 1.9567 | 0.0 | 0.0 | 0.0 | 0.4294 |
| No log | 2.0 | 14 | 1.7382 | 1.0 | 0.0133 | 0.0263 | 0.4350 |
| No log | 3.0 | 21 | 1.5156 | 0.56 | 0.1867 | 0.28 | 0.5424 |
| No log | 4.0 | 28 | 1.3070 | 0.5185 | 0.3733 | 0.4341 | 0.6215 |
| No log | 5.0 | 35 | 1.1073 | 0.6792 | 0.48 | 0.5625 | 0.6667 |
| No log | 6.0 | 42 | 0.9590 | 0.6970 | 0.6133 | 0.6525 | 0.7288 |
| No log | 7.0 | 49 | 0.8036 | 0.7324 | 0.6933 | 0.7123 | 0.7853 |
| No log | 8.0 | 56 | 0.7173 | 0.6860 | 0.7867 | 0.7329 | 0.8305 |
| No log | 9.0 | 63 | 0.5963 | 0.7778 | 0.84 | 0.8077 | 0.8814 |
| No log | 10.0 | 70 | 0.5354 | 0.7901 | 0.8533 | 0.8205 | 0.8870 |
| No log | 11.0 | 77 | 0.5048 | 0.8 | 0.8533 | 0.8258 | 0.8814 |
| No log | 12.0 | 84 | 0.4992 | 0.8293 | 0.9067 | 0.8662 | 0.9096 |
| No log | 13.0 | 91 | 0.4745 | 0.8205 | 0.8533 | 0.8366 | 0.8927 |
| No log | 14.0 | 98 | 0.4489 | 0.8608 | 0.9067 | 0.8831 | 0.9153 |
| No log | 15.0 | 105 | 0.4236 | 0.8608 | 0.9067 | 0.8831 | 0.9153 |
| No log | 16.0 | 112 | 0.4621 | 0.8193 | 0.9067 | 0.8608 | 0.9096 |
| No log | 17.0 | 119 | 0.4417 | 0.85 | 0.9067 | 0.8774 | 0.9209 |
| No log | 18.0 | 126 | 0.4642 | 0.8095 | 0.9067 | 0.8553 | 0.9040 |
| No log | 19.0 | 133 | 0.4244 | 0.85 | 0.9067 | 0.8774 | 0.9096 |
| No log | 20.0 | 140 | 0.4731 | 0.8193 | 0.9067 | 0.8608 | 0.9096 |
| No log | 21.0 | 147 | 0.4697 | 0.8193 | 0.9067 | 0.8608 | 0.9040 |
| No log | 22.0 | 154 | 0.4330 | 0.8293 | 0.9067 | 0.8662 | 0.9096 |
| No log | 23.0 | 161 | 0.4531 | 0.8193 | 0.9067 | 0.8608 | 0.9040 |
| No log | 24.0 | 168 | 0.4433 | 0.8193 | 0.9067 | 0.8608 | 0.9040 |
| No log | 25.0 | 175 | 0.4477 | 0.8095 | 0.9067 | 0.8553 | 0.9040 |
| No log | 26.0 | 182 | 0.4446 | 0.8293 | 0.9067 | 0.8662 | 0.9096 |
| No log | 27.0 | 189 | 0.4578 | 0.8293 | 0.9067 | 0.8662 | 0.9096 |
| No log | 28.0 | 196 | 0.4640 | 0.8293 | 0.9067 | 0.8662 | 0.9096 |
| No log | 29.0 | 203 | 0.4683 | 0.8193 | 0.9067 | 0.8608 | 0.9040 |
| No log | 30.0 | 210 | 0.4675 | 0.8193 | 0.9067 | 0.8608 | 0.9040 |
### Framework versions
- Transformers 4.39.1
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|