---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- twitter_pos_vcb
model-index:
- name: bert-base-cased-finetuned-Stromberg_NLP_Twitter-PoS
  results: []
language:
- en
metrics:
- seqeval
- accuracy
- f1
- recall
- precision
pipeline_tag: token-classification
---

# bert-base-cased-finetuned-Stromberg_NLP_Twitter-PoS

This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the twitter_pos_vcb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0533
- '''
  - Precision: 0.9580645161290322
  - Recall: 0.9519230769230769
  - F1: 0.954983922829582
  - Number': 312
- B
  - Precision: 0.9658270558694287
  - Recall: 0.9655240037652966
  - F1: 0.9656755060411109
  - Number: 25496
- Bd
  - Precision: 0.9630099728014506
  - Recall: 0.9572819033886085
  - F1: 0.9601373949200036
  - Number: 5548
- Bg
  - Precision: 0.9836065573770492
  - Recall: 0.9853434575313438
  - F1: 0.9844742413549753
  - Number: 5663
- Bn
  - Precision: 0.9182209469153515
  - Recall: 0.9116809116809117
  - F1: 0.9149392423159399
  - Number: 2106
- Bp
  - Precision: 0.9672037914691943
  - Recall: 0.9663488856619736
  - F1: 0.9667761495704902
  - Number': 15839
- Br
  - Precision: 0.94
  - Recall: 0.8785046728971962
  - F1: 0.9082125603864735
  - Number': 107
- Bs
  - Precision: 0.9848484848484849
  - Recall': 0.9701492537313433
  - F1: 0.9774436090225564
  - Number': 67
- Bz
  - Precision: 0.9865819209039548
  - Recall: 0.9850167459897762
  - F1: 0.9857987121813531
  - Number': 5673
- C
  - Precision: 0.9993461203138623,
  - Recall: 0.9993461203138623,
  - F1: 0.9993461203138623,
  - Number: 4588
- D
  - Precision: 0.9876836325864372
  - Recall: 0.9895926256318763
  - F1: 0.988637207575195
  - Number: 6726
- Dt
  - Precision: 1.0
  - Recall: 0.8
  - F1: 0.888888888888889
  - Number: 15
- H
  - Precision: 0.9487382595903587
  - Recall: 0.9305216426193119
  - F1: 0.9395416596626883
  - Number: 9010
- J
  - Precision: 0.9803528468323978
  - Recall: 0.980588754311382
  - F1: 0.9804707863816818
  - Number: 12467
- Jr
  - Precision: 0.9400386847195358
  - Recall: 0.9818181818181818
  - F1': 0.9604743083003953
  - Number': 495
- Js
  - Precision: 0.9612141652613828
  - Recall: 0.991304347826087
  - F1: 0.9760273972602741
  - Number': 575
- N
  - Precision: 0.9795543362923471
  - Recall: 0.9793769083475651
  - F1: 0.9794656142847902
  - Number': 38646
- Np
  - Precision: 0.9330242966751918
  - Recall: 0.9278334128119536
  - F1: 0.9304216147286205
  - Number': 6291
- Nps
  - Precision: 0.75
  - Recall: 0.23076923076923078
  - F1: 0.3529411764705882
  - Number: 26
- Ns
  - Precision: 0.9691858990616282
  - Recall: 0.9773657289002557
  - F1: 0.9732586272762003
  - Number': 7820
- O
  - Precision: 0.9984323288625675
  - Recall: 0.999302649930265
  - F1: 0.9988672998170254
  - Number: 5736
- Os
  - Precision: 1.0
  - Recall: 0.9952267303102625
  - F1: 0.9976076555023923
  - Number: 419
- P
  - Precision: 0.9887869520897044
  - Recall: 0.9918200408997955
  - F1: 0.9903011740684022
  - Number: 2934
- Rb
  - Precision: 0.9971910112359551
  - Recall: 0.9983929288871033
  - F1: 0.9977916081108211
  - Number: 2489
- Rl
  - Precision: 1.0
  - Recall: 0.9997228381374723
  - F1: 0.9998613998613999
  - Number: 3608
- Rp
  - Precision: 0.9979960600502683
  - Recall: 0.9980638586956522
  - F1: 0.9980299582215278
  - Number: 29440
- Rp$
  - Precision: 0.9975770162686051
  - Recall: 0.9972318339100346
  - F1: 0.9974043952240872
  - Number: 5780
- Sr
  - Precision: 0.9998923110058152
  - Recall: 0.9998384752059442
  - F1: 0.9998653923812088
  - Number: 18573
- T
  - Precision: 0.9987569919204475
  - Recall: 0.9984811874352779
  - F1: 0.9986190706345371
  - Number: 28970
- W
  - Precision: 0.0
  - Recall: 0.0
  - F1: 0.0
  - number: 1
- X
  - Precision: 0.9466666666666667,
  - Recall: 0.9594594594594594,
  - F1 0.9530201342281879,
  - Number: 74}
- Ym
  - Precision: 0.0
  - Recall: 0.0
  - F1: 0.0
  - Number: 5
- ' '
  - Precision: 0.9951481772882245
  - Recall: 0.9949524745984923
  - F1: 0.9950503163208444
  - Number: 15255
- '`'
  - Precision: 0.9540229885057471
  - Recall: 0.9595375722543352
  - F1: 0.956772334293948
  - Number: 173

- Overall
  - Precision: 0.9828
  - Recall: 0.9820
  - F1: 0.9824
  - Accuracy: 0.9860

## Model description

For more information on how it was created, check out the following link: https://github.com/DunnBC22/NLP_Projects/blob/main/Token%20Classification/Monolingual/StrombergNLP-Twitter_pos_vcb/NER%20Project%20Using%20StrombergNLP%20Twitter_pos_vcb%20Dataset.ipynb

## Intended uses & limitations

This model is intended to demonstrate my ability to solve a complex problem using technology.

## Training and evaluation data

Dataset Source: https://huggingface.co/datasets/strombergnlp/twitter_pos_vcb

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- 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

| Training Loss | Epoch | Step | Validation Loss | ''' Precision | ''' Recall | ''' F1 | ''' Number | B Precision | B Recall | B F1 | B Number | Bd Precision | Bd Recall | Bd F1 | Bd Number | Bg Precision | Bg Recall | Bg F1 | Bg Number | Bn Precision | Bn Recall | Bn F1 | Bn Number | Bp Precision | Bp Recall | Bp F1 | Bp Number | Br Precision | Br Recall | Br F1 | Br Number | Bs precision | Bs Recall | Bs F1 | Bs Number | Bz Precision | Bz Recall | Bz F1 | Bz Number | C Precision | C Recall | C F1 | C Number | D Precision | D Recall | D F1 | D Number | Dt Precision | Dt Recall | Dt F1 | Dt Number | H Precision | H Recall | H F1 | H Number | J Precision | J Recall | J F1 | J Number | Jr Precision | Jr Recall | Jr F1 | Jr Number | Js Precision | Js Recall | Js F1 | Js Number | N Precision | N Recall | N F1 | N Number | Np Precision | Np Recall | Np F1 | Np Number | Nps Precision | Nps Recall | Nps F1 | Nps Number | Ns Precision | Ns Recall | Ns F1 | Ns Number | O Precision | O Recall | O F1 | O Number | Os Precision | Os Recall | Os F1 | Os Number | P Precision | P Recall | P F1 | P Number | Rb Precision | Rb Recall | Rb f1 | Rb Number | Rl Precision | Rl Recall | Rl F1 | Rl Number | Rp Precision | Rp Recall | Rp F1 | Rp Number | Rp$ Precision | Rp$ Recall | Rp$ F1 | Rp$ Number | Sr Precision | Sr Recall | Sr F1 | Sr Number | T Precision | T recall | T F1 | T Number | W Precision | W Recall | W F1 | W Number | X Precision | X Recall | X F1 | X Number | Ym Precision | Ym Recall | Ym F1 | Ym Number | ' ' Precision | ' ' Recall | ' ' F1 | ' ' Number | '`' Precision | '`' Recall | '`' F1 | '`' Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
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| 0.0617 | 1.0 | 7477 | 0.0595 | 0.9331 | 0.9391 | 0.9361 | 312 | 0.9563 | 0.9536 | 0.9550 | 25496 | 0.9716 | 0.9322 | 0.9515 | 5548 | 0.9811 | 0.9786 | 0.9798 | 5663 | 0.8725 | 0.9231 | 0.8971 | 2106 | 0.9556 | 0.9586 | 0.9571 | 15839 | 0.8879 | 0.8879 | 0.8879 | 107 | 0.8590 | 1.0 | 0.9241 | 67 | 0.9793 | 0.9834 | 0.9814 | 5673 | 0.9985 | 0.9991 | 0.9988 | 4588 | 0.9818 | 0.9886 | 0.9852 | 6726 | 1.0 | 0.8 | 0.8889 | 15 | 0.9391 | 0.9105 | 0.9246 | 9010 | 0.9707 | 0.9766 | 0.9736 | 12467 | 0.9212 | 0.9677 | 0.9438 | 495 | 0.9227 | 0.9757 | 0.9484 | 575 | 0.9754 | 0.9738 | 0.9746 | 38646 | 0.9158 | 0.9200 | 0.9179 | 6291 | 0.0 | 0.0 | 0.0 | 26 | 0.9657 | 0.9688 | 0.9673 | 7820 | 0.9972 | 0.9990 | 0.9981 | 5736 | 1.0 | 0.9928 | 0.9964 | 419 | 0.9771 | 0.9908 | 0.9839 | 2934 | 0.9948 | 0.9968 | 0.9958 | 2489 | 1.0 | 0.9997 | 0.9999 | 3608 | 0.9970 | 0.9976 | 0.9973 | 29440 | 0.9974 | 0.9954 | 0.9964 | 5780 | 0.9998 | 0.9998 | 0.9998 | 18573 | 0.9977 | 0.9982 | 0.9979 | 28970 | 0.0 | 0.0 | 0.0 | 1 | 0.8861 | 0.9459 | 0.9150 | 74 | 0.0 | 0.0 | 0.0 | 5 | 0.9936 | 0.9926 | 0.9931 | 15255 | 0.9540 | 0.9595 | 0.9568 | 173 | 0.9779 | 0.9772 | 0.9775 | 0.9821 |
| 0.0407 | 2.0 | 14954 | 0.0531 | 0.9605 | 0.9359 | 0.9481 | 312 | 0.9599 | 0.9646 | 0.9622 | 25496 | 0.9674 | 0.9459 | 0.9565 | 5548 | 0.9834 | 0.9825 | 0.9830 | 5663 | 0.8920 | 0.9259 | 0.9087 | 2106 | 0.9728 | 0.9569 | 0.9648 | 15839 | 0.9592 | 0.8785 | 0.9171 | 107 | 0.9429 | 0.9851 | 0.9635 | 67 | 0.9890 | 0.9825 | 0.9858 | 5673 | 0.9991 | 0.9993 | 0.9992 | 4588 | 0.9855 | 0.9896 | 0.9875 | 6726 | 1.0 | 0.8 | 0.8889 | 15 | 0.9498 | 0.9303 | 0.9399 | 9010 | 0.9776 | 0.9797 | 0.9786 | 12467 | 0.9125 | 0.9899 | 0.9496 | 495 | 0.9481 | 0.9843 | 0.9659 | 575 | 0.9788 | 0.9771 | 0.9779 | 38646 | 0.9252 | 0.9285 | 0.9268 | 6291 | 0.5 | 0.2308 | 0.3158 | 26  | 0.96534 | 0.9769 | 0.9711 | 7820 | 0.9976 | 0.9993 | 0.9984 | 5736 | 0.9929 | 0.9952 | 0.9940 | 419 | 0.9861 | 0.9928 | 0.9895 | 2934 | 0.9972 | 0.9984 | 0.9978 | 2489 | 1.0 | 0.9997 | 0.9999 | 3608 | 0.9986 | 0.9982 | 0.9984 | 29440 | 0.9964 | 0.9978 | 0.9971 | 5780  | 0.9999 | 0.9999 | 0.9999 | 18573 | 0.9985 | 0.9983 | 0.9984 | 28970 | 0.0 | 0.0 | 0.0 | 1 | 0.9114 | 0.9730 | 0.9412 | 74 | 0.0 | 0.0 | 0.0 | 5 | 0.9949 | 0.9961 | 0.9955 | 15255 | 0.9651 | 0.9595 | 0.9623 | 173 | 0.9817 | 0.9808 | 0.9813 | 0.9850 |
| 0.0246 | 3.0 | 22431 | 0.0533 | 0.9581 | 0.9519 | 0.9550 | 312 | 0.9658 | 0.9655 | 0.9657 | 25496 | 0.9630 | 0.9573 | 0.9601 | 5548 | 0.9836 | 0.9853 | 0.9845 | 5663 | 0.9182 | 0.9117 | 0.9149 | 2106 | 0.9672 | 0.9663 | 0.9668 | 15839 | 0.94 | 0.8785 | 0.9082 | 107 | 0.9848 | 0.9701 | 0.9774 | 67 | 0.9866 | 0.9850 | 0.9858 | 5673 | 0.9993 | 0.9993 | 0.9993 | 4588 | 0.9877 | 0.9896 | 0.9886 | 6726 | 1.0 | 0.8 | 0.8889 | 15 | 0.9487 | 0.9305 | 0.9395 | 9010 | 0.9804 | 0.9806 | 0.9805 | 12467 | 0.9400 | 0.9818 | 0.9605 | 495 | 0.9612 | 0.9913 | 0.9760 | 575 | 0.9796 | 0.9794 | 0.9795 | 38646 | 0.9330 | 0.9278 | 0.9304 | 6291 | 0.75 | 0.2308 | 0.3529 | 26 | 0.9692 | 0.9774 | 0.9733 | 7820 | 0.9984 | 0.9993 | 0.9989 | 5736 | 1.0 | 0.9952 | 0.9976 | 419 | 0.9888 | 0.9918 | 0.9903 | 2934 | 0.9972 | 0.9984 | 0.9978 | 2489 | 1.0 | 0.9997 | 0.9999 | 3608 | 0.9980 | 0.9981 | 0.9981 | 29440 | 0.9976 | 0.9972 | 0.9974 | 5780 | 0.9999 | 0.9998 | 0.9999 | 18573 | 0.9988 | 0.9985 | 0.9986 | 28970 | 0.0 | 0.0 | 0.0 | 1 | 0.9467 | 0.9595 | 0.9530 | 74 | 0.0 | 0.0 | 0.0 | 5 | 0.9951 | 0.9950 | 0.9951 | 15255 | 0.9540 | 0.9595 | 0.9568 | 173 | 0.9828 | 0.9820 | 0.9824 | 0.9860 |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0
- Datasets 2.11.0
- Tokenizers 0.13.3