---
library_name: peft
tags:
- generated_from_trainer
base_model: cardiffnlp/twitter-roberta-base-sentiment-latest
metrics:
- accuracy
- precision
- recall
model-index:
- name: twitter-roberta-base-sentiment-latest-trump-stance-1
  results: []
---

<!-- 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. -->

# twitter-roberta-base-sentiment-latest-trump-stance-1

This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-sentiment-latest](https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment-latest) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1168
- Accuracy: {'accuracy': 0.6666666666666666}
- Precision: {'precision': 0.5697940503432495}
- Recall: {'recall': 0.7302052785923754}
- F1 Score: {'f1': 0.6401028277634961}

## 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: 0.001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50

### Training results

| Training Loss | Epoch | Step   | Validation Loss | Accuracy               | Precision                         | Recall              | F1 Score                   |
|:-------------:|:-----:|:------:|:---------------:|:----------------------:|:---------------------------------:|:-------------------:|:--------------------------:|
| 0.583         | 1.0   | 3600   | 0.3772          | {'accuracy': 0.83875}  | {'precision': 0.812933025404157}  | {'recall': 0.88}    | {'f1': 0.8451380552220888} |
| 0.5621        | 2.0   | 7200   | 0.3725          | {'accuracy': 0.853125} | {'precision': 0.9407176287051482} | {'recall': 0.75375} | {'f1': 0.8369188063844553} |
| 0.5813        | 3.0   | 10800  | 1.0373          | {'accuracy': 0.625625} | {'precision': 0.5719398711524696} | {'recall': 0.99875} | {'f1': 0.7273554847519345} |
| 0.5317        | 4.0   | 14400  | 0.3697          | {'accuracy': 0.875625} | {'precision': 0.8917861799217731} | {'recall': 0.855}   | {'f1': 0.8730057434588385} |
| 0.5498        | 5.0   | 18000  | 0.4457          | {'accuracy': 0.8525}   | {'precision': 0.8551637279596978} | {'recall': 0.84875} | {'f1': 0.8519447929736512} |
| 0.5388        | 6.0   | 21600  | 0.4715          | {'accuracy': 0.829375} | {'precision': 0.9136577708006279} | {'recall': 0.7275}  | {'f1': 0.8100208768267223} |
| 0.5885        | 7.0   | 25200  | 0.3773          | {'accuracy': 0.85875}  | {'precision': 0.8836898395721925} | {'recall': 0.82625} | {'f1': 0.8540051679586563} |
| 0.4961        | 8.0   | 28800  | 0.3819          | {'accuracy': 0.869375} | {'precision': 0.9053497942386831} | {'recall': 0.825}   | {'f1': 0.8633093525179856} |
| 0.5421        | 9.0   | 32400  | 0.4011          | {'accuracy': 0.85875}  | {'precision': 0.8239277652370203} | {'recall': 0.9125}  | {'f1': 0.8659549228944247} |
| 0.5123        | 10.0  | 36000  | 0.3404          | {'accuracy': 0.88125}  | {'precision': 0.9034391534391535} | {'recall': 0.85375} | {'f1': 0.877892030848329}  |
| 0.5996        | 11.0  | 39600  | 0.3435          | {'accuracy': 0.880625} | {'precision': 0.8801498127340824} | {'recall': 0.88125} | {'f1': 0.8806995627732667} |
| 0.4871        | 12.0  | 43200  | 0.2972          | {'accuracy': 0.890625} | {'precision': 0.9021879021879022} | {'recall': 0.87625} | {'f1': 0.8890298034242232} |
| 0.5272        | 13.0  | 46800  | 0.3629          | {'accuracy': 0.874375} | {'precision': 0.9423929098966026} | {'recall': 0.7975}  | {'f1': 0.8639133378469871} |
| 0.5897        | 14.0  | 50400  | 0.3164          | {'accuracy': 0.88}     | {'precision': 0.9075067024128687} | {'recall': 0.84625} | {'f1': 0.8758085381630013} |
| 0.4963        | 15.0  | 54000  | 0.3343          | {'accuracy': 0.87625}  | {'precision': 0.922752808988764}  | {'recall': 0.82125} | {'f1': 0.8690476190476191} |
| 0.5132        | 16.0  | 57600  | 0.5593          | {'accuracy': 0.855625} | {'precision': 0.9330289193302892} | {'recall': 0.76625} | {'f1': 0.8414550446122169} |
| 0.447         | 17.0  | 61200  | 0.3651          | {'accuracy': 0.874375} | {'precision': 0.8544378698224852} | {'recall': 0.9025}  | {'f1': 0.8778115501519757} |
| 0.5189        | 18.0  | 64800  | 0.3919          | {'accuracy': 0.878125} | {'precision': 0.9315263908701854} | {'recall': 0.81625} | {'f1': 0.8700866089273818} |
| 0.4835        | 19.0  | 68400  | 0.5706          | {'accuracy': 0.846875} | {'precision': 0.9541734860883797} | {'recall': 0.72875} | {'f1': 0.8263642806520198} |
| 0.455         | 20.0  | 72000  | 0.3523          | {'accuracy': 0.881875} | {'precision': 0.8813982521847691} | {'recall': 0.8825}  | {'f1': 0.8819487820112429} |
| 0.4791        | 21.0  | 75600  | 0.3292          | {'accuracy': 0.884375} | {'precision': 0.8546712802768166} | {'recall': 0.92625} | {'f1': 0.8890221955608878} |
| 0.512         | 22.0  | 79200  | 0.4456          | {'accuracy': 0.87}     | {'precision': 0.9391691394658753} | {'recall': 0.79125} | {'f1': 0.858887381275441}  |
| 0.4783        | 23.0  | 82800  | 0.3283          | {'accuracy': 0.880625} | {'precision': 0.9188445667125172} | {'recall': 0.835}   | {'f1': 0.8749181401440733} |
| 0.4699        | 24.0  | 86400  | 0.3399          | {'accuracy': 0.885}    | {'precision': 0.9074074074074074} | {'recall': 0.8575}  | {'f1': 0.8817480719794345} |
| 0.4485        | 25.0  | 90000  | 0.3156          | {'accuracy': 0.89}     | {'precision': 0.8949367088607595} | {'recall': 0.88375} | {'f1': 0.889308176100629}  |
| 0.4305        | 26.0  | 93600  | 0.3105          | {'accuracy': 0.894375} | {'precision': 0.9092088197146563} | {'recall': 0.87625} | {'f1': 0.8924252068746021} |
| 0.4704        | 27.0  | 97200  | 0.3528          | {'accuracy': 0.879375} | {'precision': 0.8634730538922155} | {'recall': 0.90125} | {'f1': 0.8819571865443425} |
| 0.4589        | 28.0  | 100800 | 0.3534          | {'accuracy': 0.879375} | {'precision': 0.8696711327649208} | {'recall': 0.8925}  | {'f1': 0.8809376927822332} |
| 0.4831        | 29.0  | 104400 | 0.3315          | {'accuracy': 0.891875} | {'precision': 0.9108781127129751} | {'recall': 0.86875} | {'f1': 0.889315419065899}  |
| 0.4931        | 30.0  | 108000 | 0.3200          | {'accuracy': 0.891875} | {'precision': 0.9185580774365821} | {'recall': 0.86}    | {'f1': 0.8883150419625565} |
| 0.4286        | 31.0  | 111600 | 0.3488          | {'accuracy': 0.8825}   | {'precision': 0.9180327868852459} | {'recall': 0.84}    | {'f1': 0.8772845953002611} |
| 0.4309        | 32.0  | 115200 | 0.3192          | {'accuracy': 0.891875} | {'precision': 0.8875154511742892} | {'recall': 0.8975}  | {'f1': 0.8924798011187073} |
| 0.3896        | 33.0  | 118800 | 0.3294          | {'accuracy': 0.881875} | {'precision': 0.8632580261593341} | {'recall': 0.9075}  | {'f1': 0.8848263254113345} |
| 0.4327        | 34.0  | 122400 | 0.3003          | {'accuracy': 0.899375} | {'precision': 0.9346938775510204} | {'recall': 0.85875} | {'f1': 0.895114006514658}  |
| 0.4179        | 35.0  | 126000 | 0.3189          | {'accuracy': 0.898125} | {'precision': 0.9368998628257887} | {'recall': 0.85375} | {'f1': 0.8933943754087639} |
| 0.4023        | 36.0  | 129600 | 0.3284          | {'accuracy': 0.8775}   | {'precision': 0.8408577878103838} | {'recall': 0.93125} | {'f1': 0.8837485172004745} |
| 0.4285        | 37.0  | 133200 | 0.3221          | {'accuracy': 0.894375} | {'precision': 0.9280868385345997} | {'recall': 0.855}   | {'f1': 0.8900455432661027} |
| 0.3988        | 38.0  | 136800 | 0.2861          | {'accuracy': 0.896875} | {'precision': 0.8905289052890529} | {'recall': 0.905}   | {'f1': 0.8977061376317421} |
| 0.4034        | 39.0  | 140400 | 0.3501          | {'accuracy': 0.895625} | {'precision': 0.9438990182328191} | {'recall': 0.84125} | {'f1': 0.8896232650363516} |
| 0.3743        | 40.0  | 144000 | 0.3654          | {'accuracy': 0.886875} | {'precision': 0.9176788124156545} | {'recall': 0.85}    | {'f1': 0.8825438027255029} |
| 0.3979        | 41.0  | 147600 | 0.3230          | {'accuracy': 0.899375} | {'precision': 0.9311740890688259} | {'recall': 0.8625}  | {'f1': 0.8955223880597015} |
| 0.3808        | 42.0  | 151200 | 0.2978          | {'accuracy': 0.90375}  | {'precision': 0.9205729166666666} | {'recall': 0.88375} | {'f1': 0.9017857142857143} |
| 0.3777        | 43.0  | 154800 | 0.2805          | {'accuracy': 0.899375} | {'precision': 0.9220607661822986} | {'recall': 0.8725}  | {'f1': 0.8965960179833012} |
| 0.3631        | 44.0  | 158400 | 0.2984          | {'accuracy': 0.898125} | {'precision': 0.9163398692810457} | {'recall': 0.87625} | {'f1': 0.8958466453674121} |
| 0.3674        | 45.0  | 162000 | 0.2924          | {'accuracy': 0.90375}  | {'precision': 0.9376693766937669} | {'recall': 0.865}   | {'f1': 0.8998699609882965} |
| 0.3539        | 46.0  | 165600 | 0.3158          | {'accuracy': 0.89375}  | {'precision': 0.899746192893401}  | {'recall': 0.88625} | {'f1': 0.8929471032745592} |
| 0.3557        | 47.0  | 169200 | 0.2861          | {'accuracy': 0.9}      | {'precision': 0.9145077720207254} | {'recall': 0.8825}  | {'f1': 0.8982188295165394} |
| 0.38          | 48.0  | 172800 | 0.2962          | {'accuracy': 0.894375} | {'precision': 0.9029374201787995} | {'recall': 0.88375} | {'f1': 0.8932406822488945} |
| 0.3754        | 49.0  | 176400 | 0.2905          | {'accuracy': 0.9}      | {'precision': 0.9166666666666666} | {'recall': 0.88}    | {'f1': 0.8979591836734694} |
| 0.3717        | 50.0  | 180000 | 0.2880          | {'accuracy': 0.89875}  | {'precision': 0.9153645833333334} | {'recall': 0.87875} | {'f1': 0.8966836734693877} |


### Framework versions

- PEFT 0.10.0
- Transformers 4.38.2
- Pytorch 2.2.1
- Datasets 2.18.0
- Tokenizers 0.15.2