File size: 2,269 Bytes
bd0a418 b12ea37 bd0a418 b12ea37 bd0a418 b12ea37 |
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 |
---
license: apache-2.0
base_model: distilbert-base-cased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert_clf_results
results: []
datasets:
- app_reviews
language:
- en
library_name: transformers
pipeline_tag: text-classification
---
<!-- 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. -->
# bert_clf_results
This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9611
- Accuracy: 0.7011
## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.0767 | 1.0 | 5401 | 0.8447 | 0.7087 |
| 0.6523 | 2.0 | 10803 | 0.8287 | 0.7156 |
| 0.7209 | 3.0 | 16204 | 0.8852 | 0.7121 |
| 0.4274 | 4.0 | 21604 | 0.9611 | 0.7011 |
### Code Implementation
```
from transformers import AutoTokenizer
from transformers import AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Andyrasika/bert_clf_results")
inputs = tokenizer(prompt, return_tensors="pt")
model = AutoModelForSequenceClassification.from_pretrained("Andyrasika/bert_clf_results")
with torch.no_grad():
logits = model(**inputs).logits
predicted_class_id = logits.argmax().item()
model.config.id2label[predicted_class_id]
```
Output
```
'LABEL_4'
```
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.0+cu118
- Datasets 2.16.0
- Tokenizers 0.15.0 |