transformers safetensors bert text-classification generated_from_trainer autotrain_compatible endpoints_compatible region:us
---
tags:
- generated_from_trainer
model-index:
- name: CoM_Small_AIL
  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. -->

# None

This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.0211
- eval_runtime: 11.1994
- eval_samples_per_second: 596.995
- eval_steps_per_second: 9.375
- step: 0

## Model description

The model is a fine-tuned version of BERT for text classification on a specific dataset. It takes a text sequence as input and outputs a probability distribution over the possible classes.

## Intended uses & limitations

The model is intended to be used for text classification tasks similar to the one it was fine-tuned on. It may not perform well on datasets with significantly different characteristics. Additionally, the model may not be suitable for tasks requiring real-time inference due to its relatively large size and computational requirements.

## Training and evaluation data

Data From : Nielzac/CoM_Audio_Image_LLM_Generation

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 34
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1

### Framework versions

- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2


```code python
from transformers import BertForSequenceClassification, TrainingArguments, Trainer, AutoTokenizer, DataCollatorWithPadding
model_id = "bert-base-uncased"
model = BertForSequenceClassification.from_pretrained(model_id, num_labels=3)
tokenizer = AutoTokenizer.from_pretrained(model_id)

def tokenize(batch):
    return tokenizer(batch["text"], truncation=True, padding="max_length", max_length=max_source_length, add_special_tokens=True, return_tensors='pt')
```