CoM_Small_AIL

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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

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')