metadata
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- HuggingFaceFW/fineweb-edu-llama3-annotations
language:
- en
distilbert-base-uncased: edu classifier
This is a (rare) encoder that supports flash attention 2! Use
attn_implementation="flash_attention_2"when loading w/ FA2 installed for faster inference.
This model is a fine-tuned version of distilbert-base-uncased on the HuggingFaceFW/fineweb-edu-llama3-annotations dataset. It achieves the following results on the evaluation set:
- Loss: 0.2324
- Mse: 0.2324
Usage
Note this is for CPU, for GPU you will need to make some (small) changes.
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("pszemraj/mpnet-base-edu-classifier")
model = AutoModelForSequenceClassification.from_pretrained("pszemraj/mpnet-base-edu-classifier")
text = "This is a test sentence."
inputs = tokenizer(text, return_tensors="pt", padding="longest", truncation=True)
outputs = model(**inputs)
logits = outputs.logits.squeeze(-1).float().detach().numpy()
score = logits.item()
result = {
"text": text,
"score": score,
"int_score": int(round(max(0, min(score, 5)))),
}
print(result)
# {'text': 'This is a test sentence.', 'score': 0.3350256383419037, 'int_score': 0}
Intended uses & limitations
Refer to the hf classifier's model card for more details
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 90085
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-09
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 1.0