Introduction of bigbird-roberta-base-edu-classifier
Model Details of bigbird-roberta-base-edu-classifier
bigbird-roberta-base: eduscore
Similar to the
original
, this model predicts a score of 0 to 5 on 'educational quality' of some text. This model was fine-tuned @ its max context length of 4096 tokens.
Usage
Note this is for CPU, for GPU you will need to make some (small) changes.
# Load model directlyfrom transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "pszemraj/bigbird-roberta-base-edu-classifier"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(
model_name, attn_implementation="eager"
)
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': 0.20170727372169495, 'int_score': 0}
Details
This model is a fine-tuned version of
google/bigbird-roberta-base
on the HuggingFaceFW/fineweb-edu-llama3-annotations dataset.
It achieves the following results on the evaluation set:
Loss: 0.2176
Mse: 0.2176
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: 4
eval_batch_size: 4
seed: 90085
gradient_accumulation_steps: 32
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
Runs of pszemraj bigbird-roberta-base-edu-classifier on huggingface.co
20
Total runs
0
24-hour runs
2
3-day runs
5
7-day runs
13
30-day runs
More Information About bigbird-roberta-base-edu-classifier huggingface.co Model
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