Comments (1)
Hello @behroozazarkhalili
As of v0.6.0, CausalBertModel
accepts a model_name
argument. For non-English languages, a multilingual model can be supplied:
import pandas as pd
df = pd.read_csv('sample_data/music_seed50.tsv', sep='\t', error_bad_lines=False)
from causalnlp.core.causalbert import CausalBertModel
cb = CausalBertModel(batch_size=16, max_length=128, model_name='distilbert-base-multilingual-cased')
cb.train(df['text'], df['C_true'], df['T_ac'], df['Y_sim'], epochs=1, learning_rate=2e-5)
print(cb.estimate_ate(df['C_true'], df['text']))
The current implementation should support any DistilBERT model from the Hugging Face model hub as CausalBert
is an instance of DistilBertPreTrainedModel
from transformers
.
We've only tested CausalNLP with English datasets using the default distilbert-base-uncased
model, though.
Hope this helps and thanks for your comments.
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