File size: 975 Bytes
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datasets:
- ai4bharat/IndicCorpV2
language:
- hi
base_model:
- google-bert/bert-base-uncased
pipeline_tag: fill-mask
library_name: transformers
---
# BERT from Scratch (1 Epoch, Training Loss: 4.13)
BERT model trained from scratch using a custom tokenizer with a 64,000-token vocabulary.
- **Training:** 1 epoch
- **Masked Language Modeling (MLM) loss:** 4.13
- **Tokenizer:** Custom-trained, vocab size, on iit-madras Hindi-monolingual dataset = 64,000
- **Architecture:**
Maximum position embeddings: 512
Hidden size: 312
Number of attention heads: 12
Number of transformer layers: 4
Intermediate (feed-forward) size: 1200
Type vocabulary size: 2 (for segment embeddings)
It is uploaded for checkpointing, experimentation, and community feedback.
## Intended Use
- Research on training dynamics
- Continued pretraining
- Fine-tuning for downstream tasks (with caution)
## Limitations
- Low training coverage (1 epoch)
- Not yet evaluated on downstream tasks
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