gokulsrinivasagan/processed_book_corpus-ld-5
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How to use gokulsrinivasagan/bert_tiny_olda_book_5_v1 with Transformers:
# Load model directly
from transformers import AutoTokenizer, DistilBertForLDAMaskedLM
tokenizer = AutoTokenizer.from_pretrained("gokulsrinivasagan/bert_tiny_olda_book_5_v1")
model = DistilBertForLDAMaskedLM.from_pretrained("gokulsrinivasagan/bert_tiny_olda_book_5_v1")This model is a fine-tuned version of on the gokulsrinivasagan/processed_book_corpus-ld-5 dataset. It achieves the following results on the evaluation set:
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The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.4427 | 0.7025 | 10000 | 1.4406 | 0.0001 |
| 1.4355 | 1.4051 | 20000 | 1.4316 | 0.0000 |
| 1.433 | 2.1076 | 30000 | 1.4302 | 0.0000 |
| 1.4306 | 2.8102 | 40000 | 1.4287 | 0.0000 |
| 1.431 | 3.5127 | 50000 | 1.4299 | 0.0000 |
| 1.4295 | 4.2153 | 60000 | 1.4261 | 0.0000 |
| 1.4266 | 4.9178 | 70000 | 1.4238 | 0.0000 |
| 1.4205 | 5.6203 | 80000 | 1.4168 | 0.0000 |
| 1.4191 | 6.3229 | 90000 | 1.4146 | 0.0001 |
| 1.419 | 7.0254 | 100000 | 1.4147 | 0.0001 |
| 1.4181 | 7.7280 | 110000 | 1.4134 | 0.0001 |
| 1.4163 | 8.4305 | 120000 | 1.4129 | 0.0001 |
| 1.4164 | 9.1331 | 130000 | 1.4111 | 0.0002 |
| 1.4147 | 9.8356 | 140000 | 1.4100 | 0.0002 |
| 1.414 | 10.5381 | 150000 | 1.4106 | 0.0002 |
| 1.4141 | 11.2407 | 160000 | 1.4098 | 0.0002 |
| 1.413 | 11.9432 | 170000 | 1.4093 | 0.0003 |
| 1.4124 | 12.6458 | 180000 | 1.4087 | 0.0003 |
| 1.4114 | 13.3483 | 190000 | 1.4078 | 0.0002 |
| 1.4115 | 14.0509 | 200000 | 1.4078 | 0.0002 |
| 1.4104 | 14.7534 | 210000 | 1.4080 | 0.0003 |
| 1.411 | 15.4560 | 220000 | 1.4075 | 0.0003 |
| 1.411 | 16.1585 | 230000 | 1.4069 | 0.0003 |
| 1.4104 | 16.8610 | 240000 | 1.4066 | 0.0003 |
| 1.4102 | 17.5636 | 250000 | 1.4068 | 0.0003 |
| 1.4094 | 18.2661 | 260000 | 1.4064 | 0.0003 |
| 1.4096 | 18.9687 | 270000 | 1.4059 | 0.0003 |
| 1.4098 | 19.6712 | 280000 | 1.4059 | 0.0003 |
| 1.409 | 20.3738 | 290000 | 1.4058 | 0.0002 |
| 1.4081 | 21.0763 | 300000 | 1.4057 | 0.0003 |
| 1.4082 | 21.7788 | 310000 | 1.4052 | 0.0003 |
| 1.4084 | 22.4814 | 320000 | 1.4054 | 0.0003 |
| 1.4088 | 23.1839 | 330000 | 1.4052 | 0.0002 |
| 1.4088 | 23.8865 | 340000 | 1.4050 | 0.0003 |
| 1.4084 | 24.5890 | 350000 | 1.4052 | 0.0003 |