加载中…
加载中…
model.onnx 738 MBpytorch_model.bin 543 MBmodel.safetensors 543 MBMore information about our hmBERT model can be found in our new paper: "hmBERT: Historical Multilingual Language Models for Named Entity Recognition".
Our Historic Language Models Zoo contains support for the following languages - incl. their training data source:
| Language | Training data | Size |
|---|---|---|
| German | Europeana | 13-28GB (filtered) |
| French | Europeana | 11-31GB (filtered) |
| English | British Library | 24GB (year filtered) |
| Finnish | Europeana | 1.2GB |
| Swedish | Europeana | 1.1GB |
We provide some statistics using different thresholds of ocr confidences, in order to shrink down the corpus size and use less-noisier data:
| OCR confidence | Size |
|---|---|
| 0.60 | 28GB |
| 0.65 | 18GB |
| 0.70 | 13GB |
For the final corpus we use a OCR confidence of 0.6 (28GB). The following plot shows a tokens per year distribution:
Like German, we use different ocr confidence thresholds:
| OCR confidence | Size |
|---|---|
| 0.60 | 31GB |
| 0.65 | 27GB |
| 0.70 | 27GB |
| 0.75 | 23GB |
| 0.80 | 11GB |
For the final corpus we use a OCR confidence of 0.7 (27GB). The following plot shows a tokens per year distribution:
Metadata is taken from here. Stats incl. year filtering:
| Years | Size |
|---|---|
| ALL | 24GB |
| >= 1800 && < 1900 | 24GB |
We use the year filtered variant. The following plot shows a tokens per year distribution:
| OCR confidence | Size |
|---|---|
| 0.60 | 1.2GB |
The following plot shows a tokens per year distribution:
| OCR confidence | Size |
|---|---|
| 0.60 | 1.1GB |
The following plot shows a tokens per year distribution:
The following plot shows a tokens per year distribution of the complete training corpus:
For the first attempt, we use the first 10GB of each pretraining corpus. We upsample both Finnish and Swedish to ~10GB. The following tables shows the exact size that is used for generating a 32k and 64k subword vocabs:
| Language | Size |
|---|---|
| German | 10GB |
| French | 10GB |
| English | 10GB |
| Finnish | 9.5GB |
| Swedish | 9.7GB |
We then calculate the subword fertility rate and portion of [UNK]s over the following NER corpora:
| Language | NER corpora |
|---|---|
| German | CLEF-HIPE, NewsEye |
| French | CLEF-HIPE, NewsEye |
| English | CLEF-HIPE |
| Finnish | NewsEye |
| Swedish | NewsEye |
Breakdown of subword fertility rate and unknown portion per language for the 32k vocab:
| Language | Subword fertility | Unknown portion |
|---|---|---|
| German | 1.43 | 0.0004 |
| French | 1.25 | 0.0001 |
| English | 1.25 | 0.0 |
| Finnish | 1.69 | 0.0007 |
| Swedish | 1.43 | 0.0 |
Breakdown of subword fertility rate and unknown portion per language for the 64k vocab:
| Language | Subword fertility | Unknown portion |
|---|---|---|
| German | 1.31 | 0.0004 |
| French | 1.16 | 0.0001 |
| English | 1.17 | 0.0 |
| Finnish | 1.54 | 0.0007 |
| Swedish | 1.32 | 0.0 |
We upsample Swedish and Finnish to ~27GB. The final stats for all pretraining corpora can be seen here:
| Language | Size |
|---|---|
| German | 28GB |
| French | 27GB |
| English | 24GB |
| Finnish | 27GB |
| Swedish | 27GB |
Total size is 130GB.
Details about the pretraining are coming soon.
Research supported with Cloud TPUs from Google's TPU Research Cloud (TRC) program, previously known as TensorFlow Research Cloud (TFRC). Many thanks for providing access to the TRC ❤️
Thanks to the generous support from the Hugging Face team, it is possible to download both cased and uncased models from their S3 storage 🤗