File size: 1,310 Bytes
e1a79ba 21a334b a16484e 21a334b a16484e 21a334b af333fa 21a334b af333fa 21a334b 44c069e 21a334b 3e8a16a 21a334b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 |
---
license: mit
language:
- en
- fr
- de
- it
- es
- pt
- pl
- nl
- ru
pipeline_tag: token-classification
inference: false
tags:
- mBERT
- BERT
- generic
- entity-recognition
---
## Model
The [multilingual BERT](https://huggingface.co/bert-base-multilingual-cased) finetunned on an artificially annotated multilingual subset of [Oscar dataset](https://huggingface.co/datasets/oscar-corpus/OSCAR-2201). This model provides domain & language independent embedding for Entity Recognition Task.
## Usage
Embeddings can be used out of the box or fine-tuned on specific datasets.
Get embeddings:
```python
import torch
import transformers
model = transformers.AutoModel.from_pretrained(
'numind/entity-recognition-general-sota-v1',
output_hidden_states=True,
)
tokenizer = transformers.AutoTokenizer.from_pretrained(
'numind/entity-recognition-general-sota-v1',
)
text = [
"NuMind is an AI company based in Paris and USA.",
"See other models from us on https://huggingface.co/numind"
]
encoded_input = tokenizer(
text,
return_tensors='pt',
padding=True,
truncation=True
)
output = model(**encoded_input)
# for better quality
emb = torch.cat(
(output.hidden_states[-1], output.hidden_states[-7]),
dim=2
)
# for better speed
# emb = output.hidden_states[-1]
``` |