
# training logs
- https://wandb.ai/junyu/huggingface/runs/1jg2jlgt

# install
- https://github.com/JunnYu/FLASHQuad_pytorch

# usage
```python
import torch
from flash import FLASHForMaskedLM
from transformers import BertTokenizerFast
tokenizer = BertTokenizerFast.from_pretrained("junnyu/flash_small_wwm_cluecorpussmall")
model = FLASHForMaskedLM.from_pretrained("junnyu/flash_small_wwm_cluecorpussmall")
model.eval()
text = "天气预报说今天的天[MASK]很好，那么我[MASK]一起去公园玩吧！"
inputs = tokenizer(text, return_tensors="pt", padding="max_length", max_length=512,  return_token_type_ids=False) #这里必须是512，不然结果可能不对。
with torch.no_grad():
    pt_outputs = model(**inputs).logits[0]

pt_outputs_sentence = "pytorch: "
for i, id in enumerate(tokenizer.encode(text)):
    if id == tokenizer.mask_token_id:
        val,idx = pt_outputs[i].softmax(-1).topk(k=5)
        tokens = tokenizer.convert_ids_to_tokens(idx)
        new_tokens = []
        for v,t in zip(val.cpu(),tokens):
            new_tokens.append(f"{t}+{round(v.item(),4)}")
        pt_outputs_sentence += "[" + "||".join(new_tokens) + "]"
    else:
        pt_outputs_sentence += "".join(
            tokenizer.convert_ids_to_tokens([id], skip_special_tokens=True))
print(pt_outputs_sentence)
# pytorch: 天气预报说今天的天[气+0.994||天+0.0015||空+0.0014||晴+0.0005||阳+0.0003]很好，那么我[们+0.9563||就+0.0381||也+0.0032||俩+0.0004||来+0.0002]一起去公园玩吧！
```
{"license": "mit", "inference": false}
transformers pytorch flash fill-mask license:mit autotrain_compatible region:us
TAGS
#transformers #pytorch #flash #fill-mask #license-mit #autotrain_compatible #region-us 


# training logs
- URL

# install
- URL

# usage

# training logs
- URL # install
- URL # usage
TAGS
#transformers #pytorch #flash #fill-mask #license-mit #autotrain_compatible #region-us 
 # training logs
- URL # install
- URL # usage
passage: TAGS
#transformers #pytorch #flash #fill-mask #license-mit #autotrain_compatible #region-us 
# training logs
- URL# install
- URL# usage