File size: 1,964 Bytes
5785133 d7b760d 1a4ceba d7b760d 5785133 d7b760d |
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 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 |
---
tags:
- mteb
model-index:
- name: alime-reranker-large-zh
results:
- task:
type: Reranking
dataset:
type: C-MTEB/CMedQAv1-reranking
name: MTEB CMedQAv1
config: default
split: test
revision: None
metrics:
- type: map
value: 82.32176162633382
- type: mrr
value: 84.91440476190478
- task:
type: Reranking
dataset:
type: C-MTEB/CMedQAv2-reranking
name: MTEB CMedQAv2
config: default
split: test
revision: None
metrics:
- type: map
value: 84.08586457179406
- type: mrr
value: 86.9011507936508
- task:
type: Reranking
dataset:
type: C-MTEB/Mmarco-reranking
name: MTEB MMarcoReranking
config: default
split: dev
revision: None
metrics:
- type: map
value: 35.497382125464284
- type: mrr
value: 35.29206349206349
- task:
type: Reranking
dataset:
type: C-MTEB/T2Reranking
name: MTEB T2Reranking
config: default
split: dev
revision: None
metrics:
- type: map
value: 68.25849742148222
- type: mrr
value: 78.64202157956387
---
# alime-reranker-large-zh
The alime reranker model.
# Usage
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
pairs = [["西湖在哪？", "西湖风景名胜区位于浙江省杭州市"],["今天天气不错","你吓死我了"]]
if torch.cuda.is_available():
device = torch.device("cuda")
else:
device = torch.device("cpu")
tokenizer = AutoTokenizer.from_pretrained("Pristinenlp/alime-reranker-large-zh")
model = AutoModelForSequenceClassification.from_pretrained("Pristinenlp/alime-reranker-large-zh").to(device)
inputs = tokenizer(pairs, padding=True, truncation=True, return_tensors='pt', max_length=512).to(device)
scores = model(**inputs, return_dict=True).logits.view(-1, ).float()
print(scores.tolist())
``` |