README.md

---
license: mit
base_model: xlm-roberta-large
tags:
- generated_from_trainer
model-index:
- name: xlm-roberta-large-metaie
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# MetaIE

This is a multilingual meta-model distilled from ChatGPT-3.5-turbo for information extraction. This is an intermediate checkpoint that can be well-transferred to all kinds of downstream information extraction tasks. This model can also be tested by different label-to-span matching as shown in the following example:

Ten languages are supported:
- English
- Français 
- Español
- Italiano
- Deutsch
- Polski
- Pусский
- 中文
- 日本語
- 한국어

```python
from transformers import AutoModelForTokenClassification, AutoTokenizer
import torch

device = torch.device("cuda:0")
path = f"KomeijiForce/xlm-roberta-large-metaie"
tokenizer = AutoTokenizer.from_pretrained(path)
tagger = AutoModelForTokenClassification.from_pretrained(path).to(device)

def find_sequences(lst):
    sequences = []
    i = 0
    while i < len(lst):
        if lst[i] == 0:
            start = i
            end = i
            i += 1
            while i < len(lst) and lst[i] == 1:
                end = i
                i += 1
            sequences.append((start, end+1))
        else:
            i += 1
    return sequences

examples = [
    "Fire volleys at the command happens: The soldiers were expected to fire volleys at the command of officers, but in practice this happened only in the first minutes of the battle .",
    "Historische Ereignisse: Siebenjährigen Krieg von 1756 bis 1763, war Preußen als fünfte Großmacht neben Frankreich, Großbritannien, Österreich und Russland in der europäischen Pentarchie anerkannt .",
    "高度: 东方明珠自落成后便为上海天际线的组成部分之一，总高468米。",
    "倒れた場所: カフカは高松の私立図書館に通うようになるが、ある日目覚めると、自分が森の中で血だらけで倒れていた。",
]

for example in examples:
    inputs = tokenizer(example, return_tensors="pt").to(device)
    tag_predictions = tagger(**inputs).logits[0].argmax(-1)

    predictions = [tokenizer.decode(inputs.input_ids[0, seq[0]:seq[1]]).strip() for seq in find_sequences(tag_predictions)]

    print(example)
    print(predictions)
```

The output will be

```python
Fire volleys at the command happens: The soldiers were expected to fire volleys at the command of officers, but in practice this happened only in the first minutes of the battle .
['first minutes of the battle']
Historische Ereignisse: Siebenjährigen Krieg von 1756 bis 1763, war Preußen als fünfte Großmacht neben Frankreich, Großbritannien, Österreich und Russland in der europäischen Pentarchie anerkannt .
['Siebenjährigen Krieg']
高度: 东方明珠自落成后便为上海天际线的组成部分之一，总高468米。
['468米']
倒れた場所: カフカは高松の私立図書館に通うようになるが、ある日目覚めると、自分が森の中で血だらけで倒れていた。
['森']
```