Update README.md

README.md

@@ -11,44 +11,73 @@ model-index:
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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-# xlm-roberta-large-metaie
-
-This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on an unknown dataset.
-
-## Model description
-
-More information needed
-
-## Intended uses & limitations
-
-More information needed
-
-## Training and evaluation data
-
-More information needed
-
-## Training procedure
-
-### Training hyperparameters
-
-The following hyperparameters were used during training:
-- learning_rate: 1e-05
-- train_batch_size: 2
-- eval_batch_size: 8
-- seed: 42
-- gradient_accumulation_steps: 16
-- total_train_batch_size: 32
-- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
-- lr_scheduler_type: linear
-- num_epochs: 1.0
-
-### Training results
-
-
-
-### Framework versions
-
-- Transformers 4.35.2
-- Pytorch 2.0.0+cu117
-- Datasets 2.15.0
-- Tokenizers 0.15.0
+# 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']
+```