### Model Summary

The checkpoint aligns with our pixel-linguist-all setting in the paper. The model is initialized from our monolingual model, and is trained on parallel data (205000 steps) <-> AllNLI (2600 steps), going back and forth for three rounds. This model is the last round checkpoint. We recommend using it with A100 GPU, aligning with training.

### Downstream Use

Semantic Textual Similarity, Information Retrieval, Reasoning Retrieval

### Out-of-Scope Use

The model might not be optimal for further fine-tuning to do other tasks (such as classification), as it's trained to do representation tasks with similarity matching.

### Training Data

Please refer to the paper for the exact process.

## Inference
Encoding with our PixelLinguist class is very straightforward, just like using a SentenceTransformer class.

```python
model_name = "AnonymousPage/checkpoint-all"
model = PixelLinguist(model_name)
texts = ["I love you","I like you"]
embeddings = model.encode(texts)
print(outputs[0] @ outputs[1].T)  # just use dot product because the embeddings are normalized automatically in the model class.
#tensor(0.9217)
```

To use the PixelLinguist class: First install the package following our Github Repo. Then define our PixelLinguist Class as follow.

```python
import torch
from PIL import Image
from pixel import (
    AutoConfig,
    PangoCairoTextRenderer,
    PIXELForSequenceClassification,
    PIXELForRepresentation,
    PoolingMode,
    get_attention_mask,
    get_transforms,
    glue_strip_spaces,
    resize_model_embeddings,
)
from tqdm import tqdm
class PixelLinguist:
    def __init__(self, model_name, batch_size = 16, max_seq_length = 64, 
                 device=None, pooling = "mean", keep_mlp = False):
        if device is not None:
            self.device = device
        else:
            self.device = "cuda:0" if torch.cuda.is_available() else "cpu"
        self.config = AutoConfig.from_pretrained(model_name, num_labels=0)
        self.batch_size = batch_size
        if keep_mlp == True:
            self.model = PIXELForSequenceClassification.from_pretrained(
                model_name,
                config=self.config,
                pooling_mode=PoolingMode.from_string(pooling),
                add_layer_norm=True
            ).to(self.device)
        else:
            self.model = PIXELForRepresentation.from_pretrained(
                model_name,
                config=self.config,
                pooling_mode=PoolingMode.from_string(pooling),
                add_layer_norm=True
            ).to(self.device)
        self.processor = PangoCairoTextRenderer.from_pretrained(model_name, rgb=False)
        self.processor.max_seq_length = max_seq_length
        resize_model_embeddings(self.model, self.processor.max_seq_length)
        self.transforms = get_transforms(do_resize=True, size=(self.processor.pixels_per_patch, self.processor.pixels_per_patch * self.processor.max_seq_length))
    def preprocess(self, texts):
        encodings = [self.processor(text=glue_strip_spaces(a)) for a in texts]
        pixel_values = torch.stack([self.transforms(Image.fromarray(e.pixel_values)) for e in encodings])
        attention_mask = torch.stack([get_attention_mask(e.num_text_patches, seq_length=self.processor.max_seq_length) for e in encodings])
        return {'pixel_values': pixel_values, 'attention_mask': attention_mask}
    def encode(self, texts, **kwargs):
        all_outputs = []
        for i in tqdm(range(0, len(texts), self.batch_size)):
            batch_texts = texts[i:i+batch_size]
            inputs = self.preprocess(batch_texts)
            inputs = {k: v.to(self.device) for k, v in inputs.items()}
            with torch.no_grad():
                outputs = self.model(**inputs).logits.detach().cpu()
            all_outputs.append(outputs)
        return torch.cat(all_outputs, dim=0)
```

### Evaluation

For STS evaluation (see Github repo):
```
python tools/evaluation_sts_all.py
```
For BEIR information retrieval evaluation (see Github repo):
```
python tools/evaluation_retrieval.py
```