M2SA / README.md

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	---
	base_model: cardiffnlp/twitter-xlm-roberta-base-sentiment-multilingual
	tags:
	- generated_from_trainer
	datasets:
	- all
	metrics:
	- precision
	- recall
	- f1
	model-index:
	- name: twitter-xlmr-clip-finetuned-all-123
	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. -->

	# twitter-xlmr-clip-finetuned-all-123

	This model is a fine-tuned version of [cardiffnlp/twitter-xlm-roberta-base-sentiment-multilingual](https://huggingface.co/cardiffnlp/twitter-xlm-roberta-base-sentiment-multilingual) on the all dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.7405
	- Precision: 0.6431
	- Recall: 0.6554
	- F1: 0.6401

	## Model description

	More information needed

	## Usage
	To use the model use the following script.
	Kindly refer to the [app.py](https://huggingface.co/spaces/FFZG-cleopatra/M2SA-demo-multimodal/blob/main/app.py) for the Transform and VisionTextDualEncoderModel class definitions.

	```
	import torch
	import torch.nn as nn

	import torchvision
	from torchvision.transforms import CenterCrop, ConvertImageDtype, Normalize, Resize
	from torchvision.transforms.functional import InterpolationMode
	from torchvision import transforms
	from torchvision.io import ImageReadMode, read_image


	from transformers import CLIPModel, AutoModel
	from huggingface_hub import hf_hub_download
	from safetensors.torch import load_model

	from datasets import load_dataset, load_metric
	from transformers import (
	AutoConfig,
	AutoImageProcessor,
	AutoModelForSequenceClassification,
	AutoTokenizer,
	logging,
	)

	id2label = {0: "negative", 1: "neutral", 2: "positive"}
	label2id = {"negative": 0, "neutral": 1, "positive": 2}

	tokenizer = AutoTokenizer.from_pretrained("cardiffnlp/twitter-xlm-roberta-base-sentiment-multilingual")

	model = VisionTextDualEncoderModel(num_classes=3)
	config = model.vision_encoder.config

	# https://huggingface.co/FFZG-cleopatra/M2SA/blob/main/model.safetensors
	sf_filename = hf_hub_download("FFZG-cleopatra/M2SA", filename="model.safetensors")

	load_model(model, sf_filename)
	image_processor = AutoImageProcessor.from_pretrained("openai/clip-vit-base-patch32")

	def predict_sentiment(text, image):
	# read the image file
	image = read_image(image, mode=ImageReadMode.RGB)

	text_inputs = tokenizer(
	text,
	max_length=512,
	padding="max_length",
	truncation=True,
	return_tensors="pt"
	)

	image_transformations = Transform(
	config.vision_config.image_size,
	image_processor.image_mean,
	image_processor.image_std,
	)
	image_transformations = torch.jit.script(image_transformations)
	pixel_values = image_transformations(image)
	text_inputs["pixel_values"] = pixel_values.unsqueeze(0)

	prediction = None
	with torch.no_grad():
	outputs = model(**text_inputs)
	print(outputs)
	prediction = np.argmax(outputs["logits"], axis=-1)
	print(id2label[prediction[0].item()])
	return id2label[prediction[0].item()]

	predict_sentiment(text, image)
	```

	## 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: 5e-05
	- train_batch_size: 16
	- eval_batch_size: 16
	- seed: 123
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- num_epochs: 50.0
	- mixed_precision_training: Native AMP

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \|
	\|:-------------:\|:-----:\|:-----:\|:---------------:\|:---------:\|:------:\|:------:\|
	\| 0.6444 \| 0.06 \| 500 \| 0.8771 \| 0.6905 \| 0.4537 \| 0.4197 \|
	\| 0.5499 \| 0.12 \| 1000 \| 0.8167 \| 0.7197 \| 0.4260 \| 0.4117 \|
	\| 0.5357 \| 0.18 \| 1500 \| 0.8084 \| 0.7263 \| 0.4696 \| 0.4424 \|
	\| 0.5175 \| 0.24 \| 2000 \| 0.8704 \| 0.6666 \| 0.4266 \| 0.3717 \|
	\| 0.5285 \| 0.3 \| 2500 \| 0.9067 \| 0.7529 \| 0.4565 \| 0.4221 \|
	\| 0.5081 \| 0.36 \| 3000 \| 0.7414 \| 0.7655 \| 0.6114 \| 0.6356 \|
	\| 0.506 \| 0.42 \| 3500 \| 0.8713 \| 0.5830 \| 0.6591 \| 0.5786 \|
	\| 0.5049 \| 0.48 \| 4000 \| 0.7514 \| 0.5551 \| 0.4568 \| 0.4464 \|
	\| 0.4999 \| 0.54 \| 4500 \| 0.7584 \| 0.6519 \| 0.5502 \| 0.5767 \|
	\| 0.507 \| 0.6 \| 5000 \| 0.8072 \| 0.6479 \| 0.5626 \| 0.5636 \|
	\| 0.5048 \| 0.66 \| 5500 \| 0.8080 \| 0.6260 \| 0.5725 \| 0.5730 \|
	\| 0.4907 \| 0.72 \| 6000 \| 0.7966 \| 0.6976 \| 0.5138 \| 0.5224 \|
	\| 0.493 \| 0.78 \| 6500 \| 0.8193 \| 0.7099 \| 0.4949 \| 0.4922 \|
	\| 0.4668 \| 0.84 \| 7000 \| 0.7502 \| 0.6282 \| 0.6942 \| 0.6501 \|
	\| 0.4717 \| 0.9 \| 7500 \| 0.7636 \| 0.6372 \| 0.5109 \| 0.5191 \|
	\| 0.4774 \| 0.96 \| 8000 \| 0.7652 \| 0.7513 \| 0.5360 \| 0.5587 \|
	\| 0.4676 \| 1.02 \| 8500 \| 0.8482 \| 0.6372 \| 0.5918 \| 0.5836 \|
	\| 0.4361 \| 1.08 \| 9000 \| 0.7456 \| 0.6687 \| 0.5177 \| 0.5175 \|
	\| 0.4536 \| 1.14 \| 9500 \| 0.8449 \| 0.7363 \| 0.5160 \| 0.5156 \|
	\| 0.4277 \| 1.2 \| 10000 \| 0.8648 \| 0.6382 \| 0.5247 \| 0.5173 \|
	\| 0.4444 \| 1.26 \| 10500 \| 0.8723 \| 0.5871 \| 0.6622 \| 0.5959 \|
	\| 0.4269 \| 1.32 \| 11000 \| 0.7856 \| 0.6151 \| 0.5521 \| 0.5526 \|
	\| 0.4322 \| 1.38 \| 11500 \| 0.7405 \| 0.6431 \| 0.6554 \| 0.6401 \|
	\| 0.4435 \| 1.44 \| 12000 \| 0.7682 \| 0.6568 \| 0.5751 \| 0.5923 \|
	\| 0.4429 \| 1.5 \| 12500 \| 0.8824 \| 0.5956 \| 0.6006 \| 0.5545 \|
	\| 0.4381 \| 1.56 \| 13000 \| 0.7879 \| 0.4457 \| 0.4727 \| 0.4395 \|
	\| 0.4389 \| 1.62 \| 13500 \| 0.7555 \| 0.6260 \| 0.6984 \| 0.6502 \|
	\| 0.4529 \| 1.68 \| 14000 \| 0.7981 \| 0.6621 \| 0.5546 \| 0.5663 \|
	\| 0.4509 \| 1.74 \| 14500 \| 0.7827 \| 0.6160 \| 0.6321 \| 0.6172 \|
	\| 0.4413 \| 1.8 \| 15000 \| 0.7895 \| 0.6381 \| 0.6357 \| 0.6285 \|
	\| 0.4198 \| 1.86 \| 15500 \| 0.8345 \| 0.5940 \| 0.5526 \| 0.5602 \|
	\| 0.4415 \| 1.92 \| 16000 \| 0.8746 \| 0.6615 \| 0.6612 \| 0.6459 \|
	\| 0.443 \| 1.98 \| 16500 \| 0.8155 \| 0.6516 \| 0.5265 \| 0.5352 \|
	\| 0.4068 \| 2.04 \| 17000 \| 0.7642 \| 0.5838 \| 0.6220 \| 0.5975 \|
	\| 0.3905 \| 2.1 \| 17500 \| 0.7929 \| 0.6720 \| 0.5555 \| 0.5740 \|
	\| 0.3969 \| 2.16 \| 18000 \| 0.8949 \| 0.5330 \| 0.4771 \| 0.4687 \|
	\| 0.3841 \| 2.22 \| 18500 \| 0.9233 \| 0.6028 \| 0.5410 \| 0.5492 \|
	\| 0.4031 \| 2.28 \| 19000 \| 0.7720 \| 0.6089 \| 0.5719 \| 0.5776 \|
	\| 0.3878 \| 2.34 \| 19500 \| 0.9046 \| 0.6265 \| 0.5358 \| 0.5318 \|
	\| 0.4001 \| 2.41 \| 20000 \| 0.8451 \| 0.6960 \| 0.5622 \| 0.5761 \|
	\| 0.3997 \| 2.47 \| 20500 \| 0.8964 \| 0.6170 \| 0.5665 \| 0.5541 \|
	\| 0.3945 \| 2.53 \| 21000 \| 0.8001 \| 0.5553 \| 0.5180 \| 0.5195 \|
	\| 0.4005 \| 2.59 \| 21500 \| 0.8357 \| 0.5519 \| 0.5100 \| 0.5170 \|
	\| 0.3907 \| 2.65 \| 22000 \| 0.8017 \| 0.5884 \| 0.5409 \| 0.5552 \|
	\| 0.3858 \| 2.71 \| 22500 \| 0.8283 \| 0.6036 \| 0.5792 \| 0.5862 \|
	\| 0.3973 \| 2.77 \| 23000 \| 0.9024 \| 0.5770 \| 0.5665 \| 0.5393 \|
	\| 0.3969 \| 2.83 \| 23500 \| 0.8341 \| 0.5642 \| 0.5528 \| 0.5558 \|
	\| 0.3911 \| 2.89 \| 24000 \| 0.8966 \| 0.6045 \| 0.5088 \| 0.5070 \|
	\| 0.3856 \| 2.95 \| 24500 \| 0.8349 \| 0.6021 \| 0.5586 \| 0.5689 \|
	\| 0.3961 \| 3.01 \| 25000 \| 0.9364 \| 0.6119 \| 0.5412 \| 0.5585 \|
	\| 0.3301 \| 3.07 \| 25500 \| 0.9542 \| 0.5757 \| 0.6084 \| 0.5813 \|
	\| 0.3385 \| 3.13 \| 26000 \| 1.0137 \| 0.5563 \| 0.5294 \| 0.5346 \|
	\| 0.3475 \| 3.19 \| 26500 \| 0.9311 \| 0.6359 \| 0.5675 \| 0.5822 \|


	### Framework versions

	- Transformers 4.38.2
	- Pytorch 2.2.1+cu121
	- Datasets 2.18.0
	- Tokenizers 0.15.2