clasificador-tweets

This model is a fine-tuned version of mrm8488/electricidad-base-discriminator on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.9373
• Accuracy: 0.7234

Model description

This model has been trained to classify tweets into 7 labor-related categories:

• Low salary
• Labor rights
• Labor exploitation
• Workplace harassment
• Abuse of authority
• Workplace negligence
• Job opportunities

The model was trained using the dataset "somosnlp-hackathon-2022/es_tweets_laboral," which contains Spanish tweets classified into the 7 mentioned categories. The dataset has the following characteristics:

• Training set: 184 tweets.
• Test set: 47 tweets.

-Columns:

text: The tweet's text. intent: The tweet's category. entities: Additional information about the entities identified in the tweets.

The tokenizer from "mrm8488/electricidad-base-discriminator" was used for tokenization.

Intended uses & limitations

Classification of tweets related to labor topics. The model's accuracy is approximately ~72%. It is designed to classify tweets in Spanish. The dataset is small (184 tweets for training), which may limit the model's generalization.

Training and evaluation data

The model was trained for 10 epochs using accuracy as the evaluation metric. The results on the test set were as follows:

Loss: 0.937 Accuracy: 72.34%

It should be noted that these results may vary across different runs due to the randomness inherent in model training.

Training procedure

The training was based on the Transformers library by HuggingFace.

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 5e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	1.0	23	1.7598	0.3404
No log	2.0	46	1.5505	0.5106
No log	3.0	69	1.3208	0.6170
No log	4.0	92	1.1691	0.6383
No log	5.0	115	1.1357	0.6383
No log	6.0	138	0.9936	0.7447
No log	7.0	161	1.0371	0.6596
No log	8.0	184	0.9330	0.7021
No log	9.0	207	0.9195	0.7234
No log	10.0	230	0.9373	0.7234

Framework versions

• Transformers 4.47.0
• Pytorch 2.5.1+cu121
• Datasets 3.2.0
• Tokenizers 0.21.0