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# classification-poems

This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on the spanish Poems Dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8228
- Accuracy: 0.7241

## Model description

The model was trained to classify poems in Spanish, taking into account the content.



## Training and evaluation data

The original dataset has the columns author, content, title, year and type of poem. 

For each example, the type of poem it belongs to is identified. Then the model will recognize which type of poem the entered content belongs to. 

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.9344        | 1.0   | 258  | 0.7505          | 0.7586   |
| 0.9239        | 2.0   | 516  | 0.8228          | 0.7241   |


### Framework versions

- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "widget": [{"text": "El amor es una experiencia universal que nos conmueve a todos, pero a veces no hallamos las palabras adecuadas para expresarlo. A lo largo de la historia los poetas han sabido decir aquello que todos sentimos de formas creativas y elocuentes."}, {"text": "Hab\u00eda un hombre a quien la Pena nombraba su amigo, Y \u00e9l, so\u00f1ando con su gran camarada la Pena, Iba andando con paso lento por las arenas resplandecientes Y zumbantes, donde van oleajes ventosos: Y llam\u00f3 en voz alta a las estrellas para que se inclinaran Desde sus p\u00e1lidos tronos. y lo consuelan, pero entre ellos se r\u00eden y cantan siempre: Y entonces el hombre a quien la Tristeza nombr\u00f3 su amigo Grit\u00f3, \u00a1Mar oscuro, escucha mi m\u00e1s lastimosa historia! El mar avanzaba y segu\u00eda gritando su viejo grito, rodando en sue\u00f1os de colina en colina. Huy\u00f3 de la persecuci\u00f3n de su gloria Y, en un valle lejano y apacible deteni\u00e9ndose, Grit\u00f3 toda su historia a las gotas de roc\u00edo que brillan. Pero nada oyeron, porque siempre est\u00e1n escuchando, Las gotas de roc\u00edo, por el sonido de su propio goteo. Y entonces el hombre a quien Triste nombr\u00f3 su amigo Busc\u00f3 una vez m\u00e1s la orilla, y encontr\u00f3 una concha, Y pens\u00f3: Contar\u00e9 mi pesada historia Hasta que mis propias palabras, resonando, env\u00eden Su tristeza a trav\u00e9s de un coraz\u00f3n hueco y perlado; Y mi propia historia volver\u00e1 a cantar para m\u00ed, Y mis propias palabras susurrantes ser\u00e1n de consuelo, \u00a1Y he aqu\u00ed! mi antigua carga puede partir. Luego cant\u00f3 suavemente cerca del borde nacarado; Pero el triste habitante de los caminos mar\u00edtimos solitarios Cambi\u00f3 todo lo que cantaba en un gemido inarticulado Entre sus torbellinos salvajes, olvid\u00e1ndolo."}, {"text": "Ven, ven, muerte, Y en triste cipr\u00e9s d\u00e9jame descansar. Vuela lejos, vuela lejos, respira; Soy asesinado por una bella y cruel doncella. Mi sudario de blanco, pegado todo con tejo, \u00a1Oh, prep\u00e1ralo! Mi parte de la muerte, nadie tan fiel la comparti\u00f3. Ni una flor, ni una flor dulce, En mi ata\u00fad negro que se desparrame. Ni un amigo, ni un amigo saludan Mi pobre cad\u00e1ver, donde mis huesos ser\u00e1n arrojados. Mil mil suspiros para salvar, Acu\u00e9stame, oh, donde Triste amante verdadero nunca encuentre mi tumba, \u00a1Para llorar all\u00ed!"}], "base_model": "BSC-TeMU/roberta-base-bne", "model-index": [{"name": "classification-poems", "results": []}]}
transformers pytorch tensorboard roberta text-classification generated_from_trainer base_model:BSC-TeMU/roberta-base-bne license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #base_model-BSC-TeMU/roberta-base-bne #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

classification-poems
====================


This model is a fine-tuned version of BSC-TeMU/roberta-base-bne on the spanish Poems Dataset dataset.
It achieves the following results on the evaluation set:


* Loss: 0.8228
* Accuracy: 0.7241


Model description
-----------------


The model was trained to classify poems in Spanish, taking into account the content.


Training and evaluation data
----------------------------


The original dataset has the columns author, content, title, year and type of poem.


For each example, the type of poem it belongs to is identified. Then the model will recognize which type of poem the entered content belongs to.


Training procedure
------------------


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 2
* eval\_batch\_size: 2
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 2


### Training results



### Framework versions


* Transformers 4.17.0
* Pytorch 1.10.0+cu111
* Datasets 2.0.0
* Tokenizers 0.11.6



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 2
* eval\_batch\_size: 2
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 2 ### Training results ### Framework versions


* Transformers 4.17.0
* Pytorch 1.10.0+cu111
* Datasets 2.0.0
* Tokenizers 0.11.6
TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #base_model-BSC-TeMU/roberta-base-bne #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 2
* eval\_batch\_size: 2
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 2 ### Training results ### Framework versions


* Transformers 4.17.0
* Pytorch 1.10.0+cu111
* Datasets 2.0.0
* Tokenizers 0.11.6
passage: TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #base_model-BSC-TeMU/roberta-base-bne #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 2
* eval\_batch\_size: 2
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 2### Training results### Framework versions


* Transformers 4.17.0
* Pytorch 1.10.0+cu111
* Datasets 2.0.0
* Tokenizers 0.11.6