diff --git "a/train.log" "b/train.log"
--- "a/train.log"
+++ "b/train.log"
@@ -1,9 +1,9 @@
-2024-05-15 14:29:25.440457: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
-2024-05-15 14:29:25.440508: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
-2024-05-15 14:29:25.442473: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
-2024-05-15 14:29:26.567248: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
-05/15/2024 14:29:28 - WARNING - __main__ -   Process rank: 0, device: cuda:0, n_gpu: 1distributed training: True, 16-bits training: False
-05/15/2024 14:29:28 - INFO - __main__ -   Training/evaluation parameters TrainingArguments(
+2024-05-15 14:31:11.490571: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
+2024-05-15 14:31:11.490622: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
+2024-05-15 14:31:11.492868: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
+2024-05-15 14:31:12.620788: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
+05/15/2024 14:31:14 - WARNING - __main__ -   Process rank: 0, device: cuda:0, n_gpu: 1distributed training: True, 16-bits training: False
+05/15/2024 14:31:14 - INFO - __main__ -   Training/evaluation parameters TrainingArguments(
 _n_gpu=1,
 accelerator_config={'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'gradient_accumulation_kwargs': None},
 adafactor=False,
@@ -30,7 +30,7 @@ disable_tqdm=False,
 dispatch_batches=None,
 do_eval=True,
 do_predict=True,
-do_train=False,
+do_train=True,
 eval_accumulation_steps=None,
 eval_delay=0,
 eval_do_concat_batches=True,
@@ -130,17 +130,17 @@ weight_decay=0.0,
 You can avoid this message in future by passing the argument `trust_remote_code=True`.
 Passing `trust_remote_code=True` will be mandatory to load this dataset from the next major release of `datasets`.
   warnings.warn(
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 /usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
   warnings.warn(
-[INFO|configuration_utils.py:726] 2024-05-15 14:29:40,285 >> loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--PlanTL-GOB-ES--bsc-bio-ehr-es/snapshots/1e543adb2d21f19d85a89305eebdbd64ab656b99/config.json
-[INFO|configuration_utils.py:789] 2024-05-15 14:29:40,289 >> Model config RobertaConfig {
+[INFO|configuration_utils.py:726] 2024-05-15 14:31:25,038 >> loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--PlanTL-GOB-ES--bsc-bio-ehr-es/snapshots/1e543adb2d21f19d85a89305eebdbd64ab656b99/config.json
+[INFO|configuration_utils.py:789] 2024-05-15 14:31:25,042 >> Model config RobertaConfig {
   "_name_or_path": "PlanTL-GOB-ES/bsc-bio-ehr-es",
   "architectures": [
     "RobertaForMaskedLM"
@@ -179,8 +179,8 @@ Passing `trust_remote_code=True` will be mandatory to load this dataset from the
   "vocab_size": 50262
 }
 
-[INFO|configuration_utils.py:726] 2024-05-15 14:29:40,381 >> loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--PlanTL-GOB-ES--bsc-bio-ehr-es/snapshots/1e543adb2d21f19d85a89305eebdbd64ab656b99/config.json
-[INFO|configuration_utils.py:789] 2024-05-15 14:29:40,382 >> Model config RobertaConfig {
+[INFO|configuration_utils.py:726] 2024-05-15 14:31:25,248 >> loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--PlanTL-GOB-ES--bsc-bio-ehr-es/snapshots/1e543adb2d21f19d85a89305eebdbd64ab656b99/config.json
+[INFO|configuration_utils.py:789] 2024-05-15 14:31:25,249 >> Model config RobertaConfig {
   "_name_or_path": "PlanTL-GOB-ES/bsc-bio-ehr-es",
   "architectures": [
     "RobertaForMaskedLM"
@@ -208,14 +208,14 @@ Passing `trust_remote_code=True` will be mandatory to load this dataset from the
   "vocab_size": 50262
 }
 
-[INFO|tokenization_utils_base.py:2087] 2024-05-15 14:29:40,392 >> loading file vocab.json from cache at /root/.cache/huggingface/hub/models--PlanTL-GOB-ES--bsc-bio-ehr-es/snapshots/1e543adb2d21f19d85a89305eebdbd64ab656b99/vocab.json
-[INFO|tokenization_utils_base.py:2087] 2024-05-15 14:29:40,392 >> loading file merges.txt from cache at /root/.cache/huggingface/hub/models--PlanTL-GOB-ES--bsc-bio-ehr-es/snapshots/1e543adb2d21f19d85a89305eebdbd64ab656b99/merges.txt
-[INFO|tokenization_utils_base.py:2087] 2024-05-15 14:29:40,392 >> loading file tokenizer.json from cache at None
-[INFO|tokenization_utils_base.py:2087] 2024-05-15 14:29:40,392 >> loading file added_tokens.json from cache at None
-[INFO|tokenization_utils_base.py:2087] 2024-05-15 14:29:40,392 >> loading file special_tokens_map.json from cache at /root/.cache/huggingface/hub/models--PlanTL-GOB-ES--bsc-bio-ehr-es/snapshots/1e543adb2d21f19d85a89305eebdbd64ab656b99/special_tokens_map.json
-[INFO|tokenization_utils_base.py:2087] 2024-05-15 14:29:40,392 >> loading file tokenizer_config.json from cache at /root/.cache/huggingface/hub/models--PlanTL-GOB-ES--bsc-bio-ehr-es/snapshots/1e543adb2d21f19d85a89305eebdbd64ab656b99/tokenizer_config.json
-[INFO|configuration_utils.py:726] 2024-05-15 14:29:40,392 >> loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--PlanTL-GOB-ES--bsc-bio-ehr-es/snapshots/1e543adb2d21f19d85a89305eebdbd64ab656b99/config.json
-[INFO|configuration_utils.py:789] 2024-05-15 14:29:40,393 >> Model config RobertaConfig {
+[INFO|tokenization_utils_base.py:2087] 2024-05-15 14:31:25,259 >> loading file vocab.json from cache at /root/.cache/huggingface/hub/models--PlanTL-GOB-ES--bsc-bio-ehr-es/snapshots/1e543adb2d21f19d85a89305eebdbd64ab656b99/vocab.json
+[INFO|tokenization_utils_base.py:2087] 2024-05-15 14:31:25,259 >> loading file merges.txt from cache at /root/.cache/huggingface/hub/models--PlanTL-GOB-ES--bsc-bio-ehr-es/snapshots/1e543adb2d21f19d85a89305eebdbd64ab656b99/merges.txt
+[INFO|tokenization_utils_base.py:2087] 2024-05-15 14:31:25,259 >> loading file tokenizer.json from cache at None
+[INFO|tokenization_utils_base.py:2087] 2024-05-15 14:31:25,259 >> loading file added_tokens.json from cache at None
+[INFO|tokenization_utils_base.py:2087] 2024-05-15 14:31:25,259 >> loading file special_tokens_map.json from cache at /root/.cache/huggingface/hub/models--PlanTL-GOB-ES--bsc-bio-ehr-es/snapshots/1e543adb2d21f19d85a89305eebdbd64ab656b99/special_tokens_map.json
+[INFO|tokenization_utils_base.py:2087] 2024-05-15 14:31:25,259 >> loading file tokenizer_config.json from cache at /root/.cache/huggingface/hub/models--PlanTL-GOB-ES--bsc-bio-ehr-es/snapshots/1e543adb2d21f19d85a89305eebdbd64ab656b99/tokenizer_config.json
+[INFO|configuration_utils.py:726] 2024-05-15 14:31:25,259 >> loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--PlanTL-GOB-ES--bsc-bio-ehr-es/snapshots/1e543adb2d21f19d85a89305eebdbd64ab656b99/config.json
+[INFO|configuration_utils.py:789] 2024-05-15 14:31:25,260 >> Model config RobertaConfig {
   "_name_or_path": "PlanTL-GOB-ES/bsc-bio-ehr-es",
   "architectures": [
     "RobertaForMaskedLM"
@@ -243,8 +243,8 @@ Passing `trust_remote_code=True` will be mandatory to load this dataset from the
   "vocab_size": 50262
 }
 
-[INFO|configuration_utils.py:726] 2024-05-15 14:29:40,477 >> loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--PlanTL-GOB-ES--bsc-bio-ehr-es/snapshots/1e543adb2d21f19d85a89305eebdbd64ab656b99/config.json
-[INFO|configuration_utils.py:789] 2024-05-15 14:29:40,478 >> Model config RobertaConfig {
+[INFO|configuration_utils.py:726] 2024-05-15 14:31:25,341 >> loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--PlanTL-GOB-ES--bsc-bio-ehr-es/snapshots/1e543adb2d21f19d85a89305eebdbd64ab656b99/config.json
+[INFO|configuration_utils.py:789] 2024-05-15 14:31:25,341 >> Model config RobertaConfig {
   "_name_or_path": "PlanTL-GOB-ES/bsc-bio-ehr-es",
   "architectures": [
     "RobertaForMaskedLM"
@@ -272,72 +272,155 @@ Passing `trust_remote_code=True` will be mandatory to load this dataset from the
   "vocab_size": 50262
 }
 
-[INFO|modeling_utils.py:3429] 2024-05-15 14:29:40,721 >> loading weights file pytorch_model.bin from cache at /root/.cache/huggingface/hub/models--PlanTL-GOB-ES--bsc-bio-ehr-es/snapshots/1e543adb2d21f19d85a89305eebdbd64ab656b99/pytorch_model.bin
-[INFO|modeling_utils.py:4160] 2024-05-15 14:29:40,847 >> Some weights of the model checkpoint at PlanTL-GOB-ES/bsc-bio-ehr-es were not used when initializing RobertaForTokenClassification: ['lm_head.bias', 'lm_head.decoder.bias', 'lm_head.decoder.weight', 'lm_head.dense.bias', 'lm_head.dense.weight', 'lm_head.layer_norm.bias', 'lm_head.layer_norm.weight']
+[INFO|modeling_utils.py:3429] 2024-05-15 14:31:25,586 >> loading weights file pytorch_model.bin from cache at /root/.cache/huggingface/hub/models--PlanTL-GOB-ES--bsc-bio-ehr-es/snapshots/1e543adb2d21f19d85a89305eebdbd64ab656b99/pytorch_model.bin
+[INFO|modeling_utils.py:4160] 2024-05-15 14:31:25,711 >> Some weights of the model checkpoint at PlanTL-GOB-ES/bsc-bio-ehr-es were not used when initializing RobertaForTokenClassification: ['lm_head.bias', 'lm_head.decoder.bias', 'lm_head.decoder.weight', 'lm_head.dense.bias', 'lm_head.dense.weight', 'lm_head.layer_norm.bias', 'lm_head.layer_norm.weight']
 - This IS expected if you are initializing RobertaForTokenClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
 - This IS NOT expected if you are initializing RobertaForTokenClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
-[WARNING|modeling_utils.py:4172] 2024-05-15 14:29:40,847 >> Some weights of RobertaForTokenClassification were not initialized from the model checkpoint at PlanTL-GOB-ES/bsc-bio-ehr-es and are newly initialized: ['classifier.bias', 'classifier.weight']
+[WARNING|modeling_utils.py:4172] 2024-05-15 14:31:25,711 >> Some weights of RobertaForTokenClassification were not initialized from the model checkpoint at PlanTL-GOB-ES/bsc-bio-ehr-es and are newly initialized: ['classifier.bias', 'classifier.weight']
 You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
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 /content/dissertation/scripts/ner/run_ner.py:397: FutureWarning: load_metric is deprecated and will be removed in the next major version of datasets. Use 'evaluate.load' instead, from the new library 🤗 Evaluate: https://huggingface.co/docs/evaluate
   metric = load_metric("seqeval")
 /usr/local/lib/python3.10/dist-packages/datasets/load.py:759: FutureWarning: The repository for seqeval contains custom code which must be executed to correctly load the metric. You can inspect the repository content at https://raw.githubusercontent.com/huggingface/datasets/2.19.1/metrics/seqeval/seqeval.py
 You can avoid this message in future by passing the argument `trust_remote_code=True`.
 Passing `trust_remote_code=True` will be mandatory to load this metric from the next major release of `datasets`.
   warnings.warn(
-
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Downloading builder script: 6.33kB [00:00, 17.4MB/s]                   
-05/15/2024 14:29:44 - INFO - __main__ -   *** Evaluate ***
-[INFO|trainer.py:786] 2024-05-15 14:29:44,602 >> The following columns in the evaluation set don't have a corresponding argument in `RobertaForTokenClassification.forward` and have been ignored: id, ner_tags, tokens. If id, ner_tags, tokens are not expected by `RobertaForTokenClassification.forward`,  you can safely ignore this message.
-[INFO|trainer.py:3614] 2024-05-15 14:29:44,607 >> ***** Running Evaluation *****
-[INFO|trainer.py:3616] 2024-05-15 14:29:44,608 >>   Num examples = 6807
-[INFO|trainer.py:3619] 2024-05-15 14:29:44,608 >>   Batch size = 8
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-***** eval metrics *****
-  eval_accuracy           =     0.0288
-  eval_f1                 =     0.0056
-  eval_loss               =      1.293
-  eval_precision          =     0.0028
-  eval_recall             =     0.2693
-  eval_runtime            = 0:00:16.57
-  eval_samples            =       6807
-  eval_samples_per_second =    410.727
-  eval_steps_per_second   =     51.348
-05/15/2024 14:30:01 - INFO - __main__ -   *** Predict ***
-[INFO|trainer.py:786] 2024-05-15 14:30:01,182 >> The following columns in the test set don't have a corresponding argument in `RobertaForTokenClassification.forward` and have been ignored: id, ner_tags, tokens. If id, ner_tags, tokens are not expected by `RobertaForTokenClassification.forward`,  you can safely ignore this message.
-[INFO|trainer.py:3614] 2024-05-15 14:30:01,184 >> ***** Running Prediction *****
-[INFO|trainer.py:3616] 2024-05-15 14:30:01,184 >>   Num examples = 6807
-[INFO|trainer.py:3619] 2024-05-15 14:30:01,184 >>   Batch size = 8
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-[INFO|trainer.py:3305] 2024-05-15 14:30:17,255 >> Saving model checkpoint to /content/dissertation/scripts/ner/output
-[INFO|configuration_utils.py:471] 2024-05-15 14:30:17,256 >> Configuration saved in /content/dissertation/scripts/ner/output/config.json
-[INFO|modeling_utils.py:2590] 2024-05-15 14:30:18,218 >> Model weights saved in /content/dissertation/scripts/ner/output/model.safetensors
-[INFO|tokenization_utils_base.py:2488] 2024-05-15 14:30:18,219 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
-[INFO|tokenization_utils_base.py:2497] 2024-05-15 14:30:18,219 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json
-[INFO|modelcard.py:450] 2024-05-15 14:30:18,357 >> Dropping the following result as it does not have all the necessary fields:
-{'task': {'name': 'Token Classification', 'type': 'token-classification'}, 'dataset': {'name': 'Rodrigo1771/drugtemist-ner', 'type': 'Rodrigo1771/drugtemist-ner', 'config': 'DrugTEMIST NER', 'split': 'validation', 'args': 'DrugTEMIST NER'}}
-***** predict metrics *****
-  predict_accuracy           =     0.0288
-  predict_f1                 =     0.0056
-  predict_loss               =      1.293
-  predict_precision          =     0.0028
-  predict_recall             =     0.2693
-  predict_runtime            = 0:00:15.76
-  predict_samples_per_second =    431.659
-  predict_steps_per_second   =     53.965
-
model.safetensors:   0%|          | 0.00/496M [00:00<?, ?B/s]
-
events.out.tfevents.1715783401.61af03e56d14.4488.0:   0%|          | 0.00/486 [00:00<?, ?B/s]
+[INFO|trainer.py:786] 2024-05-15 14:31:30,107 >> The following columns in the training set don't have a corresponding argument in `RobertaForTokenClassification.forward` and have been ignored: ner_tags, tokens, id. If ner_tags, tokens, id are not expected by `RobertaForTokenClassification.forward`,  you can safely ignore this message.
+[INFO|trainer.py:2048] 2024-05-15 14:31:30,117 >> ***** Running training *****
+[INFO|trainer.py:2049] 2024-05-15 14:31:30,118 >>   Num examples = 27,224
+[INFO|trainer.py:2050] 2024-05-15 14:31:30,118 >>   Num Epochs = 10
+[INFO|trainer.py:2051] 2024-05-15 14:31:30,118 >>   Instantaneous batch size per device = 4
+[INFO|trainer.py:2054] 2024-05-15 14:31:30,118 >>   Total train batch size (w. parallel, distributed & accumulation) = 16
+[INFO|trainer.py:2055] 2024-05-15 14:31:30,118 >>   Gradient Accumulation steps = 4
+[INFO|trainer.py:2056] 2024-05-15 14:31:30,118 >>   Total optimization steps = 17,010
+[INFO|trainer.py:2057] 2024-05-15 14:31:30,118 >>   Number of trainable parameters = 124,055,043
+
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 10%|█         | 1701/17010 [05:15<47:38,  5.36it/s][INFO|trainer.py:786] 2024-05-15 14:36:45,308 >> The following columns in the evaluation set don't have a corresponding argument in `RobertaForTokenClassification.forward` and have been ignored: ner_tags, tokens, id. If ner_tags, tokens, id are not expected by `RobertaForTokenClassification.forward`,  you can safely ignore this message.
+[INFO|trainer.py:3614] 2024-05-15 14:36:45,310 >> ***** Running Evaluation *****
+[INFO|trainer.py:3616] 2024-05-15 14:36:45,311 >>   Num examples = 6807
+[INFO|trainer.py:3619] 2024-05-15 14:36:45,311 >>   Batch size = 8
+{'loss': 0.0278, 'grad_norm': 0.008281867019832134, 'learning_rate': 4.853027630805409e-05, 'epoch': 0.29}
+{'loss': 0.0082, 'grad_norm': 0.7454735040664673, 'learning_rate': 4.7060552616108174e-05, 'epoch': 0.59}
+{'loss': 0.0055, 'grad_norm': 0.028717944398522377, 'learning_rate': 4.559082892416226e-05, 'epoch': 0.88}
 
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                                                 [INFO|trainer.py:3305] 2024-05-15 14:37:01,007 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-1701
+[INFO|configuration_utils.py:471] 2024-05-15 14:37:01,008 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-1701/config.json
+[INFO|modeling_utils.py:2590] 2024-05-15 14:37:01,983 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-1701/model.safetensors
+[INFO|tokenization_utils_base.py:2488] 2024-05-15 14:37:01,984 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-1701/tokenizer_config.json
+[INFO|tokenization_utils_base.py:2497] 2024-05-15 14:37:01,984 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-1701/special_tokens_map.json
+[INFO|tokenization_utils_base.py:2488] 2024-05-15 14:37:06,395 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
+[INFO|tokenization_utils_base.py:2497] 2024-05-15 14:37:06,395 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json
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 10%|█         | 1767/17010 [05:48<48:56,  5.19it/s]
 10%|█         | 1768/17010 [05:48<48:08,  5.28it/s]
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 10%|█         | 1778/17010 [05:50<48:03,  5.28it/s]
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 10%|█         | 1782/17010 [05:51<47:53,  5.30it/s]
\ No newline at end of file