Model save

README.md

@@ -0,0 +1,207 @@
+---
+library_name: span-marker
+tags:
+- span-marker
+- token-classification
+- ner
+- named-entity-recognition
+- generated_from_span_marker_trainer
+datasets:
+- conll2003
+metrics:
+- precision
+- recall
+- f1
+widget:
+- text: New Zealand Prime Minister Jim Bolger, emerging from coalition talks with
+    the nationalist New Zealand First party on Friday afternoon, said National and
+    NZ First would meet again on Sunday.
+- text: A police spokesman said two youths believed to be supporters of President
+    Nelson Mandela's African National Congress (ANC) had been killed when unknown
+    gunmen opened fire at the rural settlement of Izingolweni on KwaZulu-Natal province's
+    south coast on Thursday night.
+- text: Japan's Economic Planning Agency has not changed its view that the economy
+    is gradually recovering, despite relatively weak gross domestic product figures
+    released on Tuesday, EPA Vice Minister Shimpei Nukaya told reporters on Friday.
+- text: Cuttitta, who trainer George Coste said was certain to play on Saturday week,
+    was named in a 21-man squad lacking only two of the team beaten 54-21 by England
+    at Twickenham last month.
+- text: Dong Jiong (China) beat Thomas Stuer-Lauridsen (Denmark) 15-10 15-6
+pipeline_tag: token-classification
+model-index:
+- name: SpanMarker
+  results:
+  - task:
+      type: token-classification
+      name: Named Entity Recognition
+    dataset:
+      name: Unknown
+      type: conll2003
+      split: test
+    metrics:
+    - type: f1
+      value: 0.9209646189051223
+      name: F1
+    - type: precision
+      value: 0.9156457822891144
+      name: Precision
+    - type: recall
+      value: 0.9263456090651558
+      name: Recall
+---
+
+# SpanMarker
+
+This is a [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) model trained on the [conll2003](https://huggingface.co/datasets/conll2003) dataset that can be used for Named Entity Recognition.
+
+## Model Details
+
+### Model Description
+- **Model Type:** SpanMarker
+<!-- - **Encoder:** [Unknown](https://huggingface.co/unknown) -->
+- **Maximum Sequence Length:** 256 tokens
+- **Maximum Entity Length:** 8 words
+- **Training Dataset:** [conll2003](https://huggingface.co/datasets/conll2003)
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Repository:** [SpanMarker on GitHub](https://github.com/tomaarsen/SpanMarkerNER)
+- **Thesis:** [SpanMarker For Named Entity Recognition](https://raw.githubusercontent.com/tomaarsen/SpanMarkerNER/main/thesis.pdf)
+
+### Model Labels
+| Label | Examples                                                      |
+|:------|:--------------------------------------------------------------|
+| LOC   | "BRUSSELS", "Britain", "Germany"                              |
+| MISC  | "British", "EU-wide", "German"                                |
+| ORG   | "European Union", "EU", "European Commission"                 |
+| PER   | "Nikolaus van der Pas", "Peter Blackburn", "Werner Zwingmann" |
+
+## Evaluation
+
+### Metrics
+| Label   | Precision | Recall | F1     |
+|:--------|:----------|:-------|:-------|
+| **all** | 0.9156    | 0.9263 | 0.9210 |
+| LOC     | 0.9327    | 0.9394 | 0.9361 |
+| MISC    | 0.7973    | 0.8462 | 0.8210 |
+| ORG     | 0.8987    | 0.9133 | 0.9059 |
+| PER     | 0.9706    | 0.9610 | 0.9658 |
+
+## Uses
+
+### Direct Use for Inference
+
+```python
+from span_marker import SpanMarkerModel
+
+# Download from the 🤗 Hub
+model = SpanMarkerModel.from_pretrained("supreethrao/instructNER_conll03_xl")
+# Run inference
+entities = model.predict("Dong Jiong (China) beat Thomas Stuer-Lauridsen (Denmark) 15-10 15-6")
+```
+
+### Downstream Use
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+```python
+from span_marker import SpanMarkerModel, Trainer
+
+# Download from the 🤗 Hub
+model = SpanMarkerModel.from_pretrained("supreethrao/instructNER_conll03_xl")
+
+# Specify a Dataset with "tokens" and "ner_tag" columns
+dataset = load_dataset("conll2003") # For example CoNLL2003
+
+# Initialize a Trainer using the pretrained model & dataset
+trainer = Trainer(
+    model=model,
+    train_dataset=dataset["train"],
+    eval_dataset=dataset["validation"],
+)
+trainer.train()
+trainer.save_model("supreethrao/instructNER_conll03_xl-finetuned")
+```
+</details>
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Set Metrics
+| Training set          | Min | Median  | Max |
+|:----------------------|:----|:--------|:----|
+| Sentence length       | 1   | 14.5019 | 113 |
+| Entities per sentence | 0   | 1.6736  | 20  |
+
+### Training Hyperparameters
+- learning_rate: 5e-05
+- train_batch_size: 16
+- eval_batch_size: 16
+- seed: 42
+- distributed_type: multi-GPU
+- num_devices: 2
+- total_train_batch_size: 32
+- total_eval_batch_size: 32
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 3
+- mixed_precision_training: Native AMP
+
+### Framework Versions
+- Python: 3.10.13
+- SpanMarker: 1.5.0
+- Transformers: 4.35.2
+- PyTorch: 2.1.1
+- Datasets: 2.15.0
+- Tokenizers: 0.15.0
+
+## Citation
+
+### BibTeX
+```
+@software{Aarsen_SpanMarker,
+    author = {Aarsen, Tom},
+    license = {Apache-2.0},
+    title = {{SpanMarker for Named Entity Recognition}},
+    url = {https://github.com/tomaarsen/SpanMarkerNER}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

all_results.json

@@ -0,0 +1,35 @@
+{
+    "epoch": 3.0,
+    "test_LOC": {
+        "f1": 0.9360812425328554,
+        "number": 1668,
+        "precision": 0.9327380952380953,
+        "recall": 0.9394484412470024
+    },
+    "test_MISC": {
+        "f1": 0.8210089841050449,
+        "number": 702,
+        "precision": 0.7973154362416107,
+        "recall": 0.8461538461538461
+    },
+    "test_ORG": {
+        "f1": 0.9059420722603764,
+        "number": 1661,
+        "precision": 0.898696682464455,
+        "recall": 0.9133052378085491
+    },
+    "test_PER": {
+        "f1": 0.9658172778123058,
+        "number": 1617,
+        "precision": 0.9706433479075578,
+        "recall": 0.961038961038961
+    },
+    "test_loss": 0.02015703357756138,
+    "test_overall_accuracy": 0.9824701195219123,
+    "test_overall_f1": 0.9209646189051223,
+    "test_overall_precision": 0.9156457822891144,
+    "test_overall_recall": 0.9263456090651558,
+    "test_runtime": 15.7711,
+    "test_samples_per_second": 236.572,
+    "test_steps_per_second": 7.419
+}

final_checkpoint/README.md

@@ -0,0 +1,207 @@
+---
+library_name: span-marker
+tags:
+- span-marker
+- token-classification
+- ner
+- named-entity-recognition
+- generated_from_span_marker_trainer
+datasets:
+- conll2003
+metrics:
+- precision
+- recall
+- f1
+widget:
+- text: New Zealand Prime Minister Jim Bolger, emerging from coalition talks with
+    the nationalist New Zealand First party on Friday afternoon, said National and
+    NZ First would meet again on Sunday.
+- text: A police spokesman said two youths believed to be supporters of President
+    Nelson Mandela's African National Congress (ANC) had been killed when unknown
+    gunmen opened fire at the rural settlement of Izingolweni on KwaZulu-Natal province's
+    south coast on Thursday night.
+- text: Japan's Economic Planning Agency has not changed its view that the economy
+    is gradually recovering, despite relatively weak gross domestic product figures
+    released on Tuesday, EPA Vice Minister Shimpei Nukaya told reporters on Friday.
+- text: Cuttitta, who trainer George Coste said was certain to play on Saturday week,
+    was named in a 21-man squad lacking only two of the team beaten 54-21 by England
+    at Twickenham last month.
+- text: Dong Jiong (China) beat Thomas Stuer-Lauridsen (Denmark) 15-10 15-6
+pipeline_tag: token-classification
+model-index:
+- name: SpanMarker
+  results:
+  - task:
+      type: token-classification
+      name: Named Entity Recognition
+    dataset:
+      name: Unknown
+      type: conll2003
+      split: test
+    metrics:
+    - type: f1
+      value: 0.9209646189051223
+      name: F1
+    - type: precision
+      value: 0.9156457822891144
+      name: Precision
+    - type: recall
+      value: 0.9263456090651558
+      name: Recall
+---
+
+# SpanMarker
+
+This is a [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) model trained on the [conll2003](https://huggingface.co/datasets/conll2003) dataset that can be used for Named Entity Recognition.
+
+## Model Details
+
+### Model Description
+- **Model Type:** SpanMarker
+<!-- - **Encoder:** [Unknown](https://huggingface.co/unknown) -->
+- **Maximum Sequence Length:** 256 tokens
+- **Maximum Entity Length:** 8 words
+- **Training Dataset:** [conll2003](https://huggingface.co/datasets/conll2003)
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Repository:** [SpanMarker on GitHub](https://github.com/tomaarsen/SpanMarkerNER)
+- **Thesis:** [SpanMarker For Named Entity Recognition](https://raw.githubusercontent.com/tomaarsen/SpanMarkerNER/main/thesis.pdf)
+
+### Model Labels
+| Label | Examples                                                      |
+|:------|:--------------------------------------------------------------|
+| LOC   | "BRUSSELS", "Britain", "Germany"                              |
+| MISC  | "British", "EU-wide", "German"                                |
+| ORG   | "European Union", "EU", "European Commission"                 |
+| PER   | "Nikolaus van der Pas", "Peter Blackburn", "Werner Zwingmann" |
+
+## Evaluation
+
+### Metrics
+| Label   | Precision | Recall | F1     |
+|:--------|:----------|:-------|:-------|
+| **all** | 0.9156    | 0.9263 | 0.9210 |
+| LOC     | 0.9327    | 0.9394 | 0.9361 |
+| MISC    | 0.7973    | 0.8462 | 0.8210 |
+| ORG     | 0.8987    | 0.9133 | 0.9059 |
+| PER     | 0.9706    | 0.9610 | 0.9658 |
+
+## Uses
+
+### Direct Use for Inference
+
+```python
+from span_marker import SpanMarkerModel
+
+# Download from the 🤗 Hub
+model = SpanMarkerModel.from_pretrained("supreethrao/instructNER_conll03_xl")
+# Run inference
+entities = model.predict("Dong Jiong (China) beat Thomas Stuer-Lauridsen (Denmark) 15-10 15-6")
+```
+
+### Downstream Use
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+```python
+from span_marker import SpanMarkerModel, Trainer
+
+# Download from the 🤗 Hub
+model = SpanMarkerModel.from_pretrained("supreethrao/instructNER_conll03_xl")
+
+# Specify a Dataset with "tokens" and "ner_tag" columns
+dataset = load_dataset("conll2003") # For example CoNLL2003
+
+# Initialize a Trainer using the pretrained model & dataset
+trainer = Trainer(
+    model=model,
+    train_dataset=dataset["train"],
+    eval_dataset=dataset["validation"],
+)
+trainer.train()
+trainer.save_model("supreethrao/instructNER_conll03_xl-finetuned")
+```
+</details>
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Set Metrics
+| Training set          | Min | Median  | Max |
+|:----------------------|:----|:--------|:----|
+| Sentence length       | 1   | 14.5019 | 113 |
+| Entities per sentence | 0   | 1.6736  | 20  |
+
+### Training Hyperparameters
+- learning_rate: 5e-05
+- train_batch_size: 16
+- eval_batch_size: 16
+- seed: 42
+- distributed_type: multi-GPU
+- num_devices: 2
+- total_train_batch_size: 32
+- total_eval_batch_size: 32
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 3
+- mixed_precision_training: Native AMP
+
+### Framework Versions
+- Python: 3.10.13
+- SpanMarker: 1.5.0
+- Transformers: 4.35.2
+- PyTorch: 2.1.1
+- Datasets: 2.15.0
+- Tokenizers: 0.15.0
+
+## Citation
+
+### BibTeX
+```
+@software{Aarsen_SpanMarker,
+    author = {Aarsen, Tom},
+    license = {Apache-2.0},
+    title = {{SpanMarker for Named Entity Recognition}},
+    url = {https://github.com/tomaarsen/SpanMarkerNER}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

final_checkpoint/added_tokens.json

@@ -0,0 +1,4 @@
+{
+  "<end>": 50266,
+  "<start>": 50265
+}

final_checkpoint/config.json

@@ -0,0 +1,137 @@
+{
+  "architectures": [
+    "SpanMarkerModel"
+  ],
+  "encoder": {
+    "_name_or_path": "numind/generic-entity_recognition_NER-v1",
+    "add_cross_attention": false,
+    "architectures": [
+      "RobertaModel"
+    ],
+    "attention_probs_dropout_prob": 0.1,
+    "bad_words_ids": null,
+    "begin_suppress_tokens": null,
+    "bos_token_id": 0,
+    "chunk_size_feed_forward": 0,
+    "classifier_dropout": null,
+    "cross_attention_hidden_size": null,
+    "decoder_start_token_id": null,
+    "diversity_penalty": 0.0,
+    "do_sample": false,
+    "early_stopping": false,
+    "encoder_no_repeat_ngram_size": 0,
+    "eos_token_id": 2,
+    "exponential_decay_length_penalty": null,
+    "finetuning_task": null,
+    "forced_bos_token_id": null,
+    "forced_eos_token_id": null,
+    "hidden_act": "gelu",
+    "hidden_dropout_prob": 0.1,
+    "hidden_size": 768,
+    "id2label": {
+      "0": "O",
+      "1": "B-PER",
+      "2": "I-PER",
+      "3": "B-ORG",
+      "4": "I-ORG",
+      "5": "B-LOC",
+      "6": "I-LOC",
+      "7": "B-MISC",
+      "8": "I-MISC"
+    },
+    "initializer_range": 0.02,
+    "intermediate_size": 3072,
+    "is_decoder": false,
+    "is_encoder_decoder": false,
+    "label2id": {
+      "B-LOC": 5,
+      "B-MISC": 7,
+      "B-ORG": 3,
+      "B-PER": 1,
+      "I-LOC": 6,
+      "I-MISC": 8,
+      "I-ORG": 4,
+      "I-PER": 2,
+      "O": 0
+    },
+    "layer_norm_eps": 1e-05,
+    "length_penalty": 1.0,
+    "max_length": 20,
+    "max_position_embeddings": 514,
+    "min_length": 0,
+    "model_type": "roberta",
+    "no_repeat_ngram_size": 0,
+    "num_attention_heads": 12,
+    "num_beam_groups": 1,
+    "num_beams": 1,
+    "num_hidden_layers": 12,
+    "num_return_sequences": 1,
+    "output_attentions": false,
+    "output_hidden_states": false,
+    "output_scores": false,
+    "pad_token_id": 1,
+    "position_embedding_type": "absolute",
+    "prefix": null,
+    "problem_type": null,
+    "pruned_heads": {},
+    "remove_invalid_values": false,
+    "repetition_penalty": 1.0,
+    "return_dict": true,
+    "return_dict_in_generate": false,
+    "sep_token_id": null,
+    "suppress_tokens": null,
+    "task_specific_params": null,
+    "temperature": 1.0,
+    "tf_legacy_loss": false,
+    "tie_encoder_decoder": false,
+    "tie_word_embeddings": true,
+    "tokenizer_class": null,
+    "top_k": 50,
+    "top_p": 1.0,
+    "torch_dtype": "float32",
+    "torchscript": false,
+    "transformers_version": "4.35.2",
+    "type_vocab_size": 1,
+    "typical_p": 1.0,
+    "use_bfloat16": false,
+    "use_cache": true,
+    "vocab_size": 50272
+  },
+  "entity_max_length": 8,
+  "id2label": {
+    "0": "O",
+    "1": "LOC",
+    "2": "MISC",
+    "3": "ORG",
+    "4": "PER"
+  },
+  "id2reduced_id": {
+    "0": 0,
+    "1": 4,
+    "2": 4,
+    "3": 3,
+    "4": 3,
+    "5": 1,
+    "6": 1,
+    "7": 2,
+    "8": 2
+  },
+  "label2id": {
+    "LOC": 1,
+    "MISC": 2,
+    "O": 0,
+    "ORG": 3,
+    "PER": 4
+  },
+  "marker_max_length": 128,
+  "max_next_context": null,
+  "max_prev_context": null,
+  "model_max_length": 256,
+  "model_max_length_default": 512,
+  "model_type": "span-marker",
+  "span_marker_version": "1.5.0",
+  "torch_dtype": "float32",
+  "trained_with_document_context": false,
+  "transformers_version": "4.35.2",
+  "vocab_size": 50272
+}

final_checkpoint/model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:49b36674a4a2212103babf7c89a480f28beda539fe2405b40e600e9d7ecdf78f
+size 498658900

final_checkpoint/special_tokens_map.json

@@ -0,0 +1,51 @@
+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}

final_checkpoint/tokenizer_config.json

@@ -0,0 +1,75 @@
+{
+  "add_prefix_space": true,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50264": {
+      "content": "<mask>",
+      "lstrip": true,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50265": {
+      "content": "<start>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50266": {
+      "content": "<end>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "<s>",
+  "entity_max_length": 8,
+  "eos_token": "</s>",
+  "errors": "replace",
+  "marker_max_length": 128,
+  "mask_token": "<mask>",
+  "model_max_length": 256,
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "tokenizer_class": "RobertaTokenizer",
+  "trim_offsets": true,
+  "unk_token": "<unk>"
+}

final_checkpoint/training_args.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6e59b483ca68e24f1af6db4be79ca4aa90ec0e5631888f05062697b0b3dc48b1
+size 4600

model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:1a6bf2c6823af8b1db1501954b964abc4ec61106645fc8f502a4d0146beac4b4
+oid sha256:49b36674a4a2212103babf7c89a480f28beda539fe2405b40e600e9d7ecdf78f
 size 498658900

runs/Nov27_09-44-18_trinity/events.out.tfevents.1701078284.trinity.345806.0

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:83554f98f9cd005ae8526cf2b23e07b76d93b4825777f30a8b5e9c08c2e91e55
-size 10054
+oid sha256:4dff3ed0449f13d553a02e4935997ce29311a5e8bcb78641b80d40fd2bebe9d0
+size 11664

runs/Nov27_09-44-18_trinity/events.out.tfevents.1701078794.trinity.345806.1

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5be057ef897b6aa91ef00edc804d80f62ee318a892ca1d7e71a0b189e485cb86
+size 592

test_results.json

@@ -0,0 +1,35 @@
+{
+    "epoch": 3.0,
+    "test_LOC": {
+        "f1": 0.9360812425328554,
+        "number": 1668,
+        "precision": 0.9327380952380953,
+        "recall": 0.9394484412470024
+    },
+    "test_MISC": {
+        "f1": 0.8210089841050449,
+        "number": 702,
+        "precision": 0.7973154362416107,
+        "recall": 0.8461538461538461
+    },
+    "test_ORG": {
+        "f1": 0.9059420722603764,
+        "number": 1661,
+        "precision": 0.898696682464455,
+        "recall": 0.9133052378085491
+    },
+    "test_PER": {
+        "f1": 0.9658172778123058,
+        "number": 1617,
+        "precision": 0.9706433479075578,
+        "recall": 0.961038961038961
+    },
+    "test_loss": 0.02015703357756138,
+    "test_overall_accuracy": 0.9824701195219123,
+    "test_overall_f1": 0.9209646189051223,
+    "test_overall_precision": 0.9156457822891144,
+    "test_overall_recall": 0.9263456090651558,
+    "test_runtime": 15.7711,
+    "test_samples_per_second": 236.572,
+    "test_steps_per_second": 7.419
+}