End of training

README.md

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+---
+base_model: jonas-luehrs/chembert_cased-MLM-chemistry
+tags:
+- generated_from_trainer
+metrics:
+- f1
+- precision
+- recall
+- accuracy
+model-index:
+- name: chembert_cased-MLM-chemistry-textCLS-PETROCHEMICAL
+  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. -->
+
+# chembert_cased-MLM-chemistry-textCLS-PETROCHEMICAL
+
+This model is a fine-tuned version of [jonas-luehrs/chembert_cased-MLM-chemistry](https://huggingface.co/jonas-luehrs/chembert_cased-MLM-chemistry) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.5883
+- F1: 0.7662
+- Precision: 0.7616
+- Recall: 0.7838
+- Accuracy: 0.7838
+
+## Model description
+
+More information needed
+
+## 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: 2e-05
+- train_batch_size: 16
+- eval_batch_size: 16
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 3
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | F1     | Precision | Recall | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:------:|:---------:|:------:|:--------:|
+| 1.1285        | 1.0   | 125  | 0.7318          | 0.7165 | 0.7123    | 0.7297 | 0.7297   |
+| 0.6049        | 2.0   | 250  | 0.6196          | 0.7524 | 0.7483    | 0.7703 | 0.7703   |
+| 0.4449        | 3.0   | 375  | 0.5883          | 0.7662 | 0.7616    | 0.7838 | 0.7838   |
+
+
+### Framework versions
+
+- Transformers 4.33.2
+- Pytorch 2.0.1+cu118
+- Datasets 2.14.5
+- Tokenizers 0.13.3

config.json

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+{
+  "_name_or_path": "jonas-luehrs/chembert_cased-MLM-chemistry",
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "Automobile",
+    "1": "Catalyst",
+    "2": "Construct",
+    "3": "HouseConst",
+    "4": "Household",
+    "5": "IndustConst",
+    "6": "Process"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "Automobile": 0,
+    "Catalyst": 1,
+    "Construct": 2,
+    "HouseConst": 3,
+    "Household": 4,
+    "IndustConst": 5,
+    "Process": 6
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "torch_dtype": "float32",
+  "transformers_version": "4.33.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 28996
+}

pytorch_model.bin

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special_tokens_map.json

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+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tokenizer_config.json

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+{
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": false,
+  "mask_token": "[MASK]",
+  "max_len": 512,
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}

training_args.bin

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