DistilBERT multi-label HR classifier

README.md

@@ -1,26 +1,74 @@
 ---
+library_name: transformers
+license: apache-2.0
+base_model: distilbert/distilbert-base-uncased
 tags:
-- ml-intern
+- generated_from_trainer
+metrics:
+- precision
+- recall
+- accuracy
+model-index:
+- name: hr-conversations-classifier
+  results: []
 ---
 
-# AurelPx/hr-conversations-classifier
+<!-- 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. -->
 
-<!-- ml-intern-provenance -->
-## Generated by ML Intern
+# hr-conversations-classifier
 
-This model repository was generated by [ML Intern](https://github.com/huggingface/ml-intern), an agent for machine learning research and development on the Hugging Face Hub.
+This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.6631
+- F1 Micro: 0.1481
+- F1 Macro: 0.1452
+- Precision: 0.08
+- Recall: 1.0
+- Accuracy: 0.0
+- Hamming: 0.92
 
-- Try ML Intern: https://smolagents-ml-intern.hf.space
-- Source code: https://github.com/huggingface/ml-intern
+## Model description
 
-## Usage
+More information needed
 
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
+## Intended uses & limitations
 
-model_id = "AurelPx/hr-conversations-classifier"
-tokenizer = AutoTokenizer.from_pretrained(model_id)
-model = AutoModelForCausalLM.from_pretrained(model_id)
-```
+More information needed
 
-For non-causal architectures, replace `AutoModelForCausalLM` with the appropriate `AutoModel` class.
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 3e-05
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 16
+- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 5
+- num_epochs: 8
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | F1 Micro | F1 Macro | Precision | Recall | Accuracy | Hamming |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:---------:|:------:|:--------:|:-------:|
+| 1.3535        | 1.0   | 5    | 0.6631          | 0.1481   | 0.1452   | 0.08      | 1.0    | 0.0      | 0.92    |
+| 1.1928        | 2.0   | 10   | 0.5828          | 0.1481   | 0.1452   | 0.08      | 1.0    | 0.0      | 0.92    |
+| 1.0551        | 3.0   | 15   | 0.5213          | 0.1401   | 0.1322   | 0.0759    | 0.9062 | 0.0      | 0.89    |
+| 0.9999        | 4.0   | 20   | 0.4835          | 0.1418   | 0.1274   | 0.0771    | 0.875  | 0.0      | 0.8475  |
+
+
+### Framework versions
+
+- Transformers 5.8.0
+- Pytorch 2.11.0+cu130
+- Datasets 4.8.5
+- Tokenizers 0.22.2

config.json

@@ -0,0 +1,72 @@
+{
+  "activation": "gelu",
+  "architectures": [
+    "DistilBertForSequenceClassification"
+  ],
+  "attention_dropout": 0.1,
+  "bos_token_id": null,
+  "dim": 768,
+  "dropout": 0.1,
+  "dtype": "float32",
+  "eos_token_id": null,
+  "hidden_dim": 3072,
+  "id2label": {
+    "0": "LABEL_0",
+    "1": "LABEL_1",
+    "2": "LABEL_2",
+    "3": "LABEL_3",
+    "4": "LABEL_4",
+    "5": "LABEL_5",
+    "6": "LABEL_6",
+    "7": "LABEL_7",
+    "8": "LABEL_8",
+    "9": "LABEL_9",
+    "10": "LABEL_10",
+    "11": "LABEL_11",
+    "12": "LABEL_12",
+    "13": "LABEL_13",
+    "14": "LABEL_14",
+    "15": "LABEL_15",
+    "16": "LABEL_16",
+    "17": "LABEL_17",
+    "18": "LABEL_18",
+    "19": "LABEL_19"
+  },
+  "initializer_range": 0.02,
+  "label2id": {
+    "LABEL_0": 0,
+    "LABEL_1": 1,
+    "LABEL_10": 10,
+    "LABEL_11": 11,
+    "LABEL_12": 12,
+    "LABEL_13": 13,
+    "LABEL_14": 14,
+    "LABEL_15": 15,
+    "LABEL_16": 16,
+    "LABEL_17": 17,
+    "LABEL_18": 18,
+    "LABEL_19": 19,
+    "LABEL_2": 2,
+    "LABEL_3": 3,
+    "LABEL_4": 4,
+    "LABEL_5": 5,
+    "LABEL_6": 6,
+    "LABEL_7": 7,
+    "LABEL_8": 8,
+    "LABEL_9": 9
+  },
+  "max_position_embeddings": 512,
+  "model_type": "distilbert",
+  "n_heads": 12,
+  "n_layers": 6,
+  "pad_token_id": 0,
+  "problem_type": "multi_label_classification",
+  "qa_dropout": 0.1,
+  "seq_classif_dropout": 0.2,
+  "sinusoidal_pos_embds": false,
+  "tie_weights_": true,
+  "tie_word_embeddings": true,
+  "transformers_version": "5.8.0",
+  "use_cache": false,
+  "vocab_size": 30522
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:47236dfdfc9cfc4c87c96dfdefc072d83982c74151298784f3a5007d77c165bb
+size 267887936

tokenizer_config.json

@@ -0,0 +1,15 @@
+{
+  "backend": "tokenizers",
+  "cls_token": "[CLS]",
+  "do_lower_case": true,
+  "is_local": false,
+  "local_files_only": false,
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}

training_args.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f2a67b9b3a81433171067eee5ac5567f0effae5b3d842161da65bc3dda49ab2f
+size 5329