Auto Upload

1_Pooling/config.json

@@ -0,0 +1,7 @@
+{
+  "word_embedding_dimension": 384,
+  "pooling_mode_cls_token": false,
+  "pooling_mode_mean_tokens": true,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false
+}

README.md

@@ -0,0 +1,82 @@
+---
+license: cc-by-nc-4.0
+pipeline_tag: sentence-similarity
+tags:
+    - sentence-transformers
+    - feature-extraction
+    - sentence-similarity
+    - transformers
+    - generated_from_trainer
+datasets:
+    - squad
+    - newsqa
+    - LLukas22/cqadupstack
+    - LLukas22/fiqa
+    - LLukas22/scidocs
+    - deepset/germanquad
+    - LLukas22/nq
+---
+
+# all-MiniLM-L12-v2-embedding-all
+
+This model is a fine-tuned version of [all-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2) on the following datasets: [squad](https://huggingface.co/datasets/squad), [newsqa](https://huggingface.co/datasets/newsqa), [LLukas22/cqadupstack](https://huggingface.co/datasets/LLukas22/cqadupstack), [LLukas22/fiqa](https://huggingface.co/datasets/LLukas22/fiqa), [LLukas22/scidocs](https://huggingface.co/datasets/LLukas22/scidocs), [deepset/germanquad](https://huggingface.co/datasets/deepset/germanquad), [LLukas22/nq](https://huggingface.co/datasets/LLukas22/nq).
+
+
+
+## Usage (Sentence-Transformers)
+
+Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
+
+```
+pip install -U sentence-transformers
+```
+
+Then you can use the model like this:
+
+```python
+from sentence_transformers import SentenceTransformer
+sentences = ["This is an example sentence", "Each sentence is converted"]
+
+model = SentenceTransformer('LLukas22/all-MiniLM-L12-v2-embedding-all')
+embeddings = model.encode(sentences)
+print(embeddings)
+```
+
+## Training hyperparameters
+The following hyperparameters were used during training:
+
+- learning_rate: 2E-05
+- per device batch size: 60
+- effective batch size: 120
+- seed: 42
+- optimizer: AdamW with betas (0.9,0.999) and eps 1E-08
+- weight decay: 1E-02
+- number of epochs: 4
+- mixed_precision_training: bf16
+
+## Training results
+| Epoch | Train Loss | Validation Loss |
+| ----- | ---------- | --------------- |
+| 0 | 0.0655 | 0.055 |
+| 1 | 0.0549 | 0.051 |
+| 2 | 0.049 | 0.0481 |
+| 3 | 0.0451 | 0.0471 |
+
+## Evaluation results
+| Epoch | top_1 | top_3 | top_5 | top_10 | top_25 |
+| ----- | ----- | ----- | ----- | ----- | ----- |
+| 0 | 0.537 | 0.697 | 0.753 | 0.812 | 0.867 |
+| 1 | 0.538 | 0.699 | 0.755 | 0.814 | 0.872 |
+| 2 | 0.544 | 0.705 | 0.761 | 0.818 | 0.876 |
+| 3 | 0.544 | 0.703 | 0.759 | 0.817 | 0.874 |
+
+## Framework versions
+- Transformers: 4.25.1
+- PyTorch: 1.13.0+cu116
+- PyTorch Lightning: 1.8.6
+- Datasets: 2.7.1
+- Tokenizers: 0.13.1
+- Sentence Transformers: 2.2.2
+
+## Additional Information
+This model was trained as part of my Master's Thesis **'Evaluation of transformer based language models for use in service information systems'**. The source code is available on [Github](https://github.com/LLukas22/Master).

config.json

@@ -0,0 +1,26 @@
+{
+  "_name_or_path": "sentence-transformers/all-MiniLM-L12-v2",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 384,
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "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",
+  "torch_dtype": "float32",
+  "transformers_version": "4.25.1",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

@@ -0,0 +1,7 @@
+{
+  "__version__": {
+    "sentence_transformers": "2.2.2",
+    "transformers": "4.25.1",
+    "pytorch": "1.13.0+cu116"
+  }
+}

lightning_logs/version_0/events.out.tfevents.1675760360.ki-jupyternotebook-8bdd

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bd739566f3ae781689313b4017f46e16bbe4bc2444849f041ffe6f142baf20c5
+size 80502

lightning_logs/version_0/hparams.yaml

@@ -0,0 +1 @@
+{}

modules.json

@@ -0,0 +1,14 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

pytorch_model.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1de12ff768dc3ddba42c82386efe6a53ac85658f890e5ff00ea64ac33462196c
+size 133508397

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 512,
+  "do_lower_case": false
+}

special_tokens_map.json

@@ -0,0 +1,7 @@
+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tokenizer_config.json

@@ -0,0 +1,16 @@
+{
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "name_or_path": "sentence-transformers/all-MiniLM-L12-v2",
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "special_tokens_map_file": "/home/jovyan/_huggingface-shared/hub/models--sentence-transformers--all-MiniLM-L12-v2/snapshots/9e16800aed25dbd1a96dfa6949c68c4d81d5dded/special_tokens_map.json",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}