Delete training_ConMATH_ConMATH-2023-03-29_18-47-43

training_ConMATH_ConMATH-2023-03-29_18-47-43/0_Asym/140000857040976_MarkuplmTransformerForConMATH/added_tokens.json

@@ -1,4 +0,0 @@
-{
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-  "[empty-title]": 50265
-}

training_ConMATH_ConMATH-2023-03-29_18-47-43/0_Asym/140000857040976_MarkuplmTransformerForConMATH/config.json

@@ -1,34 +0,0 @@
-{
-  "_name_or_path": "output/training_markuplm_simcsemicrosoft-markuplm-base-2023-03-16_18-40-10",
-  "architectures": [
-    "MarkupLMModel"
-  ],
-  "attention_probs_dropout_prob": 0.1,
-  "bos_token_id": 0,
-  "classifier_dropout": null,
-  "eos_token_id": 2,
-  "gradient_checkpointing": false,
-  "hidden_act": "gelu",
-  "hidden_dropout_prob": 0.1,
-  "hidden_size": 768,
-  "initializer_range": 0.02,
-  "intermediate_size": 3072,
-  "layer_norm_eps": 1e-05,
-  "max_depth": 50,
-  "max_position_embeddings": 514,
-  "max_xpath_subs_unit_embeddings": 1024,
-  "max_xpath_tag_unit_embeddings": 256,
-  "model_type": "markuplm",
-  "num_attention_heads": 12,
-  "num_hidden_layers": 12,
-  "pad_token_id": 1,
-  "position_embedding_type": "absolute",
-  "subs_pad_id": 1001,
-  "tag_pad_id": 216,
-  "torch_dtype": "float32",
-  "transformers_version": "4.26.1",
-  "type_vocab_size": 1,
-  "use_cache": true,
-  "vocab_size": 50267,
-  "xpath_unit_hidden_size": 32
-}

training_ConMATH_ConMATH-2023-03-29_18-47-43/0_Asym/140000857040976_MarkuplmTransformerForConMATH/generation_config.json

@@ -1,7 +0,0 @@
-{
-  "_from_model_config": true,
-  "bos_token_id": 0,
-  "eos_token_id": 2,
-  "pad_token_id": 1,
-  "transformers_version": "4.26.1"
-}

training_ConMATH_ConMATH-2023-03-29_18-47-43/0_Asym/140000857040976_MarkuplmTransformerForConMATH/preprocessor_config.json

@@ -1,4 +0,0 @@
-{
-  "feature_extractor_type": "MarkupLMFeatureExtractor",
-  "processor_class": "MarkupLMProcessor"
-}

training_ConMATH_ConMATH-2023-03-29_18-47-43/0_Asym/140000857040976_MarkuplmTransformerForConMATH/pytorch_model.bin

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training_ConMATH_ConMATH-2023-03-29_18-47-43/0_Asym/140000857040976_MarkuplmTransformerForConMATH/sentence_bert_config.json

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

training_ConMATH_ConMATH-2023-03-29_18-47-43/0_Asym/140000857040976_MarkuplmTransformerForConMATH/special_tokens_map.json

@@ -1,15 +0,0 @@
-{
-  "bos_token": "<s>",
-  "cls_token": "<s>",
-  "eos_token": "</s>",
-  "mask_token": {
-    "content": "<mask>",
-    "lstrip": true,
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-  "unk_token": "<unk>"
-}

training_ConMATH_ConMATH-2023-03-29_18-47-43/0_Asym/140000857040976_MarkuplmTransformerForConMATH/tokenizer_config.json

@@ -1,268 +0,0 @@
-{
-  "add_prefix_space": false,
-  "bos_token": {
-    "__type": "AddedToken",
-    "content": "<s>",
-    "lstrip": false,
-    "normalized": true,
-    "rstrip": false,
-    "single_word": false
-  },
-  "cls_token": {
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-    "content": "<s>",
-    "lstrip": false,
-    "normalized": true,
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-    "lstrip": true,
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-  "max_depth": 50,
-  "max_width": 1000,
-  "model_max_length": 512,
-  "name_or_path": "output/training_markuplm_simcsemicrosoft-markuplm-base-2023-03-16_18-40-10",
-  "only_label_first_subword": true,
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training_ConMATH_ConMATH-2023-03-29_18-47-43/0_Asym/140000860578048_Transformer/config.json

@@ -1,24 +0,0 @@
-{
-  "_name_or_path": "sentence-transformers/all-mpnet-base-v2",
-  "architectures": [
-    "MPNetModel"
-  ],
-  "attention_probs_dropout_prob": 0.1,
-  "bos_token_id": 0,
-  "eos_token_id": 2,
-  "hidden_act": "gelu",
-  "hidden_dropout_prob": 0.1,
-  "hidden_size": 768,
-  "initializer_range": 0.02,
-  "intermediate_size": 3072,
-  "layer_norm_eps": 1e-05,
-  "max_position_embeddings": 514,
-  "model_type": "mpnet",
-  "num_attention_heads": 12,
-  "num_hidden_layers": 12,
-  "pad_token_id": 1,
-  "relative_attention_num_buckets": 32,
-  "torch_dtype": "float32",
-  "transformers_version": "4.26.1",
-  "vocab_size": 30527
-}

training_ConMATH_ConMATH-2023-03-29_18-47-43/0_Asym/140000860578048_Transformer/pytorch_model.bin

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training_ConMATH_ConMATH-2023-03-29_18-47-43/0_Asym/140000860578048_Transformer/sentence_bert_config.json

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

training_ConMATH_ConMATH-2023-03-29_18-47-43/0_Asym/140000860578048_Transformer/special_tokens_map.json

@@ -1,15 +0,0 @@
-{
-  "bos_token": "<s>",
-  "cls_token": "<s>",
-  "eos_token": "</s>",
-  "mask_token": {
-    "content": "<mask>",
-    "lstrip": true,
-    "normalized": false,
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-  "unk_token": "[UNK]"
-}

training_ConMATH_ConMATH-2023-03-29_18-47-43/0_Asym/140000860578048_Transformer/tokenizer_config.json

@@ -1,16 +0,0 @@
-{
-  "bos_token": "<s>",
-  "cls_token": "<s>",
-  "do_lower_case": true,
-  "eos_token": "</s>",
-  "mask_token": "<mask>",
-  "model_max_length": 512,
-  "name_or_path": "sentence-transformers/all-mpnet-base-v2",
-  "pad_token": "<pad>",
-  "sep_token": "</s>",
-  "special_tokens_map_file": null,
-  "strip_accents": null,
-  "tokenize_chinese_chars": true,
-  "tokenizer_class": "MPNetTokenizer",
-  "unk_token": "[UNK]"
-}

training_ConMATH_ConMATH-2023-03-29_18-47-43/0_Asym/config.json

@@ -1,17 +0,0 @@
-{
-  "types": {
-    "140000860578048_Transformer": "sentence_transformers.models.Transformer",
-    "140000857040976_MarkuplmTransformerForConMATH": "sentence_transformers.models.MarkuplmTransformerForConMATH"
-  },
-  "structure": {
-    "latex": [
-      "140000860578048_Transformer"
-    ],
-    "mathml": [
-      "140000857040976_MarkuplmTransformerForConMATH"
-    ]
-  },
-  "parameters": {
-    "allow_empty_key": true
-  }
-}

training_ConMATH_ConMATH-2023-03-29_18-47-43/1_Pooling/config.json

@@ -1,9 +0,0 @@
-{
-  "word_embedding_dimension": 768,
-  "pooling_mode_cls_token": false,
-  "pooling_mode_mean_tokens": true,
-  "pooling_mode_max_tokens": false,
-  "pooling_mode_mean_sqrt_len_tokens": false,
-  "pooling_mode_weightedmean_tokens": false,
-  "pooling_mode_lasttoken": false
-}

training_ConMATH_ConMATH-2023-03-29_18-47-43/README.md

@@ -1,93 +0,0 @@
----
-pipeline_tag: sentence-similarity
-tags:
-- sentence-transformers
-- feature-extraction
-- sentence-similarity
-
----
-
-# {MODEL_NAME}
-
-This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
-
-<!--- Describe your model here -->
-
-## 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('{MODEL_NAME}')
-embeddings = model.encode(sentences)
-print(embeddings)
-```
-
-
-
-## Evaluation Results
-
-<!--- Describe how your model was evaluated -->
-
-For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
-
-
-## Training
-The model was trained with the parameters:
-
-**DataLoader**:
-
-`torch.utils.data.dataloader.DataLoader` of length 45861 with parameters:
-```
-{'batch_size': 4, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
-```
-
-**Loss**:
-
-`sentence_transformers.losses.MultipleNegativesSymmetricRankingLoss.MultipleNegativesSymmetricRankingLoss` with parameters:
-  ```
-  {'scale': 20.0, 'similarity_fct': 'cos_sim'}
-  ```
-
-Parameters of the fit()-Method:
-```
-{
-    "epochs": 1,
-    "evaluation_steps": 4586,
-    "evaluator": "sentence_transformers.evaluation.AlignmentandUniformityEvaluator.AlignmentandUniformityEvaluator",
-    "max_grad_norm": 1,
-    "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
-    "optimizer_params": {
-        "lr": 5e-05
-    },
-    "scheduler": "WarmupLinear",
-    "steps_per_epoch": null,
-    "warmup_steps": 4587,
-    "weight_decay": 0.01
-}
-```
-
-
-## Full Model Architecture
-```
-SentenceTransformer(
-  (0): Asym(
-    (latex-0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel 
-    (mathml-0): MarkuplmTransformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MarkupLMModel 
-  )
-  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False})
-)
-```
-
-## Citing & Authors
-
-<!--- Describe where people can find more information -->

training_ConMATH_ConMATH-2023-03-29_18-47-43/config_sentence_transformers.json

@@ -1,7 +0,0 @@
-{
-  "__version__": {
-    "sentence_transformers": "2.2.2",
-    "transformers": "4.26.1",
-    "pytorch": "1.12.1+cu113"
-  }
-}

training_ConMATH_ConMATH-2023-03-29_18-47-43/modules.json

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