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Ukhushn/distilbert-base-uncased-finetuned-homedepot-SBERT
5b0ee44fada32d98a236bd2b07ed42f26a9d0a94
2022-05-20T20:07:14.000Z
[ "pytorch", "distilbert", "feature-extraction", "sentence-transformers", "sentence-similarity", "transformers" ]
sentence-similarity
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Ukhushn
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Ukhushn/distilbert-base-uncased-finetuned-homedepot-SBERT
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--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # Ukhushn/distilbert-base-uncased-finetuned-homedepot-SBERT 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('Ukhushn/distilbert-base-uncased-finetuned-homedepot-SBERT') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('Ukhushn/distilbert-base-uncased-finetuned-homedepot-SBERT') model = AutoModel.from_pretrained('Ukhushn/distilbert-base-uncased-finetuned-homedepot-SBERT') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_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=Ukhushn/distilbert-base-uncased-finetuned-homedepot-SBERT) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 6661 with parameters: ``` {'batch_size': 8, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "epochs": 4, "evaluation_steps": 1000, "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 2665, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: DistilBertModel (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}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
Dizzykong/gpt2-medium-recipes
71c9dd35a1bbaa269ab901365af5984c08439414
2022-05-21T02:55:18.000Z
[ "pytorch", "tensorboard", "gpt2", "text-generation", "transformers", "generated_from_trainer", "model-index" ]
text-generation
false
Dizzykong
null
Dizzykong/gpt2-medium-recipes
4
null
transformers
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--- tags: - generated_from_trainer model-index: - name: gpt2-medium-recipes 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. --> # gpt2-medium-recipes This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on an unknown dataset. ## 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: 5e-05 - train_batch_size: 1 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 0.04 ### Training results ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1
apthakur/distilbert-base-uncased-apala-finetuned-emotion
6d87f26aa37a11819c1c3b6d86401e30468e9a37
2022-05-21T01:38:16.000Z
[ "pytorch", "tensorboard", "distilbert", "text-classification", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
text-classification
false
apthakur
null
apthakur/distilbert-base-uncased-apala-finetuned-emotion
4
null
transformers
19,802
--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-apala-finetuned-emotion 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. --> # distilbert-base-uncased-apala-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.3696 - Accuracy: 0.476 - F1: 0.4250 ## 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: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 250 | 1.3899 | 0.476 | 0.4059 | | No log | 2.0 | 500 | 1.3696 | 0.476 | 0.4250 | ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1
connectivity/feather_berts_1
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2022-05-21T14:27:26.000Z
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2022-05-21T14:27:27.000Z
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2022-05-21T14:27:30.000Z
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2022-05-21T14:27:31.000Z
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2022-05-21T14:27:38.000Z
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connectivity/feather_berts_9
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connectivity/feather_berts_11
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2022-05-21T14:27:45.000Z
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2022-05-21T14:27:46.000Z
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connectivity/feather_berts_16
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connectivity/feather_berts_30
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2022-05-21T14:29:00.000Z
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