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Here’s a summary of the model varun-v-rao/roberta-large-mnli-model2:\n\n- **Base Model:** roberta-large (by FacebookAI)\n- **Task:** Text classification, specifically for Natural Language Inference (NLI) using the Multi-Genre Natural Language Inference (MNLI) framework.\n- **Architecture:** RobertaForSequenceClassification\n- **Parameters:** ~355 million\n- **Precision:** 32-bit\n- **Performance:** Achieved 90.4% accuracy and 0.3560 loss on the evaluation set.\n- **Training:** Fine-tuned for 3 epochs with a learning rate of 2e-5, batch size of 64, using the Adam optimizer.\n- **Intended Use:** Classifying the relationship between pairs of sentences (entailment, contradiction, neutral).\n- **License:** MIT\n\n**Note:** The model is not designed for tasks outside of text classification/NLI. If you need capabilities like text generation or question answering, consider other models fine-tuned for those 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model varun-v-rao/t5-large-lora-4.72M-squad-model1 is a fine-tuned version of the T5-large transformer model, specifically adapted for question-answering tasks using the SQuAD dataset. It utilizes LoRA (Low-Rank Adaptation) techniques to efficiently fine-tune the large T5 model with approximately 4.72 million additional parameters. The model is designed to take a context passage and a question as input and generate an answer based on the context. It is suitable for extractive and generative question-answering tasks and requires about 3 GB of RAM and disk space to run. The model is released under the Apache 2.0 license.","downloads":12,"scoreTrending":null,"hfOwner":{"avatar":"https://huggingface.co/avatars/2e436734720843f0abab0159a17c39e6.svg"}},"architecture":[{"name":"T5ForQuestionAnswering"}],"__typename":"model","size":2.975749969482422},{"id":"eyJiYXNlTmFtZSI6ImdwdDItbG9yYS01OTJLLXNubGktbW9kZWwyIiwiZmlsZU5hbWUiOiJ2YXJ1bi12LXJhby9ncHQyLWxvcmEtNTkySy1zbmxpLW1vZGVsMiIsIm5hbWUiOiJ2YXJ1bi12LXJhby9ncHQyLWxvcmEtNTkySy1zbmxpLW1vZGVsMiIsIm9yZyI6InZhcnVuLXYtcmFvIiwic291cmNlIjoiYnl0ZXpfYmVuY2htYXJrZXIifQ==","org":"varun-v-rao","name":"varun-v-rao/gpt2-lora-592K-snli-model2","displayName":"gpt2-lora-592K-snli-model2","params":124442112,"created":null,"updated":"2025-04-17T17:27:15.344Z","description":null,"closedSource":false,"meterSize":"micro","containerized":null,"scoreSota":null,"scoreTrending":null,"scorePerformance":null,"minRequiredCpuRamGB":0.6870231628417969,"sizeName":"micro","planSizeAlias":"free","source":"bytez_benchmarker","task":"text-classification","costTagsJSON":null,"tasks":[{"id":"eyJuYW1lIjoidGV4dC1jbGFzc2lmaWNhdGlvbiIsInR5cGUiOiJodWdnaW5nZmFjZS10YXNrIn0=","name":"text-classification","description":null,"scoreTrending":null,"count":{"stars":0,"papers":0,"models":0}}],"repo":{"source":"huggingface","created":"2024-06-20T03:12:51.000Z","updated":"2024-06-20T03:57:49.000Z","likes":0,"description":null,"downloads":4,"scoreTrending":null,"hfOwner":{"avatar":"https://huggingface.co/avatars/2e436734720843f0abab0159a17c39e6.svg"}},"architecture":[{"name":"GPT2ForSequenceClassification"}],"__typename":"model","size":0.6870231628417969},{"id":"eyJiYXNlTmFtZSI6ImdwdDItbG9yYS01OTJLLXNubGktbW9kZWwzIiwiZmlsZU5hbWUiOiJ2YXJ1bi12LXJhby9ncHQyLWxvcmEtNTkySy1zbmxpLW1vZGVsMyIsIm5hbWUiOiJ2YXJ1bi12LXJhby9ncHQyLWxvcmEtNTkySy1zbmxpLW1vZGVsMyIsIm9yZyI6InZhcnVuLXYtcmFvIiwic291cmNlIjoiYnl0ZXpfYmVuY2htYXJrZXIifQ==","org":"varun-v-rao","name":"varun-v-rao/gpt2-lora-592K-snli-model3","displayName":"gpt2-lora-592K-snli-model3","params":124442112,"created":null,"updated":"2025-04-17T17:39:54.550Z","description":null,"closedSource":false,"meterSize":"micro","containerized":null,"scoreSota":null,"scoreTrending":null,"scorePerformance":null,"minRequiredCpuRamGB":0.6863517761230469,"sizeName":"micro","planSizeAlias":"free","source":"bytez_benchmarker","task":"text-classification","costTagsJSON":null,"tasks":[{"id":"eyJuYW1lIjoidGV4dC1jbGFzc2lmaWNhdGlvbiIsInR5cGUiOiJodWdnaW5nZmFjZS10YXNrIn0=","name":"text-classification","description":null,"scoreTrending":null,"count":{"stars":0,"papers":0,"models":0}}],"repo":{"source":"huggingface","created":"2024-06-20T03:57:51.000Z","updated":"2024-06-20T04:42:55.000Z","likes":0,"description":"Certainly! Here’s a summary of the model varun-v-rao/gpt2-lora-592K-snli-model3:\n\n- **Base Model:** openai-community/gpt2\n- **Task:** Text Classification (specifically, Natural Language Inference)\n- **Dataset:** Fine-tuned on the SNLI (Stanford Natural Language Inference) dataset\n- **Architecture:** GPT2ForSequenceClassification\n- **Parameters:** ~124M\n- **Performance:** \n - Accuracy: 0.7878 (on SNLI validation set)\n - Validation Loss: 0.5303\n- **Training Details:** \n - Learning Rate: 2e-5 \n - Batch Size: 256 (train), 128 (eval) \n - Optimizer: Adam \n - Epochs: 3\n\n**Intended Use:** \nThis model is designed for classifying the relationship between pairs of sentences (entailment, contradiction, neutral) as in the SNLI task.\n\n**Limitations:** \n- It is specialized for SNLI-style natural language inference and may not generalize to other text classification tasks without further fine-tuning.\n\n**License:** MIT\n\nLet me know if you need more technical details or usage 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model varun-v-rao/roberta-large-snli-model1 is a fine-tuned version of the roberta-large transformer model, specifically adapted for text classification tasks. It was trained on the SNLI (Stanford Natural Language Inference) dataset, which is commonly used for natural language inference (NLI) tasks—determining the relationship (entailment, contradiction, or neutral) between pairs of sentences.\n\nKey details:\n\n- Model type: RobertaForSequenceClassification (based on roberta-large)\n- Task: Text classification (NLI)\n- Training results: Achieved a validation accuracy of 93.25% and a validation loss of 0.2176\n- Training setup: Used Adam optimizer, learning rate of 2e-5, batch size of 256, and trained for 3 epochs\n- Hardware requirements: ~2 GB disk space and RAM, suitable for a g4dn.xlarge (micro) instance\n- License: MIT\n\nIntended use: This model is best suited for classifying the relationship between two sentences (entailment, contradiction, neutral) in English, as in NLI tasks.","downloads":10,"scoreTrending":null,"hfOwner":{"avatar":"https://huggingface.co/avatars/2e436734720843f0abab0159a17c39e6.svg"}},"architecture":[{"name":"RobertaForSequenceClassification"}],"__typename":"model","size":1.4792366027832031},{"id":"eyJiYXNlTmFtZSI6ImJlcnQtbGFyZ2UtY2FzZWQtbG9yYS0xLjU3TS1zcXVhZC1tb2RlbDIiLCJmaWxlTmFtZSI6InZhcnVuLXYtcmFvL2JlcnQtbGFyZ2UtY2FzZWQtbG9yYS0xLjU3TS1zcXVhZC1tb2RlbDIiLCJuYW1lIjoidmFydW4tdi1yYW8vYmVydC1sYXJnZS1jYXNlZC1sb3JhLTEuNTdNLXNxdWFkLW1vZGVsMiIsIm9yZyI6InZhcnVuLXYtcmFvIiwic291cmNlIjoiYnl0ZXpfYmVuY2htYXJrZXIifQ==","org":"varun-v-rao","name":"varun-v-rao/bert-large-cased-lora-1.57M-squad-model2","displayName":"bert-large-cased-lora-1.57M-squad-model2","params":332531714,"created":null,"updated":"2025-04-17T13:31:15.261Z","description":null,"closedSource":false,"meterSize":"micro","containerized":null,"scoreSota":null,"scoreTrending":null,"scorePerformance":null,"minRequiredCpuRamGB":1.4558677673339844,"sizeName":"micro","planSizeAlias":"free","source":"bytez_benchmarker","task":"question-answering","costTagsJSON":null,"tasks":[{"id":"eyJuYW1lIjoicXVlc3Rpb24tYW5zd2VyaW5nIiwidHlwZSI6Imh1Z2dpbmdmYWNlLXRhc2sifQ==","name":"question-answering","description":null,"scoreTrending":null,"count":{"stars":0,"papers":0,"models":0}}],"repo":{"source":"huggingface","created":"2024-02-08T11:35:14.000Z","updated":"2024-02-08T12:44:25.000Z","likes":0,"description":"The model varun-v-rao/bert-large-cased-lora-1.57M-squad-model2 is a fine-tuned version of the BERT large cased model, specifically adapted for question-answering tasks using the SQuAD dataset. 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If you need summarization, multi-turn dialogue, or generative QA, consider other models fine-tuned for those 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Here are the key details:\n\n- **Base Model:** openai-community/gpt2-large\n- **Task:** Question Answering (extractive, SQuAD-style)\n- **Fine-tuning Method:** LoRA (Low-Rank Adaptation) with approximately 2.95 million additional parameters\n- **Dataset:** SQuAD (Stanford Question Answering Dataset)\n- **Model Size:** 774 million parameters (GPT-2 Large base)\n- **Intended Use:** Answering questions based on a given context passage\n- **Hardware Requirements:** ~3 GB disk space, 4 GB RAM, suitable for g4dn.xlarge or similar GPU instances\n- **License:** MIT\n\nThis model is best suited for extractive question-answering tasks, where the answer is a span of text from a provided context. If you need summarization, classification, or other NLP tasks, you may want to consider a different or further fine-tuned 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