Text Classification
Transformers
Safetensors
English
distilbert
sentiment-analysis
Eval Results (legacy)
text-embeddings-inference
Instructions to use bmdavis/my-language-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bmdavis/my-language-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="bmdavis/my-language-model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("bmdavis/my-language-model") model = AutoModelForSequenceClassification.from_pretrained("bmdavis/my-language-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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language: en
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license: apache-2.0
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.88 # You can update this
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This model is a fine-tuned version of [`distilbert-base-uncased`](https://huggingface.co/distilbert-base-uncased) on the [IMDb movie review dataset](https://huggingface.co/datasets/imdb) for **binary sentiment classification** (positive/negative). It was trained using Hugging Face Transformers and PyTorch.
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## 🔍 Intended Use
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language: en
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license: apache-2.0
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tags:
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.88 # You can update this later
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# 🧠 Sentiment Analysis Model — DistilBERT Fine-Tuned on IMDb 🎬
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This model is a fine-tuned version of [`distilbert-base-uncased`](https://huggingface.co/distilbert-base-uncased) on the [IMDb movie review dataset](https://huggingface.co/datasets/imdb) for **binary sentiment classification** (positive/negative). It was trained using Hugging Face Transformers and PyTorch.
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## 🔍 Intended Use
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