Upload model, tokenizer, config, and README.md

README.md

@@ -0,0 +1,83 @@
+
+---
+library_name: transformers
+tags: [sentiment-analysis, BERT, social-media, emotion-detection]
+---
+
+# BERT Tweet Sentiment Analysis
+
+## Model Details
+
+### Model Description
+This model is a BERT-based transformer (`bert-base-uncased`) fine-tuned for sentiment analysis on tweets. It classifies text into six emotion categories: sadness, joy, love, anger, fear, and surprise. The model was trained with a custom classification head on tweet data to capture short-form, social media style text.
+
+- **Developed by:** Bibhu Mishra
+- **Model type:** Sequence Classification
+- **Language(s) (NLP):** English
+- **License:** MIT
+- **Finetuned from model:** bert-base-uncased
+
+### Model Sources
+- **Repository:** [Hugging Face Model Hub](https://huggingface.co/mishrabp/bert-base-uncased-tweet-sentiment-analysis)
+
+## Uses
+
+### Direct Use
+This model can be used to classify tweets or short text into emotions, useful for social listening, customer sentiment analysis, and marketing insights.
+
+### Downstream Use
+The model can be integrated into larger NLP pipelines, such as real-time social media monitoring dashboards, chatbots, or recommendation systems that require sentiment-aware behavior.
+
+### Out-of-Scope Use
+- Not suitable for clinical, medical, or diagnostic use.
+- Performance may decrease on very long text, sarcasm, or highly domain-specific language.
+
+## Bias, Risks, and Limitations
+- Limited to English-language social media text.
+- May misclassify sarcastic or context-heavy tweets.
+- Emotions are simplified into six categories, which may not capture nuanced sentiment.
+
+### Recommendations
+Use this model for **exploratory sentiment analytics** and not for critical decisions. Always review predictions in the context of business requirements.
+
+## How to Get Started with the Model
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
+
+model_name = "mishrabp/bert-base-uncased-tweet-sentiment-analysis"
+
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+classifier = pipeline("text-classification", model=model, tokenizer=tokenizer)
+
+tweets = [
+    "I love spending time with my friends!",
+    "I feel so sad about the news today."
+]
+
+results = classifier(tweets)
+print(results)
+```
+
+#### Output
+```json
+[
+    {"label": "love", "score": 0.95},
+    {"label": "sadness", "score": 0.88}
+]
+
+```
+
+### Labels
+```
+| ID | Emotion  |
+| -- | -------- |
+| 0  | sadness  |
+| 1  | joy      |
+| 2  | love     |
+| 3  | anger    |
+| 4  | fear     |
+| 5  | surprise |
+```

config.json

@@ -0,0 +1,42 @@
+{
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "dtype": "float32",
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "sadness",
+    "1": "joy",
+    "2": "love",
+    "3": "anger",
+    "4": "fear",
+    "5": "surprise"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "anger": 3,
+    "fear": 4,
+    "joy": 1,
+    "love": 2,
+    "sadness": 0,
+    "surprise": 5
+  },
+  "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",
+  "problem_type": "single_label_classification",
+  "transformers_version": "4.57.3",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5fb06992cf649b2ef8c4197eb830dbdb223c2d9ee737fdeecf94aa1f07683df9
+size 437970952

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 +1,56 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "[CLS]",
+  "do_lower_case": true,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}

training_args.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0f90f69ed75b1ed401297cb47c9551bd6ae4853a09053a7a72f3cd1dd4bb37af
+size 5777