Add model card, licence, training config and per-seed evaluation

README.md

@@ -86,7 +86,7 @@ The fine-tuning and full-corpus inference code lives at
 | Pre-training | [`distilroberta-base`](https://huggingface.co/distilroberta-base) | 6-layer distilled RoBERTa, 82.1 M parameters |
 | Domain adaptation | [`mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis`](https://huggingface.co/mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis) | Financial-news sentiment |
 | Task adaptation | [`MAPAi/InflaBERT`](https://huggingface.co/MAPAi/InflaBERT) | Inflation-news sentiment (negative / neutral / positive) |
-| **This checkpoint** | **`andreadm/inflareddert`** | Full fine-tuning on Reddit titles, labels re-mapped to **down / neutral / up** |
+| **This checkpoint** | **`andreadm/infla-reddit-bert`** | Full fine-tuning on Reddit titles, labels re-mapped to **down / neutral / up** |
 
 Architecture: `RobertaForSequenceClassification`, 6 hidden layers, hidden
 size 768, 12 attention heads, 50 265-token byte-level BPE vocabulary, 512-token
@@ -106,7 +106,7 @@ classification head was re-initialised for the three directional classes.
 ```python
 from transformers import pipeline
 
-clf = pipeline("text-classification", model="andreadm/inflareddert")
+clf = pipeline("text-classification", model="andreadm/infla-reddit-bert")
 clf("Fed officials warn prices will keep climbing as CPI hits 40-year high")
 # [{'label': 'up', 'score': 0.999}]
 ```
@@ -117,7 +117,7 @@ Batched, with the paper's `-1 / 0 / +1` encoding:
 import torch
 from transformers import AutoModelForSequenceClassification, AutoTokenizer
 
-name = "andreadm/inflareddert"
+name = "andreadm/infla-reddit-bert"
 tokenizer = AutoTokenizer.from_pretrained(name)
 model = AutoModelForSequenceClassification.from_pretrained(name).eval()
 encoding = {"down": -1, "neutral": 0, "up": 1}